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Graphene aerogels decorated with metal (ru and ni) nanoparticles as high-performance catalysts for cox-free production of hydrogen from ammonia decomposition
Hydrogen is one of the most promising alternative energy sources to reduce the consumption of fossil fuels and their negative consequences. However, storage and transportation of hydrogen require harsh conditions, which can be overcome by storing hydrogen chemically in ammonia thanks to the advantages it offers, such as high hydrogen content, well-established infrastructure, and more importantly carbon-free elemental composition. Ruthenium is the most active metal for ammonia decomposition, which suffers from scarcity and high cost. Therefore, efficient use of this precious metal is crucial in design of the catalysts. Furthermore, nickel is the second most active metal for ammonia decomposition, and it is widely utilized owing to its broader availability and much lower cost than ruthenium. However, it requires a basic support with high surface area to reach high rates for ammonia decomposition. In this thesis study, to overcome these challenges, graphene aerogel (GA), a promising support material with high specific surface area, highly porous three-dimensional structure, high electrical conductivity, and abundant oxygen-containing functional groups, was utilized as a support to disperse both Ru and Ni nanoparticles to be able to reach high catalytic activity in COx-free hydrogen production from ammonia. Firstly, a family of Ru catalyst was prepared by dispersion of Ru on GA at high loadings to achieve a high performance in ammonia decomposition. Catalytic performance measurements on ammonia decomposition showed that the GA-supported catalyst with a Ru loading of 13.6 wt% provides an ammonia conversion of 71.5% at a space-velocity of 30,000 mL NH3 gcat-1 h-1 and at 450 °C, corresponding to a hydrogen production rate of 21.9 mmol H2 gcat-1 min-1. The addition of K increased the ammonia conversion to a record high value of 97.6% under identical conditions, reaching a hydrogen generation rate of 30.0 mmol H2 gcat-1 min-1, demonstrated to be stable for at least 80 h. A comparison of the turnover frequencies of catalysts indicated that this increase in performance upon the addition of K originated from an increase in the number of the active Ru sites and the corresponding electron density available for reaction. Secondly, three different Ni precursors with different pH of impregnation solutions were used to load Ni nanoparticles on GA as catalysts for ammonia decomposition to utilize the correct precursor based on the surface characteristics. Characterization and catalytic performance measurements on ammonia decomposition showed that the best dispersion and homogeneity, as well as catalytic performance were achieved with Ni (II) acetylacetonate (Ni(acac)2) precursor. An average Ni nanoparticle size of 13.6 ± 4.3 nm was obtained on the GA-supported Ni catalyst prepared by using Ni(acac)2 precursor and an impregnation solution with a pH of 10.2. This catalyst with a Ni loading of 11.1 wt% provided an ammonia conversion of 70.2% at a space velocity of 30,000 mL NH3 gcat-1 h-1 and 600 °C corresponding to a hydrogen production rate of 21.5 mmol H2 gcat-1 min-1. Data illustrated that the Ni precursor determines the pH of the impregnation solution, and the pH is one of the major parameters affecting the average Ni nanoparticle size and homogeneity of nanoparticles as well as the catalytic activity. Also, controlling the surface characteristics and interaction between support material and corresponding metal enhance the catalytic performance of ammonia decomposition. As a result, since impregnation solution prepared via Ni(acac)2 has the highest pH as ammonia decomposition is more favorable with basic catalysts, it provided better catalytic properties than impregnation solution prepared via Ni(NO3)2.6H2O.
High-throughput computational screening of covalent organic frameworks for gas separations
Capture of CO2 from flue gas and the purification of natural gas are environmentally and economically crucial. Covalent organic frameworks (COFs) are a newly emerging family of nanoporous materials in which light elements (B, C, N, O, H) are connected by strong covalent bonds. Due to their various structural properties, COFs gained a lot of attention for gas separation applications over the last decade. However, the large number of experimentally synthesized COFs makes it nearly impossible to measure the gas separation potentials of each COF material. Thus, we used a high-throughput computational approach in this thesis to assess the CO2/N2, CH4/H2, CH4/N2 and C2H6/CH4 separation performances of COFs. First, grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations were performed to investigate the adsorption- and membrane-based CO2/N2 separation performance of 295 COFs. Results elucidated that COFs could outperform traditional adsorbents such as zeolites and activated carbons for CO2/N2 separation. According to the structure-performance relations, COFs with pore sizes<10 Å, 0.6CH4>N2>H2, in accordance with molecular simulation results, and the degree of linker aromaticity could be used to identify the highly alkane selective COFs. These results show that COFs could outperform traditional adsorbent and membrane materials for gas separation processes. The findings of this thesis will pave the way for future computational and experimental studies to identify the most promising COF candidates and to design new materials with high gas separation potentials.
Modification of the structure of graphene aerogels by reduction: Consequences on catalytic properties, as carbocatalysts and as supports for atomically dispersed iridium
Scarcity of hydrocarbon sources is a widely recognized issue in today's world. Some precious transition metals like Ir, Ru. and Pd are used to process these scarce sources in the industry. However, scarcity and expensiveness of these transition metals urged researchers to investigate different alternatives. One of the alternatives is the usage of metal free catalysts. Also known as carbocatalysts produced by carbon derivative materials containing no metals could be the solution for the faced problems. One of the carbon-derivative materials, graphene aerogel could be a good candidate for better understanding of this class of catalysts because of its 3D form, very high surface area, high conductivity, and tuneable surface characteristics. In this study, graphene aerogels were synthesized by modified Hummers method and reduced under different conditions of gas and temperature. By this way, systematic change of graphene aerogel's oxygen functional groups ratio and creation of a higher surface density of defects were aimed. As a result of systematic thermal reduction process, some surface modifications were completed. These changes were investigated by different characterization methods including X-Ray Diffraction (XRD), Atomic Force Microscopy (AFM), Brunauer-Emmett-Teller (BET) surface area analysis, CHSN-O elemental analysis, X-Ray Photoelectron Spectroscopy (XPS) and Raman spectroscopy. By an increase in the reduction temperature, oxygen functional groups detached from the surfaces as some variable defects emerged on the samples. Based on the results of these samples catalytic performances for ethylene hydrogenation reaction, the contribution of the created defects to the catalytic activity was investigated. To explain this behavior deeper, a model was constructed based on CHSN-O elemental analysis and Raman spectroscopy results to quantify each defected carbon atom. By the relationship between the catalytic activity and defected carbon atom amounts, each defect types' contribution to the catalytic performance was quantified. To check the validity of the constructed model, the structural parameters of anadditional sample prepared by reduction at 700 °C was quantified and evaluated based on the corresponding catalytic activity for ethylene hydrogenation. The rate measured experimentally and estimated by the mathematical model differed by only ±2.5%, which confirmed the validity of the constructed model. Based on the model, amorphized carbon groups and polyene like structures contributed to the activity, as polyene like structures contributed 10 times more compared to amorphized carbon groups.The atomically dispersed supported metal catalysts are developing rapidly. However, issues regarding reaching a high metal loading and limited tunability of the catalytic properties constitute the major challenges with these types of catalysts. TO be able to contribute to meet these challenges, reduced graphene aerogels were used as support materials to synthesize single Ir complexes with approximately 10 wt% metal loading. By the change in reduction parameters of graphene aerogel, controlling of electronic environment of the metal complexes was aimed. For the quantification of metal loading and electron donation amount, TGA and FTIR characterization methods were applied. Based on the TG analysis, it was proven that all the samples were approximately 10 wt% metal loaded. By the FTIR results, it was unveiled that the most electron donation to the complexes were achieved by the usage of NrGA-300 and NrGA-500 samples. As the thermal reduction temperature increases, the level of electron donation from the surface to the metal decreased, also NH3 treatment boosted the electron donation to the metal complexes. The catalysts were tested for partial hydrogenation of 1,3-butadiene and acetylene hydrogenation reactions. Based on performance results, electronically richer metal complexes provided better partial hydrogenation selectivity values for both reactions. Even 100% partial hydrogenation selectivity value was obtained for 1,3-butadiene hydrogenation. These outcomes of this study indicate by usage of graphene aerogel, it is possible to obtain very high metal loaded single-isolated Ir complexes. Moreover, by the change in the reduction parameters, electronic environment of the metal sites were controlled, resulted to obtain tunable partial hydrogenation selectivity for acetylene and 1,3-butadiene. By this way, a better understanding on single-atom catalysts were obtained and the outcome transferred to the literature.
Preparation of Pt/Al2O3 and PtPd/Al2O3 diesel oxidation catalysts by supercritical deposition
Diesel oxidation catalysts (DOCs) are aftertreatment system parts responsible for the oxidation of CO, unburnt hydrocarbons and NO gases coming from heavy-duty diesel engines. In this study, DOCs were prepared, characterized, tested and compared with their commercial counterparts. In the first part of this study, four commercial monolithic DOCs with two different platinum group metal (PGM) loadings and Pt:Pd ratios of 1:0, 2:1 or 3:1 (w/w) were investigated systematically for NO, CO, and C3H6 oxidation, CO – C3H6 co-oxidation, and CO – C3H6 – NO oxidation reactions via transient activity measurements in a laboratory scale simulated diesel engine exhaust environment. As PGM loading increased, light-off curves shifted to lower temperatures for individual and co-oxidation reactions of CO and C3H6. CO and C3H6 were observed to inhibit the oxidation of themselves and each other. Addition of Pd to Pt was found to enhance CO and C3H6 oxidation performance of the catalysts while the presence and amount of Pd was found to increase the extent of self-inhibition of NO oxidation. NO inhibited CO and C3H6 oxidation reactions while NO oxidation performance was enhanced in the presence of CO and C3H6 due to the probable occurrence of reduced Pt and Pd sites during CO and C3H6 oxidations. The optimum Pt:Pd ratio for individual and co-oxidations of CO, C3H6 and NO was found to be Pt:Pd = 3:1 (w/w) in the range of experimental conditions investigated in this study. Pt/Al2O3 and bimetallic PtPd/Al2O3 catalysts were prepared via supercritical deposition (SCD) method using supercritical carbon dioxide. The effects of Pt loading of Pt/Al2O3 and Pd addition to Pt/Al2O3 on particle size, particle size distribution (PSD) and activity for NO and C3H6 oxidation and C3H6-selective catalytic reduction (C3H6-SCR) were investigated. Pt/Al2O3 catalysts were prepared with Pt loadings of 0.6, 1.2 and 2.1 wt% and a bimetallic PtPd/Al2O3 catalyst was prepared with total metal loading of 1.4 wt% and Pt:Pd molar ratio of 1.3:1. A small fraction of the particles agglomerated after calcination at 550 oC. Around 98% of the particles had an average particle size of 1 nm. The rest of the particles were larger and average size of these larger particles was 10 nm for monometallic catalysts and 6.5 nm for PtPd/Al2O3. All catalysts were found to be active for NO and C3H6 oxidation and C3H6-SCR reactions. NO oxidation performance of 1.2 wt% Pt/Al2O3 catalyst was the highest. C3H6 oxidation activity increased with increasing metal content. Light-off temperature for C3H6 oxidation shifted to higher temperature in the presence of NO, suggesting competitive oxidation of C3H6 and NO. Concentration profiles indicated that C3H6-SCR started when C3H6 conversion by oxidation reached 50%; C3H6 was consumed both by oxidation and C3H6-SCR at higher conversions. Morphologies of used and aged catalysts were investigated. HAADF-STEM imaging revealed that bimodality of the nanoparticles, sharp PSD and average nanoparticle sizes were maintained after usage in NO and C3H6 oxidation reactions. After thermal ageing at 800 oC, 1.2 wt% Pt/Al2O3 preserved the bimodality of the nanoparticles with slightly higher average nanoparticle size. 2.1 wt% Pt/Al2O3 prepared by SCD was compared with commercial monolithic Pt/Al2O3 DOC for NO oxidation performance. The DOC prepared by SCD performed better at temperatures lower than 325 oC. While maximum conversions achieved by both catalysts were more than 50%, commercial DOC achieved slightly higher values at a higher temperature. C3H6 oxidation kinetics of 2.1 wt% Pt/Al2O3 and bimetallic PtPd/Al2O3 prepared by SCD were investigated in a tubular flow reactor at differential conditions and in exhaust environment of diesel engines. Langmuir-Hinshelwood reaction mechanism with dissociative adsorption of O2 was assumed. C3H6-TPD results suggested dissociative adsorption of C3H6 as well. MATLAB was used to determine rate constants, adsorption equilibrium constants and exponents in the rate expressions by nonlinear regression. Pre-exponential factors and apparent activation energies were calculated using Arrhenius Law. For both catalysts, increase in C3H6 feed concentration decreased C3H6 oxidation reaction rate at isothermal conditions. The reaction rate increased with increasing O2 concentration for 2.1 wt% Pt/Al2O3 and it was zeroth order in terms of O2 for PtPd/Al2O3. Apparent activation energies of the oxidation reaction were calculated as 84.3 kJ mol-1 and 17.9 kJ mol-1 respectively for 2.1 wt% Pt/Al2O3 and PtPd/Al2O3. Average error between experimental and theoretical rates were calculated as 18.3% and 13.6% respectively. To our knowledge, this is the first study in which bimetallic PtPd/Al2O3 catalyst was prepared by SCD. This is also the first study on the kinetics of C3H6 oxidation over a DOC prepared by SCD and one of the very few studies where the presence of H2O in the feed mixture was taken into account during the investigation of C3H6 oxidation reaction kinetics.
Investigation of fluidization regimes and coating process for alginate aerogel particles in a wurster fluidized bed
Aerogels are solid materials with high porosities, open and inter-connected pore structures, and high surface areas. Over the last decade, there are an increasing number of research efforts concerning in coatings of aerogels since the combination of the porous structure of aerogels with the morphological, mechanical, and functional properties of a coating material leads to outstanding performances in different applications. In this study, coating process was investigated for highly porous alginate aerogel particles in a laboratory scale Wurster fluidized bed. In the first part of this study, fluidization regimes for alginate aerogel particles with two different particle sizes were characterized in the Wurster fluidized bed both in the annular zone and the tube zone without spraying. For both particle sizes, minimum fluidization and bubbling regimes were observed both in the zones whereas pneumatic regimes existed only in the tube zone. Moreover, a new regime which was horizontal circular motion was identified for large particles in the tube zone. There existed two other regimes in the bed which were turbulent and circulatory particle motion regimes. All fluidization regimes were mapped on Kunii and Levenspiel diagrams. Circulatory particle motion regime was given on these diagrams for the first time. Furthermore, effects of particle size, batch volume, Wurster tube size, perforated plate geometry and partition gap height on the boundaries of each fluidization regime were investigated by measuring superficial air velocities at the onset and the end of each regime. In general, increasing particle size and partition gap height led to an increase in superficial air velocities at the onset and the end of each regime. With increasing tube diameter and tube length, superficial air velocities at the onset and the end of pneumatic, turbulent and circulatory particle motion regimes increased. Increasing batch volume for small particles caused a decrease in superficial air velocities at the end of bubbling and pneumatic regimes and the onset of turbulent and pneumatic regimes. At the onset and the end of bubbling, turbulent and pneumatic regimes, superficial air velocities for large particles for 400 ml batch volume were higher than superficial air velocities for large particles for 200 ml batch volume. In this part, circulatory particle motion regime which is desirable for a successful coating process was particularly investigated in detailed. The results showed that there is an upper limit for each parameter to obtain a circulatory motion of the particles. It was found that the partition gap height should be 2 cm for proper particle circulation. Maximum batch volume for the tube with 10 cm diameter was found as 500 ml whereas maximum batch volume was 250 ml for the tube with 8 cm diameter. In the second part of this study, alginate aerogel particles were successfully coated with an aqueous polymer solution in a Wurster fluidized bed without damaging the pores of aerogels. The polymer was a mixture of hydroxypropyl cellulose (HPC) and copovidone and is generally used as a protective coating for pharmaceutical dosage forms. Variations occurring in coating layer thickness around alginate aerogel particles and changing coating layer surface morphology with coating time, bed temperature, atomizing air pressure and polymer rheology were investigated for the first time. Moreover, phase and rheological behavior of the coating polymer solution and individual solutions of HPC and copovidone were studied at different temperatures to understand their spreading and adhesion mechanisms on aerogel surfaces. Several sets of experiments were conducted at three different bed temperatures and atomizing air pressures. Coating time for all the runs ranged from 5 minutes to 40 minutes and the coating layer thickness ranged from 12.4 ± 4.6 µm to 170.6 ± 43.3 µm. The smoothest coating layer surface and the highest coating efficiency which was 69.2 ± 0.4 % with a linear increase in coating layer thickness were achieved at 50 °C with 1.7 bar. An increase in atomizing air pressure from 1.5 bar to 1.7 bar resulted in a smoother coating layer. A high mean coating polymer solution droplet velocity with a narrow droplet size distribution led to a homogeneous spreading and less variance in coating layer thickness at 1.7 bar. It was found that changing bed temperature led to more important changes in coating layer thickness compared to atomizing air pressure whereas both bed temperature and atomizing air pressure affected coating layer surface morphology to a great extent. Finally, drug loaded, and unloaded alginate aerogel particles were successfully coated with a methacrylic acid-ethyl acrylate copolymer aqueous solution using a Wurster fluidized bed. For unloaded aerogels, atomizing air pressure was set to 1.7 bar depending on optimized conditions obtained in the protective coating. To prevent particle breakage and provide an increase in coating layer thickness, atomizing air pressure was changed during the coating process for drug loaded aerogels between 1.3 bar and 1.5 bar. The highest coating layer thickness was 50 ± 5 μm and was reached in 50 minutes for unloaded aerogels whereas coating layer thickness was found as 83.8 ± 11.9 μm in 3 hours for drug loaded aerogels. Two different coating layer surface morphologies on the coating layer were observed as bead and fiber for both unloaded and loaded aerogels. Rheology experiments showed that coating polymer stayed stable in a wide range of frequency domain and its viscosity was nearly constant in lower Newtonian region in applied atomizing air pressure range. Therefore, different coating mechanisms at different atomizing air pressures may lead to bead and fiber shaped surface morphologies. Subsequently, drug loaded, and coated alginate aerogels were used as drug carriers. Ibuprofen was used as a model drug and its release from coated and uncoated aerogels were investigated both in the acidic and basic mediums. It was first time shown in the literature, ibuprofen release in the acidic medium was prevented via synergetic effects of coating polymer with a proper coating layer thickness and alginic acid layer around the aerogels. In the basic medium, uncoated and coated aerogels provided different release profiles compared to the release profile of crystalline ibuprofen. Enteric coating led to a decrease in the release rate whereas ibuprofen release rate was increased with uncoated aerogels.
Novel MOF/aerogel composites ((MOFACs) for drug delivery purposes
Composites of MOFs and aerogels (MOFACs) are a new class of nanostructured materials attracting increasing attention due to their favorable properties. The combination of micro-/mesoporosities of MOFs with meso-/macroporosities of aerogels makes MOFACs hierarchically multimodal porous materials. MOFACs with their high surface areas, combined morphological, mechanical, physicochemical and functional properties of both MOFs and aerogels have demonstrated outstanding performances in various applications. In the field of drug delivery, MOFs and aerogels have both been investigated extensively. Combining bimodal porosity and functionality of both MOFs and aerogels is a promising area for drug delivery applications. In this study, novel Metal-Organic Frameworks/Aerogel composites (MOFACs) comprised of different types of MOFs (Fe-BTC, ZIF-8 or UiO-66) and calcium alginate were synthesized. Spherical bead shaped composites were obtained by incorporating microporous MOFs into meso- and macroporous aerogel matrix by simple mixing combined with dripping technique. The composites were characterized with nitrogen sorption, XRD, SEM and FTIR. The nitrogen adsorption study showed that the MOFACs synthesized had achieved hierarchically macro-, meso- and micro porosities and MOFs micropores were open and accessible. XRD analysis revealed that the MOFs were intact and in crystalline form. FTIR study showed that the MOF and alginate matrix formed a physical mixture in the composite. The synthesized composites were further loaded with two different drugs paracetamol (acetaminophen) or ibuprofen. Paracetamol was loaded from ethanolic solutions via SAS (supercritical antisolvent precipitation) inside the pores whereas ibuprofen was loaded from supercritical CO2 solutions. The factors that affect the loading amount and distribution of the drug inside the composite matrix were investigated. Paracetamol loadings were found to be highly dependent on and limited by the drug concentration in ethanolic loading solutions as well as having excess solutions in the extraction vessel prior to supercritical drying. Almost 70 wt% paracetamol loadings were achieved with 1.2 M loading solutions and having excess solutions. Around 20 wt% ibuprofen loadings were achieved from supercritical CO2 solutions. The release behavior of the drugs from the composite matrices to PBS buffer or distilled water were investigated. It was shown that the Fe-BTC/Alginate composites were promising for paracetamol delivery and ZIF-8/Alginate composites were found to be promising for ibuprofen delivery as both systems achieved delayed drug delivery with increasing amounts of MOF content in the composites. Lastly, all release kinetics were fitted into Korsmeyer-Peppas model. It was found that Fickian diffusion is the main transport mechanism while some PBS systems with excess solutions also showed swelling behavior.
Towards heterogeneous biocatalysis of glutathione production by selective conjugation of enzymes
Many value-added specialty chemicals are produced using biological processes. While these processes are efficient, safe and environmentally friendly; they are fragile and sensitive to changes in environmental parameters such as pH and temperature. Enzyme immobilization may alleviate these concerns by stabilizing the proteins and allow for their convenient recycling. However, immobilization by conventional methods risks enzyme inactivation due to lack of control over the coupling chemistry. Creation of catalytic biomaterials / particles by precise conjugation may solve the mentioned issues. In this project, significant progress was made toward such a proof-of-concept catalytic biomaterial. The concept comprises a PEG-based scaffold, to which biosynthetic enzymes would be coupled that contain uniquely reactive non-natural amino acids (nnAAs). For this purpose, glutamate-cysteine ligase (GCL) and glutathione synthetase (GS) enzymes were used, which catalyze the biosynthesis of the high-value antioxidant glutathione (GSH). GCL and GS enzymes were produced by cell-free protein synthesis (CFPS), and modified by site-specific incorporation of the Click-compatible nnAA para-propargyloxy-phenylalanine (pPaF). Various parameters related to CFPS and the addition of exogenous proteins such as chaperones were studied to improve modified protein yields. The activities of the modified enzymes compared to those of their wild-type counterparts, and the modified enzymes retained 80% of their activity. Finally, modified green fluorescent protein (GFP) was used to study PEG coupling via Click chemistry. In the second part of the thesis, production of the biological anti-inflammatory drug anakinra was studied towards use in treating Covid-19-related cytokine storm. Anakinra is a licensed biological drug used in the treatment of various inflammatory diseases such as Behçet's disease and familial Mediterranean fever (FMF). It is obtained by recombinant production of human interleukin 1 receptor antagonist (IL-1Ra) protein in bacterial cultures. In a part of the study, expression of anakinra under different promoters (T7, tac and cspA) in Escherichia coli was tested. After production and purification, the pure and intact protein was shown to have comparable biological affinity, activity, purity and safety as the commercially available drug. Optimal process conditions were identified for anakinra production to be transferred to large-scale manufacturing.
Structural factors controlling the stability of atomically dispersed supported iridium catalysts
Atomically dispersed supported metal catalysts provide many advantages such as, offering maximum utilization of noble metals, providing new and unprecedented catalytic properties, and obtaining structure-performance relationships owing to their high degree of uniformity. Besides these advantages, they face several challenges which limit their utilization in industrially viable applications. This dissertation focuses on overcoming two of these challenges: their limited stability under reaction conditions and their limited metal loadings. SiO2, γ-Al2O3, and MgO-supported Ir(C2H4)2 complexes at 1 wt% Ir loading were synthesized to evaluate them for their stability under a pure H2 flow during a ramp from room temperature to 120 °C by in-situ X-ray absorption spectroscopy (XAS) measurements. Infrared (IR) spectroscopy measurements and density functional theory calculations confirmed the strong, intermediate, and weak electron donor tendency of MgO, -Al2O3, and SiO2, respectively, to the Ir centers. Extended X-ray absorption fine structure (EXAFS) indicated nanoparticle formation on the weak-electron-donor SiO2 and small Ir4 cluster formation on the intermediate-electron-donor -Al2O3, upon H2 treatment. The Ir complexes remained intact when supported on the strong-electron-donor MgO under identical treatment conditions. When the most severely aggregating sample, Ir(C2H4)2/SiO2, was coated with an electron-donor ionic liquid (IL), 1-n-ethyl-3-methylimidazolium acetate ([EMIM][OAc]), aggregation could be hindered, as well. Results illustrate that the electron density on Ir centers controls their aggregation behavior. A special support, reduced graphene aerogel (rGA), was used to support Ir(C2H4)2 complexes at a high loading of 9.9 wt%. Characterization of the sample by IR and EXAFS evidenced that Ir centers in the fresh catalyst are site-isolated. Next, under a flow of equimolar ethylene and H2 during a ramp from room temperature to 100 °C and a subsequent 30 min isothermal period at 100 °C, small Ir4 clusters were formed on rGA. When the feed condition was changed to a H2-rich feed flow (H2:C2H4 = 2, molar) and then a pure H2 flow for 30 min each at 100 °C, Ir4 clusters were transformed into small Ir6 clusters, confirmed by in-situ XAS and scanning transmission electron microscopy. These small clusters still offer atomic dispersion of Ir, and provide advantages for ethylene hydrogenation owing to the presence of neighboring Ir sites, easing H2 activation. It was found that Ir clusters provide higher catalytic performance compared to their site-isolated analogues in the following order: Ir/rGA << Ir4/rGA < Ir6/rGA. The unique properties of rGA lead to the stabilization of small Ir6 clusters at a significantly high metal loading. Next, rGA-supported Ir(C2H4)2 complexes at a loading of 9.9 wt% were coated with an electron donating IL ([EMIM][OAc]) aiming to stabilize atomically dispersed Ir complexes during reaction. In-situ EXAFS data confirmed that subjecting the [EMIM][OAc]-coated catalyst to ethylene hydrogenation conditions under varying H2:C2H4 ratios at 100 °C, followed by a pure H2 treatment at 100 °C did not result in any aggregation. COSMO-RS calculations point out that besides the electronic effect, a filtering effect of the IL takes place. IL coatings offer a broad potential for the stabilization of atomically dispersed metal catalysts at such a high surface metal density. The rGA was further used as a support for Ir with reactive ethylene ligands, to reach an exceptionally high metal loading of 23.8 wt%. In-situ XAS data confirmed that the catalyst remained stable under working state for 2 h under ethylene hydrogenation with equimolar ethylene and H2 flow. Increasing the temperature at the aforementioned feed conditions to 100 °C, retaining the catalyst for 30 min at equimolar flow, 40 min at H2-rich feed flow (H2:C2H4 = 2, molar) and finally 40 min at pure H2 flow led to the formation of Ir nanoclusters (~ 1 nm) characterized by Ir–Ir first and second shell coordination numbers of 6.6 ± 0.3 and 2.6 ± 0.9, respectively. Another aim was to assess the thermal stability limits of ILs on metal oxides so that once they are used as coatings on the atomically dispersed supported metal catalysts, these stability limits set the highest operating temperatures. 29 different ILs were immobilized on MgO and SiO2, to represent basic and acidic supports, respectively. The maximum tolerable temperatures of bulk ILs, and when they were immobilized on the metal oxides were evaluated by thermogravimetric analysis. To investigate the factors affecting the thermal stability of ILs on metal oxides, changing alkyl chain length, the methylation on C2 site in imidazolium ILs, the change in substituent position in the ring of pyridinium ILs, the change in the anion type, and the change in the IL family (imidazolium, pyridinium, piperidinium, pyrrolidinium) was investigated. Results show that the basicity of the support and the hydrophilicity of the anion and their resulting interactions are crucial in determining the thermal stability limits of ILs on metal oxides. Findings present the opportunities for picking a suitable IL/metal oxide pair for their use in catalytic systems, such as their utilization in stabilizing atomically dispersed supported metal catalysts. Results presented in this dissertation provide the factors determining the stability of atomically dispersed iridium complexes on supports and the use of rGA as a promising support material to stabilize Ir complexes during reaction and to reach a high metal loading. Data presented further demonstrated the opportunities of using IL coatings to hinder aggregation of these catalysts, even under harsh conditions, such as pure H2 at elevated temperatures.
Combining ionic liquids with metal organic frameworks for CO2capture and separation applications
Growing global reliance on fossil fuels for energy production has resulted in record-high emissions of CO2 into the atmosphere, causing environmental disasters due to global warming and climate change. Thus, the demand for energy resources alternatives to traditional fossil fuels is ever-increasing. Natural gas (CH4) is one of the promising and relatively clean energy source; however, it contains undesirable impurities such as CO2, which reduces the overall heating value and acidifies the gas streams. Therefore, it is highly desirable to selectively and efficiently capture and separate CO2 from natural gas and flue gas streams. In this regard, numerous nanoporous materials with high specific surface areas, high thermal stabilities, and large overall pore volumes have been studied for gas capture and separations applications. However, the synthesis of the sophisticated architecture of microporous structures requires complex synthesis procedures. An alternative approach is to design composite materials by combining two or more materials with different physicochemical properties by using a simple post-synthesis modification strategy. In this dissertation, ionic liquids (ILs) are combined with metal organic frameworks (MOFs) by a post-synthesis modification technique to tune the physicochemical properties of pristine MOFs and investigate the gas capture and separation performance of the corresponding IL-based MOF adsorbent materials. This approach offers the opportunity to introduce new adsorption sites and promotes molecular diffusion paths for the guest molecules. In the first part of this dissertation, influence of ILs' structural factors on the IL-MOF interactions, and consequently the impact of the corresponding molecular interactions on the CO2 capture performance of IL-based MOF composites are discussed in detail. For instance, 1-n-butyl-3-methylimidazolium thiocyanate ([BMIM][SCN])-incorporated zeolitic imidazolate framework (ZIF-8) showed 2.6- and four-times higher ideal CO2/CH4 and CO2/N2 selectivity at low-pressure. Similarly, when imidazolium-based IL with a fluorinated anion was incorporated into ZIF-8, the CO2/CH4 selectivity improved by three-times. On the other hand, when an IL with a small anion was incorporated into ZIF-8, ideal CH4/N2 selectivity of the resulting IL/ZIF-8 composite improved approximately two-times when compared to IL/ZIF-8 composite having a relatively bulky anion. Considering the existence of nearly infinite numbers and variety of ILs and MOFs, the selection of IL-MOF configuration for the rational design of hybrid IL/MOF composite is challenging. Thus, it is highly required to develop a systematic methodology and rationale approach to find the best IL and MOF candidates, which will ultimately result in a composite material with extraordinary performance for any targeted application. Therefore, we developed an integrated computational-experimental hierarchical approach by combining three powerful computational tools i.e., Conductor-like screening model for realistic solvents (COSMO-RS) calculations, density functional theory (DFT) modeling, and grand canonical Monte Carlo (GCMC) simulations to select IL-MOF configuration in a systematic manner. Subsequently, the best predicted IL/MOF composite was synthesized, and gas adsorption tests were performed. Our results showed that ideal CO2/N2 selectivity of IL/UiO-66 composite increased from 33.8 to almost infinite selectivity (>100000) in the low-pressure region. Although, incorporating ILs into MOFs improves gas capture and separation performance, partial occupation of ILs molecules in MOF pores reduces the available pore volume, resulting in lower overall gas uptake. In the second part of this dissertation, a new concept known as core-shell type IL/MOF composite was introduced by depositing IL (shell) on the outside surface of the core (MOF). In this case, the IL layer acts as a smart gate, selectively transporting adsorbate molecules through the IL layer into MOF pores, which are completely available for the guest molecules. The resulting core-shell type IL/MOF composite exhibited 5.7-times improved CO2 uptake. Furthermore, ideal CO2/CH4 selectivity improved by 45-times at low pressure, which is the highest level of improvement among other MOFs prepared using various post-synthesis modification techniques. Finally, in the last part of this dissertation, the impact of changes in structural factors of ILs such as the functionalization of the cation, increasing the alkyl chain length of cation, methylation at the C2 position of cation, and type, electronic environment, and size of the anion on the decomposition temperatures of IL/MOF composites are discussed. Moreover, quantitative structure-property relationship (QSPR) analysis were made using bulk ILs structural descriptors determined by DFT calculations to obtain a mathematical expression which can satisfactorily predict the decomposition temperatures of IL/MOF composites.
Supercritical CO2 assisted preparation of Pt, PtCu, PtZn, PtCo and PtZnCo nanoparticles on various carbon aerogels as ORR electrocatalysts
Monometallic Pt, bimetallic PtCu, PtZn, PtCo and trimetallic PtZnCo nanoparticles on various carbon aerogels and N-doped carbon aerogels were prepared using supercritical deposition technique. Electrochemical oxygen reduction performance of the electrocatalysts was investigated along with material properties. For the first part of the thesis carbon aerogel (CA) and Vulcan supported PtCu electrocatalysts were prepared using the simultaneous and sequential in-situ supercritical deposition (SCD) method followed by thermal annealing and electrochemical dealloying. Effect of deposition technique to PtCu nanoparticle morphology and electrochemical performance was investigated. Highly dispersed PtCu alloy nanoparticles with small nanoparticle sizes were obtained by both routes. Simultaneous SCD resulted in a more uniform PtCu composition in PtCu alloy nanoparticles before dealloying, whereas sequential SCD led to Cu-rich surface on the PtCu alloy nanoparticles. After dealloying, PtCu/CA electrocatalyst prepared by simultaneous supercritical deposition had an enhanced electrochemical surface area of 159.4 m2/g due to the synergistic effects of PtCu nanoparticle size and PtCu composition in nanoparticles. All dealloyed electrocatalysts had higher mass activities and PtCu/CA electrocatalyst prepared by simultaneous SCD had a mass activity of 0.15 A/mgPt which was 2-fold of the mass activity of commercial Pt/C. PtCu/CA electrocatalyst prepared by sequential SCD showed a mass activity of 0.08 A/mgPt which was slightly higher than the mass activity of commercial Pt-C (0.07 A/mgPt) In the second part of the thesis effect of N-doping on electrochemical performance was investigated. Pt nanoparticles on polyamide aerogel (PA) derived CAs were prepared using SCD technique. PAs were pyrolyzed at 800 oC and some monoliths were subsequently etched with CO2 at 1000 oC to increase mesoporosity and surface area to demonstrate the effect of pore volume to Pt nanoparticle dispersion and electrocatalytic performance towards oxygen reduction. The N-rich backbone of PAs yielded homogenously distributed N atoms in the CA structure enabling homogenous distribution of Pt nanoparticles, efficient dispersion of the Nafion ionomer and possible ORR-active sites. Highly dispersed Pt nanoparticles with average size of 1.5 and 3.0 nm were obtained on carbon aerogels from polyamide aerogels resulting from pyrolysis (CPA) or pyrolysis and reactive CO2-etching (ECPA), respectively. Both electrocatalysts had only graphitic and pyridinic N-sites with the former being the dominant species. Oxygen reduction mass and specific activity of Pt-ECPA were 4- and 3-fold of the mass and specific activity of commercial Pt-C, respectively. Pt-CPA also showed similar mass and specific activity to that of commercial Pt-C due to lower mesopore volume and higher average Pt nanoparticle size. Accelerated stability tests (AST) revealed superior stability of Pt-CPA electrocatalyst due to favorable initial Pt nanoparticle size enabling successful immobilization Pt nanoparticles on CPA through the N-functionalities. In the final part of the thesis synergistic effects of alloying and nitrogen doping was investigated. Monometallic Zn, Co and bimetallic ZnCo-Alginate aerogels (AA) were prepared using sol-gel technique followed by crosslinking with polymeric 4,4'-Diphenylmethane diisocyanate (pMDI) and supercritical drying. Pyrolysis of the crosslinked AAs resulted in metal and nitrogen doped CAs (NCA). Three different N-species were present on the metal doped-NCAs; graphitic, pyridinic and pyridinic N-oxides with high loadings indicating crosslinking is an efficient N-doping technique. Pt nanoparticles were deposited on the Zn, Co and ZnCo-NCAs using SCD technique followed by thermal annealing. Bimetallic PtZn, PtCo and trimetallic PtZnCo alloy nanoparticles were obtained with different Pt:Co:Zn molar ratios. Average nanoparticle size of the electrocatalysts with 5 wt.% initial pMDI concentration was around 6 nm for trimetallic Pt-ZnCo-NCAs and around 9 nm for 10 wt.% initial concentration. Highest average nanoparticle sizes were obtained for Pt-Zn-NCAs (12.5 and 28.6 nm) and the lowest were obtained for Pt-Co-NCAs (5.9 nm and 4.1 nm). A volcano type structure-activity relationship was established with respect to Co and Zn mole ratios in the electrocatalysts. Higher mass activity was obtained for Pt:Co:Zn mole ratio of 68:24:8. The electrocatalyst showed 1.5-fold higher mass activity and 9-fold higher specific activity than commercial Pt-C at the half of the Pt loading (9.7 wt.% and 20 wt.%, respectively).This electrocatalyst also showed the highest stability over 2,500 potential cycles.
Design of integrated renewable energy systems under uncertainty towards green deal in turkish energy market
Today, we are in a situation where we observe the effects of global warming extremely. That is why the regulations regarding the Paris Agreement are planned to be adapted as soon as possible to keep the global temperature rise within 2 0C. In that sense, it is crucial to implement the transition from non-renewable energy resources to renewable ones. Additionally, integrating renewable energy-based equipment and adopting new ways of producing energy resources, for example, Power to Gas technology, becomes essential because of the current environmental concerns. Additionally, it is vital to supply the growing energy demand with the increasing population. However, all the parameters above regarding the electricity demand, carbon tax policies, and intermittency of renewable energy-based equipment have uncertain nature, which needs to be taken into account. Hence, a multi-period two-stage stochastic MILP model is formulated to meet one-third of the power and heat demand of a certain location in Turkey, where the wind speed, solar irradiance, temperature, power demand, carbon emission trading (CET) price, and CO2 emission limit are considered as uncertain parameters in this study. Three case studies with scenarios that include the different combinations of the aforementioned uncertain parameters are investigated to observe the effect of these uncertain parameters. This model finds one single optimal design for the energy grid while considering several scenarios regarding uncertainties simultaneously. In the optimal results, more renewable energy-based equipment with higher rated power values is chosen from Case 1 to Case 3.c. Cases 2 and 3 investigate the optimality of PtG technology, which is not optimal for the specific location due to wind and solar profiles. Additionally, implementing the CO2 emission limit as an uncertain parameter instead of including CET price as an uncertain parameter result in lower annual CO2 emission rates and higher net present cost value. This work also includes the results of the deterministic MILP model with a higher candidate pool. In the deterministic results, it is observed that photovoltaic, oil co-generator, reciprocating ICE, micro turbine, and bio-gasifier are the equipment that is commonly chosen under the three different scenarios. Deterministic results also show that concepts such as green hydrogen and power-to-gas are currently not preferable for the investigated location.
Human growth factor production towards in vitro vascularization
Tissue engineering is an important method for treating damaged or injured tissues. The formation of new blood vessels from endothelial cells is called angiogenesis. This process includes numerous interactions among different proteins, cells, and the extracellular matrix; these interactions govern the formation of new vessels by introducing different growth factors at different times to regulate the blood flow in tissues. Thus, growth factors play a significant role during vascularization. In this thesis, our primary aim was to successfully synthesize three different growth factors, namely basic fibroblast growth factor/fibroblast growth factor 2 (bFGF/FGF-2), and vascular endothelial growth factor (VEGF), and angiopoietin-1 (Ang-1). These three proteins were chosen since bFGF and VEGF are important in early angiogenesis, while Ang-1 is one of the main factors governing late angiogenesis/maturation of primary vessels. In order to produce these proteins, two different methods were used, namely production in E.coli and cell-free protein synthesis (CFPS).
Composites of porous materials with ionic liquids: Synthesis, characterization, and selective gas sorption applications
Hybrid composites prepared by combining ionic liquids (ILs) with porous materials have become an increasingly important research field over the last few years owing to their tunable physicochemical properties. The enormous and continuous growth in the number of publications on the synthesis of such IL/porous materials and their exceptional performances in many different applications, mainly in gas adsorption and separation applications, is a proof of importance of this field. Combined advantages of ILs and porous materials provide great potentials in gas sorption and separation owing to the superior performance measures of the hybrid composites. In this thesis, it was aimed to provide a contribute to the evolution of IL/porous material composites as a research field by considering two different types of porous materials, including metal-organic frameworks (MOFs) and carbonaceous-materials. Accordingly, in depth analyses on the synthesis methods, characterization techniques, and selective gas separation performance of IL-modified porous materials were conducted to provide important insights into the design of novel IL/porous material composites and to identify the most promising hybrid materials for gas adsorption and separation applications. Examination of the structural factors controlling the chemical and thermal stabilities of novel IL/MOF composites was made to elucidate the resulting changes in the physicochemical structure of pristine MOF upon the incorporation of IL. Results illustrated that the composite with the ligand-functionalized MOF exhibited a lower degree of decrease in the thermal stability limits compared to the those containing non-functionalized counterpart. Then, systematic changes in the structure of MOF were further investigated by incorporating the same IL, into the cages of three different Zr-MOFs and extraordinary performance improvements in the selective gas separation were achieved emphasizing the significance of MOF selection on the resulting molecular affinity. Motivated by the superior performance obtained through the presence of a surface IL layer, a core-shell type IL/MOF composite was designed by an integrated computational-experimental hierarchical approach for improved air separation application. Later on, an activated carbon (AC) was modified with IL to extend the scope of the field by utilizing a cost-effective and chemically robust porous material for enhanced CO2 separation performance. The main challenges and opportunities in synthesis methods, characterization techniques, and gas sorption applications of IL/porous materials were discussed in detail to create a road map for the era. Recent advances of the field addressed in this thesis will provide a more in-depth insight into the design and development of these novel hybrid materials and their replacement with conventional materials.
Synthesis of copper-exchanged zeolites by supercritical ion exchange for the production of liquid fuels
A new technique termed Supercritical Ion Exchange (SCIE) was developed and used to synthesize Cu-mordenite (Cu-MORS). The ion exchange takes place between the Cu complex (Copper(II)trifluoroacetylacetonate) dissolved in supercritical CO2 (scCO2) and the extraframework protons in zeolite without requiring an aqueous phase. The occurrence of the ion exchange reaction was demonstrated by using 1H NMR analysis of the high-pressure fluid phase samples and by visual inspection of the fluid phase color change during the synthesis. SCIE resulted in selective ion-exchange inferred by the equilibrium isotherm. The other zeolite frameworks such as ZSM-5 and SSZ-13 were successfully used in SCIE to synthesize copper exchanged zeolites. As the first application, the stepwise direct methane to methanol (sDMTM) over Cu-MORS was investigated. The results showed that methanol productivity increased linearly with increasing Cu loading up to a certain Cu wt%. Cu-MORS displayed 16% higher methanol productivity as compared to Cu-MORA (prepared by aqueous ion exchange) with the same Cu loading, demonstrating the importance of site selective ion-exchange for zeolite catalysis. Increasing the oxygen activation temperature and methane reaction time enhances the methanol productivity of Cu-MORS. The reducibility of Cu-MORS was compared with those of Cu-MORA prepared by aqueous ion exchange (AIE) using H2-TPR. It was demonstrated for the first time that deconvoluted H2-TPR profile coupled with effects of Cu loading and oxygen activation temperature on methanol productivity data can be used to distinguish the active Cu sites from inactive ones based on their reduction temperature. The copper sites responsible for methane activation were found to be reduced below 150 °C by H2 in both Cu-MORS and Cu-MORA. From the stoichiometry of the reaction of H2 with Cu2+ species, the average number of copper atoms of active sites were calculated as 2.07 and 2.80 for Cu-MORS and Cu-MORA, respectively. Differences in structure of copper species caused by the synthesis routes were also detected by in-situ FTIR upon NO adsorption indicating a higher susceptibility of Cu-MORS towards autoreduction. The results demonstrated the potential of TPR based methods to identify copper active sites and suggested the importance of site selective ion exchange in order to controllably synthesize active Cu species in zeolites. Catalytic hydrothermal liquefaction of microalgae was the second application of Cu-exchanged zeolites as the catalysts prepared by SCIE. Two different microalgae, Chlamydomonas nivalis (C. nivalis) and Nannochloropsis gaditana (N. gaditana), were cultivated in a pilot scale open pond. The harvested wet biomass was converted to bio-crude by hydrothermal liquefaction (HTL) with/without catalyst. C. nivalis is known as snow algae grown in cold environments was used for this process for the first time in the literature. Catalytic HTL experiments were performed by introduction of Cu-MOR, Cu-ZSM-5, and Cu-SSZ13, synthesized by SCIE using scCO2. The composition of all bio-crudes was analyzed by elemental analyzer and GC-MS methods. First, the effect of different operating conditions on the yields of the products and the bio-crude composition was determined for non-catalytic process. Temperature, duration, and dry content of the feed were the process parameters exploited in the ranges of 250-350 ºC, 10-60 min, and 5-20 wt%, respectively. For C. nivalis, 300 ºC, 60 min, and dry content of 20 wt% were the optimum conditions led to maximum bio-crude yield of 18.8 wt%, while 300 ºC, 30 min, and dry content of 10 wt% were the optimum ones for N. gaditana at which the maximum bio-crude yield of 34 wt% was observed. Bio-crude yield of N. gaditana was improved using Cu-MOR, while using catalysts for the case of C, nivalis resulted in more gasification with no positive effect on bio-crude yield. Moreover, elemental analysis showed that the fraction of nitrogen and oxygen in biocrude decreased in catalytic HTL runs, which was in line with GC-MS results showing that the concentration of hydrocarbons and cyclic compounds increased in presence of catalysts accompanied by a decrease in concentration of nitrogenous compounds.
LaCoO3 as a catalyst precursor for CO2 hydrogenation to methane: Effects of calcination temperature on catalytic properties
A series of lanthanum cobalt oxides (LaCoO3) calcined at different temperatures (600, 700, 800, and 900 °C) have been investigated as catalyst precursors for the CO2 methanation reaction. Structural characteristics of the as-prepared samples were studied by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), X-ray fluorescence spectroscopy (XRF), temperature programmed desorption of CO2 (CO2-TPD), temperature programmed reduction (H2-TPR), N2 adsorption-desorption, and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDX) techniques. Data showed that the samples prepared at lower calcination temperatures were observed to have a slightly distorted rhombohedral crystalline structure, higher BET surface area, enhanced reducibility, and lower oxygen vacancy concentration. After the reductive treatment under 400 °C, the trend associated with the changes in the oxygen vacancy concentrations was reversed, whereas the crystal structure remained unchanged. The CO2-TPD results indicated that the reduced samples with decreasing calcination temperatures resulted in a better affinity to CO2, which is crucial for CO2 activation. The catalytic activity of reduced samples on CO2 methanation was measured at differential and high CO2 conversion conditions. Arrhenius plots showed no drastic variation in the apparent activation energy, confirming with XPS results that the difference between catalytic performances within each sample could result from the number of active sites rather than the changes in the identity of active sites. During CO2 methanation, the perovskite structure was destroyed and converted into mainly La2CO3OH, especially for the best-performing catalyst LaCoO3-600. With the increasing calcination temperature of the perovskite, the formation of La2O2CO3, La(OH)3, and metallic cobalt phases on the spent catalyst was observed. With successful activation of the LaCoO3 prior to the reaction, the superior activity of 73% CO2 conversion and 95% CH4 selectivity was observed at a space velocity of 12000 mlCO2 gcat-1 h-1 at 350°C and 40 bar using a CO2:H2 ratio of 1:4. Moreover, stability test of the LaCoO3-600 catalyst for a time-on-stream of 72-h under the identical conditions demonstrated that the methane selectivity remained unaffected with only a 10 % CO2 conversion decrease. To the best of our knowledge, the methane production rate (5959 gCH4kgcat-1h-1) that we measured on LaCoO3-600 catalyst is superior to that of all other ABO3-type perovskites, and it is attributed to the enhanced oxygen vacancy concentration of the reduced catalysts.
Viscoelastic effects on structure and dynamics of liposomes in polymeric matrices
Recent decades have seen significant advancements in the pharmaceutical industry and nanotechnology, which have led to the development of clinical research and the discovery of modern drug delivery methods. As one of the widely used nanoparticles, liposomes are employed in pharmaceutics, tissue engineering, cell membrane modelling, cosmetics, and agriculture with their high biocompatibility, biodegradability, low toxicity, and resemblance to biological membranes. Although liposomes offer serious advantages, they have drawbacks such as their thermodynamically unstable structure, quick elimination in the bloodstream, and a desire to targeted and dosage-controlled delivery. To overcome these issues, the combination of liposomes with polymer solutions, and hydrogels have been utilized since polymers provide mechanical support, stimuli-responsive release, and increased stability towards chemicals, enzymes, and immune systems. Besides, polymeric scaffolds can be designed to mimic the topology and mechanical characteristics of the native extracellular microenvironment. Although there is a great deal of interest in liposome-polymer complexes from the application point of view, the effects of viscoelasticity of polymers on liposome structure, permeability, and mobility need detailed understanding. This thesis contains two different studies on liposomes and Polyethylene glycol (PEG) mixtures. In the first one, we used 100 nm unilamellar DMPC/DMPG liposomes, and aqueous solutions of poly (ethylene glycol) (PEG) with various molecular weights from 1.5 kDa to 400 kDa to simulate the viscous media of cells. The structural phase map and multiscale dynamics of liposomes in their neat form and in the presence of PEG solutions were investigated. The findings point to a dynamical coupling at various length/time scales between polymer chains and phospholipid bilayers. The relaxation of the entire chain directly affects the microviscosity of the lipid bilayers, which causes faster dynamics of the lipids within the bilayers when compared to the polymer-free liposome case. We observed a thermal-thickening behaviour of polymer-liposome solutions at the transition temperature of the lipids from gel to fluid, which is tuneable by the concentration of liposomes and the length of the polymer chain. In the second study, we used unilamellar 100 nm DMPC/DMPG liposomes and PEG hydrogels having different elasticities, from 1 Pa to 180 Pa, and created a composite hydrogel. We studied the release of liposomes from the ECM-mimetic gel matrices under quiescent and shear deformation. The presence of liposomes provides composite hydrogels with temperature-controlled swelling capacity that is sensitive to membrane microviscosity. By systematically varying shear deformation from linear to nonlinear regimes, we studied the effect of shear deformation on the release of liposomes from the hydrogels of different stiffness.
Kinetic study for hydrodesulfurization processes of diesel fuels over CoMo-based catalyst
Diesel is an important fuel for transportation vehicles and is to be produced in compliance with Euro V specifications. The sulfur content diesel fuels is limited to be maximum of 10 ppm and deep desulfurization process is required to reduce the sulfur content of the diesel feed. Refineries process various kinds of crude oils containing high amounts of nitrogen, aromatics, metals, and sulfur compounds, with complex structures. In addition, the quality of the crude oil is also being declining; the refineries operate at the commercial units including hydrodesulfurization (HDS) processes at more severe conditions. Several changes are also implemented by the refineries such as using more active catalysts, increasing hydrogen consumption, operating temperature and pressure, improving the reactors and understanding reactor and reaction modeling. However, severe operating conditions cause rapid catalyst deactivation and decrease the catalyst life. To optimize the operating conditions and maximize the catalyst performance, HDS kinetic models are important for reviewing the catalyst activity and comparing the activities of the catalysts in the case of selecting and evaluating new catalysts i.e., catalyst screening. Therefore, it is necessary to understand the nature of HDS process for diesel feeds with respect to different operating conditions. Apparent HDS kinetics applied for industrial real feeds depend significantly on feed properties having variety of different sulfur compounds, nature of the hydrocarbon matrix, operating conditions and type of catalysts. Therefore, kinetic parameters determined for a study is specific to the related process. The purpose of this study is to investigate apparent kinetic model for diesel HDS reaction using commercial CoMo/Al2O3 catalyst based on sulfur distribution data. In the first part of the work, different characterization methods are applied to commercial CoMo/Al2O3 catalyst. In the second part of the work, HDS performance tests were conducted at different operating conditions such as temperature, pressure and H2/oil ratio and LHSV. A pilot plant is designed, constructed and started-up during the course of the work to carry out the performance tests with continuous, safe, 24/7 unattended operations. The diesel feed and the obtained diesel products were analyzed with extensive methods to investigate the changes in the product properties and catalyst activity. In the third part of the work, apparent HDS kinetic models are derived with power law model based on total sulfur of the feed and the products as a reference point to compare with literature. In the fourth part of the work, apparent HDS kinetic model was investigated using the sulfur distribution of the feed and the liquid products by dividing into several sub-fractions using the sulfur distribution data from CNS-Simdis analysis. Then, power law model based on the sulfur contents of the related sub-fractions was applied to determine the apparent reaction rate order and activation energy of each sub-fraction. In the fifth part of the work, the feed and the products were separated physically into several fractions with distillation and the properties of the fractions were analyzed. Power law model was applied to each fraction to compare with the results obtained in the third part of the work.
Screening of supported bimetallic catalysts for hydroconversion of microalgae oil
Civil aviation sector is one of the fastest growing industries, resulting in a constant increase of CO2 emissions and global warming. Air transport industry and members pledged to achieve net-zero carbon emissions by 2050 at the International Air Transport Association (IATA) 77th annual general meeting. In order to achive this target, it is essential to switch from non-renewable fuels to sustainable aviation fuels (SAF). In line with carbon neutral growth target of the aviation sector and necessity of sustainable aviation fuels to achieve this target, microalgae can be a valuable sustainable source since they do not threaten natural sources, do not require arable land and grow faster and can accumulate high amount of lipid when compared to other biomass feedstocks. Microalgae oil containing triglycerides can be hydrotreated to remove oxygen content first and then long chain free fatty acid molecules are hydrocracked to shorter chains under pressurized H2 atmosphere by using catalysts to obtain liquid fuel. Under this framework, other than temperature, pressure and other reaction parameters, the type of catalysts used has the major importance and effect on the product selectivity. Apart from noble metal catalysts, sulfided bimetallic catalysts, such as NiMo, CoMo etc. are the commonly used catalysts for hydroprocessing. However, sulfided catalysts can cause contamination of the product. In this respect, playing with the atomic ratio of the bimetals without sulfiding the catalyst have been an attractive alternative method to enhance hydroprocessing performance. In this collaborative research, microalgae Nannochloropsis gaditana were cultivated in open ponds, harvested and the lipid accumulated in the cells were extracted via disruption of the cells and taken via solvent extraction by our collaboratives at Boğaziçi University. Meanwhile, more than 48 non-sulfided bimetallic catalysts NiMo, CoMo, NiW and CoW with different atomic ratios were synthesized on γ-Al2O3, Zeolite-Beta and Zeolite-Y. They were characterized in a deep detail by combining X-ray diffraction (XRD), X-ray fluorescence spectroscopy (XRF), scanning electron microscopy with energy dispersive X-ray (SEM-EDX), Brunauer-Emmett-Teller (BET) and X-ray photoelectron spectroscoy (XPS). Hydroconversions of Nannochloropsis gaditana oil were tested by using these bimetallic catalysts under different reaction conditions in batch reactor firstly. The quick screening study was done by performing 2 hours duration hydroconversion tests in batch reactors operating at 220 °C and 30 bar. The collected liquid products were analyzed by gas chromatography and mass spectrometry (GC-MS), high-temperature simulated distillation (HT-Simdis), carbon-nitrogen-sulfur simulated distillation (CNS-Simdis) to understand transformation performance of fatty acids into liquid fuel range hydrocarbons cuts. Based on this screening study which was conducted at identical conditions, the best performing catalyst with the highest jet fuel yield was determined as 3.2CoMo/γ-Al2O3.
Modeling of delayed coker unit
Delayed Coker Unit (DCU) converts the vacuum residual feedstock to lighter and more valuable products such as motor fuels and eliminates the low-order, environment-damaging streams. Thus, optimal operation of this unit provides great economic return. In this thesis, models have been developed to build a base for the future optimization and control studies of an industrial DCU which exists in the TUPRAS Refinery. In the unit, flow starts from the bottom of the main fractionator column, passes through coking furnaces and then coking drums and pours back in the main fractionator column. In the first part of the thesis, fractionator bottom and coker furnace models are provided. Fractionator bottom model is created on the Aspen Hysys simulation program. The designed furnace model, on the other hand, is formed based on steady-state, multi-phase and Euler-Euler approach. It is assumed that all reactions occur in the liquid phase, coke is not formed as a result of cracking reactions and the reaction products are formed in small amounts as gas phase. The actual operational output temperature and pressure values of the furnace and the model prediction results are very close. One of the most important equipment of the coking unit is coke drums, which are periodically changed and put into operation. Thermal decomposition takes place in coke drums and the vacuum residue turns into white products and coke. In the last part of the thesis, steady-state models of coke drums are developed. The steady-state model is constructed assuming the coke drums as a trickle bed reactor, in which the gas and liquid flow proceed through the reactor, while the coke bed remains motionless like the catalyst. The obtained steady-state model was used for predicting the coke particle diameter and the distribution of the products at the outlet of the coker drums. Coke drum model was verified by comparing obtained results with actual plant data.
Graphitic carbon nitride/red phosphorus heterojunctions decorated with platinum nanoparticles as catalysts for the photo-assisted hydrolysis of ammonia borane
Increasing usage of fossil fuel as the primary energy source is the main reasoning of global warming due to the undesirable greenhouse gas emissions. To mitigate greenhouse gas emissions and ensure a sustainable future, it is urgent to adopt alternative energy sources. Among the alternatives, hydrogen as an energy carrier, which can be produced from numerous sources at all geographies and emits only water upon its combustion, is one of the most promising one. Therefore, it plays an important role in the energy transition for net-zero emission targets. However, the safe storage and transportation of hydrogen remain significant challenges that need to be addressed. While storage of hydrogen in gas or liquid state presents several risks and is not efficient in terms of the capacity, chemical hydrogen storage in low-volume and lightweight solid materials offers the best solution. Among the chemical hydrogen storage materials, ammonia borane is the most promising one due to its high hydrogen content, stability under ambient conditions, high solubility in water and non-toxicity. However, the dehydrogenation of ammonia borane is a considerably slow process, and an efficient catalyst is needed to make it a viable option for widespread use in hydrogen economy. Among the possible ways of dehydrogenation of ammonia borane, hydrolysis of ammonia borane (HAB) was both theoretically and experimentally shown to be superior to other methods. For this reason, to date, many metal catalysts (Fe, Ni, Co, Pt, Ru, Rh, Au) have been tested in the HAB. However, these metal catalysts tend to agglomerate and suffer from low stability due to their high surface energy. As a solution, the utilization of high surface area support materials for the immobilization of metal NPs has been reported as the best choice. Immobilization of metal NPs onto semiconducting support materials has got particular attention since it is possible to boost the activity of metal catalysts under light irradiation by the heterojunction formation between semiconducting support and metal NPs. Two-dimensional graphitic carbon nitride (g-CN) has gained great attention in photocatalytic hydrogen generation reactions due to to its favorable band positions and visible-light activable bandgap (2.7 eV). However, g-CN suffers from high photogenerated electron-hole recombination and low visible light utilization, which inhibits its widespread application. In this regard, coupling g-CN with another semiconductor material to form heterojunctions is seen as a great option to improve its optical properties and charge dynamics. For this purpose, red phosphorus (RP), a commercially available, low-cost, visible light-active semiconductor material with its wide range of optical absorption and suitable band configuration, can be a proper option for constructing a heterojunction with g-CN. Therefore, g-CN was coupled with RP (RP/g-CN) to reach improved optical properties in this thesis. Since HAB reaction requires a metal catalyst for hydrogen generation, the as prepared RP/g-CN heterojunction was also decorated with Pt nanoparticles (NPs), and the resulting Pt/RP/g-CN ternary composites were tested in the HAB. The Pt/RP/g-CN catalyst was synthesized by using a two-step procedure comprising the liquid-phase impregnation of hydrogen hexachloroplatinate (IV) complex (hexachloroplatinic acid) to as-prepared RP/g-CN binary heterojunctions and then following chemical reduction by NaBH4. We demonstrated that the catalytic activity of Pt/RP/g-CN increased by 33% under visible light irradiation compared to dark one. The highest activity was recorded with the 5.53 wt% Pt loaded optimum RP25/g-CN75 catalyst with the turnover frequency (TOF) of 142 mol H2.mol Pt-1. min-1. The structural, chemical, and optical properties of all synthesized pristine, binary and ternary materials were investigated by using many advanced characterization methods (XRD, TEM, XPS, FTIR, UV-vis DRS, PL, TRPL, EIS). The improved activity of ternary catalyst compared to Pt/RP and Pt/g-CN binary ones was attributed to its increased visible light absorption ability and reduced electron-hole recombination. Reduction in the photogenerated charge recombination was attributed to the Schottky junction formation between both semiconductors and Pt, and heterojunction formation between RP and g-CN. To unveil the mechanism, the band structures and electron flow paths were further investigated by several experiments. RP and g-CN was revealed to have staggered band structure. Investigations revealed type-2 heterojunction formation with no hole flow making it possible to utilize higher oxidation potentials. We called this structure as complex type-2 heterojunction. Mechanistic studies were also performed by performing several trapping experiments and the roles of electrons, holes and OH• radicals were explained. Next, kinetic studies were performed with the optimum catalyst to determine rate equation and activation energy. The photocatalytic HAB was found to be first order in terms of Pt concentration and zeroth order in terms of AB concentration, and activation energy was found as 52.64 kJ/mol. The reusability and stability of the catalyst was also investigated, and activity was recorded for 10 consecutive experiments. After the initial use, the activity was noted to drastically decrease. However, after that it was observed to conserve its activity for at least ten cycles with only a small decrease after each cycle. Post-characterization of the catalyst was revealed Pt NPs agglomeration after the first-use giving rise to decrease in the activity.
Investigating the effect of reduction temperature and ionic liquid deposition on gas separation performance of ionic liquid/reduced graphene aerogel composites
With the advancements in oil and chemical industries, efficient methods to mitigate the separation problems became increasingly important. Sorption-based gas separation is one of the promising methods that aims to tackle the separation necessities with cost-efficient synthesis steps and high gas selectivities. Carbon-based materials are one of the most preferred sorbents due to their availability and tunability, and metal-organic frameworks (MOFs) are one of the most heavily researched sorbents due to their easily designable nature and exceptional capability to sorb gases with highly porous structure. To enhance the gas separation capability of existing sorbents, many functionalization techniques are being investigated. The incorporation of ionic liquids (ILs) into porous hosts has proved to be an influential functionalization method to tune the sorption-based gas separation performance of porous materials with many reports illustrating the effect of IL deposition in porous composites in the last decade. Inspired by these studies, this thesis is dedicated to investigating the gas separation performance of IL-loaded reduced graphene aerogels (rGAs) and an IL-coated MOF composite.
Physics-informed machine learning-based modeling and control of dynamic process systems
Many of the processes in chemical engineering applications are of dynamic nature. Mechanistic modeling of these processes is challenging due to the nonlinearity and complexity including the fact that the models often comprise uncertainties. On the other hand, recurrent neural networks that can process sequential data are useful to be utilized to model dynamic processes by using the available data. Although these networks can capture the complexities, they might contribute to overfitting and require high-quality and adequate data. Physics-informed neural networks might offer promising results by overcoming the limitations associated with inadequate training. In this thesis, two different physics-informed training approaches are investigated. The first approach is using a multi-objective loss function in the training including the discretized form of the differential equation. The second approach is using a hybrid recurrent neural network cell with embedded physics-informed and data-driven nodes performing Euler discretization. Two synthetic case studies for a semi-batch reactor and a wastewater treatment plant are developed to observe the effect of physics-informed approaches. Additionally, data from a wastewater treatment plant in Tüpraş İzmit Refinery is used to observe the difficulties and usefulness of the implemented approach in industrial case studies. It is demonstrated that physics-informed neural networks can improve test performance even though a decrease in training performance might be observed. Additionally, hybrid recurrent neural networks predict the trend successfully regardless of the learning performances of the data-driven nodes. For some of the machine learning models, hyperparameter optimization is done using search algorithms. When physics-informed training is performed, smaller and more robust architectures are obtained using hyperparameter optimization. This thesis also includes the comparison of nonlinear model predictive controllers which use the first-principles model, recurrent neural network model, and physics-informed recurrent neural network model. The controller performances are observed for two case studies of a semi-batch reactor and a Van de Vusse reactor. It is concluded that recurrent neural network-based controllers could achieve desired performances and deliver similar results with nonlinear first principles-based model predictive controllers. Finally, it is observed that physics-informed recurrent neural network-based controllers could improve the controller performances compared with the physics-uninformed recurrent neural network-based controllers.
Drug repurposing in Ras/Raf/Mek/ERK signaling pathway
Ras/Raf/MEK/ERK signaling pathway regulates cell growth, division, and differentiation. In this work, we focus on drug repurposing in the Ras/Raf/MEK/ERK signaling pathway, considering structural similarities of protein-protein interfaces. The protein-protein complexes in this pathway are extracted from literature and the interfaces formed by physically interacting proteins are found via PRISM (a template-based protein-protein docking tool) if not available in Protein Data Bank. As a result, the structural coverage of these interactions has been increased from 21% to 92% using PRISM. Multiple conformations of each protein are used to include protein dynamics. Then, the Food and Drug Administration (FDA) approved drugs bound to the interfaces are proposed for the other protein-protein interfaces that are structurally similar. The results suggest that HIV protease inhibitors tipranavir, indinavir and saquinavir bind to Epidermal Growth Factor Receptor (EGFR) and Receptor Tyrosine-Protein Kinase ErbB-3 (ERBB3/HER3) interface. Tipranavir and indinavir also bind to EGFR and Receptor Tyrosine- Protein Kinase ErbB-2 (ERBB2/HER2) interface. Additionally, a drug used in Alzheimer's disease (galantamine) and an antinauseant for cancer chemotherapy patients (granisetron) can bind to RAF proto-oncogene serine/threonine-protein kinase (RAF1) and Serine/threonine-protein kinase B-raf (BRAF) interface. Hence, these drugs can be used for anti-tumor activities in cancer with future experimental validation.
Structural factors controlling selectivity of supported metal catalysts for partial and semi-hydrogenation
Acetylene semi-hydrogenation and 1,3-butadiene selective hydrogenation are commonly used as model reactions for studying partial hydrogenation reactions and purifying impurities in olefin products in the petrochemical field. Therefore, catalyst design must not only consider the catalytic activity for acetylene and 1,3-butadiene but also the selectivity of the target product to avoid over-hydrogenation and polymer formation while maintaining catalytic stability. While previous research has significantly advanced catalyst development and our understanding of the catalytic process, it has mainly focused on studying Pd-based catalysts. Although Pd catalysts have shown promising catalytic performance, there is a lack of studies on other metal catalysts, particularly regarding the reaction mechanism of their active sites and the factors influencing their catalytic performance. This dissertation aims to supply this intermission by focusing on studying and discussing two non-palladium-based metals, copper and rhodium, catalysts for partial hydrogenation reactions. First, a series of CunCeMgOx catalysts with various copper nanoparticle sizes and surface defect densities were synthesized and tested for partial hydrogenation of 1,3-butadiene. Data demonstrated a reaction pathway involving the dissociation of molecular hydrogen on the peripheral oxygen vacancies (Ov-Cu+) before reacting with 1,3-butadiene adsorbed on the corresponding Cu+ atoms. Analysis of the performance data indicated that the turnover frequency of these Cu+ sites is approximately five-times higher than those of the surface Cu0 sites. Among the catalysts considered, Cu0.5CeMgOx with the smallest copper nanoparticle size showed a comparable stability, while the others were easily deactivating because of carbon deposition. Furthermore, different from the conventional copper-based catalyst, the Cu0.5CeMgOx catalyst achieved a complete suppression of total hydrogenation even at space velocities offering a complete 1,3-butadiene conversion. The findings offer a broad potential for the rational design of low-cost, highly selective, and stable copper-based partial hydrogenation catalysts for reactions that are prone to coke formation. The analysis and discussion of catalytic active sites and reaction pathways for partial hydrogenation in copper-based catalysts have been carried out through experimental design, leading to reasonable speculations. However, because of the complexity of supported nanoparticle catalysts, the focus of research has shifted towards supported mononuclear catalysts, aiming to achieve a deeper and more accurate understanding of catalytic reaction mechanisms. In the case of supported mononuclear metal catalysts, their catalytic performance is primarily influenced by their coordination environment. Nonetheless, the facile sintering of supported atomically dispersed precious metal catalysts under high temperatures, particularly in reducing conditions, poses a challenge for their practical applications. The carbonyl ligand of mononuclear rhodium complexes supported on HY zeolite can mildly inhibit the sintering of rhodium under reducing conditions, but severely suppress the catalytic activity of the hydrogenation reaction. In this study, we demonstrate that substitution of the carbonyl ligand with acetylene ligand can maintain the atomic dispersion of the supported mononuclear rhodium complex under harsh reducing condition (> 573 K), as confirmed by in-situ X-ray absorption near-edge structure (XANES), extended X-ray absorption fine structure (EXAFS) spectroscopies, and in-situ Infrared (IR) spectroscopies. In contrast, the supported rhodium carbonyl complex aggregates into nanoclusters under the same conditions. Furthermore, our results indicate that the acetylene ligand can provide improved anti-sintering ability while retaining a limited ethylene hydrogenation activity towards ethane. Meanwhile acetylene ligands can be applied to the high temperature stability of oxide-supported rhodium complexes in acetylene semi-hydrogenation reactions. After addressing the stability-limitation of supported mononuclear rhodium catalysts under reducing conditions by changing the ligand type, supported rhodium catalysts were investigated for potential application in partial hydrogenation reactions. Supported rhodium catalysts are known to be unselective for semi-hydrogenation reactions. Now, by tuning the electronic structure of the active sites determined by the metal nuclearity and the electron-donor properties of the support, we demonstrate that atomically dispersed HY zeolite-supported rhodium with reactive acetylene ligands affords a stable ethylene selectivity >90% for acetylene semi-hydrogenation, even while ethylene present in a large excess over acetylene at 373 K and atmospheric pressure. IR and XAS complemented with calculations at the level of density functional theory and kinetics measurements show how the catalyst performance depends on the electronic structure of the rhodium, influenced by the support as a ligand. After investigating the influence of electron density on supported mononuclear rhodium centers on their catalytic performance, we conducted further experiments using ionic liquids to encapsulate HY zeolite-supported mononuclear rhodium complexes. The objective was to modulate electron densities on rhodium centers and analyze the effect of the ionic liquid structure on their electron-donating ability. The results reveal that the structure of the cations in the ionic liquids, especially the imidazolium cations, has a relatively minor impact on the electron-donating capacity of the ionic liquids when compared to the type of anions, which have a more significant effect on the electron-donating capacity. This finding serves as a valuable reference for controlling the effect of electronic structure on supported mononuclear rhodium. Moreover, the weak effect of cations on electron-donating capacity can also be combined with the filtration properties of ionic liquids to control the catalytic performance of the catalyst. The results presented in this dissertation demonstrate the potential application of copper-based catalysts in selective hydrogenation reactions. Additionally, it highlights the promising prospects of supported mononuclear rhodium catalysts in semi-hydrogenation reactions, containing the ability to enhance mononuclear rhodium's resistance to sintering under reducing conditions, the capacity to modulate catalytic performance for semi-hydrogenation through the support's electron-donor capacity and the potential use of ionic liquids for modifying mononuclear rhodium catalysts.
A machine learning model to guide the synthesis of supported palladium catalysts
Supported Pd catalysts are widely used in petrochemical, pharmaceutical and automotive industries. While Pd catalysts appear as a great opportunity to achieve extraordinary performances, the high cost of these materials makes it crucial to investigate efficient ways to design the catalysts. It is known that these catalysts widely preferred in structure-sensitive reactions. The structure-sensitive nature of these reactions creates an opportunity for researchers to achieve better performances via tuning the Pd particle size. Metal particle size is highly linked to the synthesis conditions, but the effects of synthesis condition parameters are hard to define due to high number of variables and the complexity of procedures. In this thesis, the relation between metal particle size and synthesis conditions was investigated by using machine learning (ML) and high activity of supported Pd catalyst was studied. In the first part of this thesis, methodology for both computational and experimental studies were introduced. Initially, the studies on supported Pd catalysts published in 2000-2023 were collected from literature. The collected data contains 1543 data points in total and 1322 of them used in the model. Synthesis method, metal loading, support, support surface area (SSA), precursor, solvent, solvent pH, support point zero charge (PZC), and support calcination/calcination/ reduction conditions were selected as the catalyst preparation parameters. Dispersion and Pd particle size were collected and used as the target variables. In pre-analysis of the data, relations between the variables, their individual relation with target variables and the preferences in literature were discussed. In the second part, dispersion and size predictive models developed via random forest regression model with 5-fold cross-validation in RStudio were introduced. The model enables to decide synthesis conditions to achieve desired metal dispersion or particle size. After the predictive models were built, a list of catalysts was synthesized, and their metal dispersion were measured via CO chemisorption experiments. The predictive models were used to predict experimental results and the precision of the models was discussed. Machine learning models presented here offer broad potential for directing the experimental efforts for the synthesis of supported Pd catalysts with desired structures for any target reaction and propose a potential to utilize the expensive Pd metal in the most efficient way. In the last section of this thesis, the promising performance of supported Pd catalysts was investigated with the selected reaction, hydrogenation of Dicyclopentadiene (DCPD) and importance of achieving high Pd dispersion in the synthesis was highlighted.
In vitro characterization of CLOCK-interacting small molecules that changes the phase of the circadian rhythm
Circadian rhythms, the intrinsic 24-hour biological cycles, govern a myriad of physiological processes, including sleep/wake sequences, hormonal oscillations, thermoregulation, and blood pressure fluctuations. These rhythms are predominantly conserved across a broad spectrum of organisms, from prokaryotic cyanobacteria to complex eukaryotes such as mammals and plants, and they enhance an organism's adaptive capabilities in relation to their environmental context. Within mammals, the regulation of these rhythmic patterns occurs on two primary levels: the organismal level, where the master clock situated in the suprachiasmatic nuclei (SCN) synchronizes the circadian clock through a delicate interplay of neuronal and hormonal cues, and the molecular level, where intricate transcriptional-translational feedback loops (TTFLs) modulate the clock mechanism. Key proteins such as CLOCK, BMAL1, CRYs, and PERs are integral to the functionality of the clock mechanism. The protein interaction begins with transcription factors CLOCK and BMAL1 forming a heterodimer that binds to the E-BOX of clock-controlled genes, such as Cry and Per, thereby initiating their transcription. Following accumulation of PERs and CRYs in the cytosol, a heterodimer forms and, upon binding with Casein Kinase Iε, translocates into the nucleus to inhibit CLOCK:BMAL1 driven transcription. Subsequent degradation of CRYs and PERs through ubiquitin-dependent proteasomal degradation lifts this transcriptional repression, triggering the commencement of a new cycle. Disruptions to this carefully orchestrated rhythm can have significant health implications, potentially precipitating severe pathologies like metabolic and cardiovascular diseases, cancer, sleep disorders, depression, and Alzheimer's disease. Consequently, the search for small molecules capable of regulating the circadian clock holds promising potential. The objective of this study is to identify novel small molecules that can engage, and subsequently modulate the activity of the CLOCK protein, utilizing structure-based drug design. Following an in silico analysis of millions of molecules, candidate drug molecules were chosen through a comprehensive evaluation process. These selected molecules were synthesized and subjected to in vitro screening to assess their circadian rhythm attributes. The resulting phase-altering small molecules were then further characterized to elucidate their potential in treating circadian rhythm-related pathologies.
Dynamically bonded cellulose nanocrystals hydrogels: Structure, rheology and fire prevention performance
Flame retardants are chemical additives incorporated into materials to reduce their flammability and slow down the spread of fire. Conventional flame retardants such as, halogenated compounds and organophosphates have been utilized for a long period. While effective in enhancing fire safety, some of these traditional materials are proven to pose a risk to the health and environment. Sustainable alternatives are much needed to provide effective fire protection while minimizing adverse impacts. Bio-based materials, like starch-based compounds or cellulose derivatives, hold significant potential as sustainable options due to their biodegradability and lack of toxicity. Essentially, cellulose nanocrystals (CNCs) stand out for their superior strength, modulus, surface area, and liquid crystalline characteristics compared to bulk cellulose, and most importantly, their modifiability enables them to be utilized in innovative applications in material science. Hydrogels have a significant promise to be used as a green flame-retardant coating, considering their ability to absorb and retain water within their intricate structure, which is a characteristic that plays a pivotal role in reducing the flammability of materials. Dynamically bonded hydrogels are characterized by reversible physical interactions and offer key advantages. Their self-healing ability, shear-thinning behavior for easy processing, and responsiveness to external stimuli make them ideal for various applications. The versatility in design, reduced stiffness in the relaxed state, and biocompatibility highlight their potential for innovative and tailored solutions in diverse fields. Boron-based compounds favor intumescent systems, notably, they synergize effectively with cellulosic substances and improve their flame-retardant characteristics by promoting charring. In this study, we aimed to create a dynamic hydrogel network between CNCs by crosslinking with borax. Utilizing borax as a crosslinker yielded dual effects: it facilitated the dynamic network formation through boron-ester bonds and enhanced flame-retardant properties when combined with CNCs. First, we elucidated the gelation behavior of CNC-borax hydrogel network by varying the concentrations of CNC and borax separately. Therefore, the concentration dependency of morphological and viscoelastic properties was unraveled for both borax and CNC variables. We found that, both the CNC and borax play a pivotal role on improving the stiffness of the gels in different principles. Borax presence is essential to trigger CNC gelation in low concentrations by facilitating the boron-ester bond formation, which attributes dynamic properties to the network with remarkable self-healing properties. Higher borax concentrations in the gel were characterized by the presence of undissolved borax crystals in polarized optical microscopy images. Therefore, the composition of these gels involved both the borate ions originated from dissolved borax, and the undissolved borax crystals in different sizes. On the other hand, CNCs played an important role in improving the stiffness of the network owing to their ability to form self-supported networks through intermolecular interactions and H-bonding. The ease of applicability of gels were assessed by spreading on pine wood surface. The gels with higher borax content provided better control and distribution, while weaker gels tend to drip. These findings provided us valuable insight into the development of effective, readily applicable, and sustainable hydrogel materials. Secondly, we assessed the performance of flame-retardant coatings on the flammability of pine wood substrates. Similar to the previous study, the contribution of both components to the flame-retardant properties were also evaluated by varying their concentrations. Fire test results revealed that, CNC-Borax hydrogel coating significantly improved the flammability of the wood substrate. Specifically, hydrogels containing both undissolved and dissolved borax parts exhibited remarkable fire prevention performance. This was due to the harmonious work of boric acid and borax; the smoldering and glowing behavior was controlled by boric acid, while the prevention of flame was ensured by borax. In combination, they promoted the glassy and robust char layer formation with high thermal stability and shielded the flammable wood substrate. As a result, hydrogel coating notably delayed the ignition time while accelerating the flameout time. Limiting oxygen index (LOI) and fire performance index (FPI) of the hydrogel coated woods were drastically increased. Examining the sustained effectiveness of the gels post-application is another vital aspect to consider in flame-retardancy applications. Therefore, a second fire case was simulated by reigniting the samples to evaluate the effectiveness of the remaining film residue from the first application of the gels. The extended fire protection effect demonstrated its efficacy in preventing damage to the integrity of the substances. The CNC-Borax hydrogels have demonstrated considerable promise as effective solutions for flame retardancy applications. Their effectiveness in these scenarios opens new avenues for the development of environmentally conscious flame retardants, particularly relevant for combatting forest fires or wildfire situations. Given the prevalence of wildfires in extremely hot and dry climates, where cellulosic materials like wood and grass serve as combustible fuel, ensuring the efficiency of CNC-Borax gels after the drying process becomes crucial. This prolonged effectiveness makes them specifically well-suited to use in challenging situations, contributing to enhanced fire protection and safety measures.
Effect of polymer architecture on structure and dynamics of polymer nanocomposites
Polymer nanocomposites (PNCs) possess exceptional physical properties that make them highly desirable for various applications. While the investigation of these systems has predominantly focused on linear polymer chains, the influence of polymer matrix architecture on local dynamics, bulk rheology, and nanoparticle (NP) motion remains largely unexplored. In the first phase of this research, we utilized quasi-elastic neutron scattering, bulk rheology, and X-ray photon correlation spectroscopy to examine nanocomposites comprising spherical silica nanoparticles well dispersed in poly(ethylene oxide) matrices with distinct architectures (linear, stars, and hyperbranched). Our findings demonstrate a profound alteration in the mechanical reinforcement of nanocomposites with nonlinear polymers, surpassing conventional counterparts with linear polymers by orders of magnitude. The pivotal roles of polymer compactness and interpenetrability in determining bulk rheology were identified. Moreover, at the microscopic level, the average segmental dynamics were significantly impeded by attractive NPs in matrices with a high degree of branching, contrasting the negligible effect observed in linear polymer matrices at equivalent NP loadings. Additionally, nanoscale dynamics in compact nonlinear matrices exhibited strong decoupling from bulk viscoelasticity, enabling rapid relaxation even at approximately 30% by volume. In PNCs, the outstanding rheological performance is largely attributed to a significant fraction of interfacial polymers. Nevertheless, strategies to control the structure and dynamics of interfacial polymers have been limited. In the second phase, we propose a facile approach centered on varying the macromolecular architecture of interfacial polymers. Our results demonstrate that altering the topology of bound polymers from linear to star and hyperbranched structures effectively modifies polymer-NP interactions and chain interpenetration in interphases, without altering the type, molecular weight of the polymer, or NP surface chemistry in attractive PNCs. The dependency of dispersion and rheological behavior of PNCs on the functionality and arm length of polymers at interfaces is evident. Distinct polymer chain architectures lead to fundamentally different rheological responses and internal dynamics, offering opportunities to rationalize advanced thermoplastic nanocomposites with tunable mechanical behavior. In the third phase, we investigated three model poly(methyl methacrylate) (PMMA) polymers with linear, bottlebrush, and star architectures, each having the same total molar mass, in their neat form and as nanocomposites with well-dispersed silica nanoparticles. Linear chains formed an entangled polymer network, while branched bottlebrush and star chains exhibited a viscoelastic response without a rubbery entanglement plateau and a weak arm relaxation regime between Rouse and terminal flow, akin to other branched polymers. The addition of nanoparticles primarily influenced the terminal relaxation regime, hindering the overall chain motion in the presence of attractive nanoparticles. Broadband dielectric spectroscopy results revealed over 10 times slower segmental relaxation for star homopolymers and a slowdown in the α-relaxation process for all three architectures in their composite form. In the fourth phase, utilizing small angle light scattering (SALS), UV-vis spectroscopy, and rheology, we examined the effect of polymer chain architecture on the dispersion and viscosity of polymer-NP solutions. Our findings suggest that, compared to linear polymers with the same molecular weight, star polymers with short arms create a more compact polymer layer at the interface. Moreover, the viscosity of solutions exhibited a direct response to additional branching, with up to a sixfold higher viscosity observed in solutions with linear polymers compared to those with hyperbranched polymers. In the fifth and final phase, we investigated the structure and dynamics of NPs in solutions with different polymer architectures using X-ray photon correlation spectroscopy (XPCS) and rheology. A direct relationship was observed between increasing branching of polymer chains and disordering in the structure of NPs. Hydroxyl end groups in highly branched architectures protonated the surface of silica NPs, resulting in decreased effective surface charge and weakened electrostatic repulsion among NPs. This led to a decrease in excluded volume between NPs and a broader spacing range among them. Furthermore, increasing branching directly affected the diffusivity of NPs in the medium, where NP motion was subdiffusive in solutions with less-branched linear and four-armed star polymers and diffusive in solutions with hyperbranched chains. In conclusion, this comprehensive study provides critical insights into the intricate interplay between polymer architecture and the physical properties of nanocomposites. It establishes a foundation for designing tailored materials with enhanced performance and tunable mechanical behavior across diverse applications.
Optimization of poly(lactic-co-glycolic acid) particle properties for biomedical applications
In addressing modern health challenges, the strategic use of biomaterials stands at the forefront of scientific innovation and progression. These materials have become essential tools in the search for treatments and cures against many diseases. Poly(lactic- co-glycolic acid) (PLGA) is one such material that has exceptional biocompatibility and versatility and has been approved by the Food and Drug Administration (FDA) in terms of utilization in biomedical applications. Different health applications require particles with specific sizes and properties; for instance, drug delivery applications generally require nano-sized particles while cell-based therapies rely mostly on micro-sized particles to house the cells within. Consequently, the tailoring of PLGA particles for specific purposes requires a comprehensive optimization study. Here, by using a computational approach, the effect of individual synthesis parameters in the final size on the PLGA particles has been identified. According to the effects of these individual parameters, an artificial neural network (ANN) model is developed to predict the particle size accurately and robustly. Development of an ANN model required its optimization, in terms of model performance. Since the training data of the model comes from experimental results, the model lacked a high number of data points. Thus, any kind of generated model carries a risk of overfitting, where the model specializes in predicting the training set and performs poorly on presented unknown data. To be able to understand whether a model overfits, several cross-validation methods like leave-one-out cross-validation (LOOCV) and leave-p-out cross-validation (LPOCV) have been utilized. As a result, the Bayesian Regularization (BR) backpropagation method with 5-1-5 multiple hidden layer size network architecture was found to be robustly predicting the final size with any given parameters. Utilizing this ANN model, particles with desired sizes have been synthesized and used in several applications such as i- macroporous PLGA particles that carry cells and ii- PLGA particles that form an aggregate with spheroids to improve the viability. The first application required extensive optimization regarding the pore size of the particles which is essential for cell penetration. As a result, NIH-3T3 fibroblast cells were successfully pushed inside the PLGA particles via centrifugal cell immobilization without having any negative impacts on the viability of these cells. As for the second application, negatively charged PLGA particles were unsuccessful in forming aggregates with the fibroblast spheroids. Nevertheless, coating PLGA particles with chitosan showed promising results without impairing the viability of the cells and the release kinetics from the particles. In conclusion, this study offers a method to estimate particle size prior to synthesis and opens the way for further innovations in biomedical applications, leading to the development of more effective and customized therapeutic solutions.
Combining molecular simulations and machine learning to unlock gas separation performances of MOFs and MOF-based composites
Metal-organic frameworks (MOFs) have become a well-known class of porous materials for solving energy-related gas separation challenges thanks to their high porosities, large surface areas, and easy-to-modify structural properties. Due to the enormous number of synthesized MOFs (>125,000), molecular simulation methods play an important role in assessing the gas separation performances of MOFs and MOF-based composites. In this thesis, we combined high-throughput computational screening (HTCS) and machine learning (ML) approaches to assess the performances of a very large number and type of MOFs and MOF-based composite materials for a variety of gas separation applications. In the first part, we focused on air separation and performed grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations to compute O2 and N2 permeabilities and O2/N2 selectivities of 5629 MOF membranes and 78,806 different types of MOF/polymer mixed matrix membranes (MMMs). Our results showed that many MOF membranes exceed the upper bound established for traditional polymer membranes thanks to the high permeabilities and/or selectivities of MOFs. In the second part, we focused on 11 different gas separation applications to explore 5599 MOF membranes and >180,000 different types of MOF/polymer MMMs. Results showed that many MOFs offer a great opportunity for making MMM applications by improving both the permeability and selectivity of polymers. Since creating all this molecular simulation data needs computationally demanding calculations and analyzing this very large dataset is not practical, in the third part of the thesis, we utilized machine learning (ML) to significantly accelerate the assessment of MOF membranes and MOF/polymer MMMs for six different gas separation applications. Results showed that the ML models that we trained based on the GCMC and MD simulation data accurately predict the adsorption and diffusion properties of He, H2, N2, and CH4 gases in MOFs. In the fourth part, we developed ML models to study 1000 different types of MOFs and ionic liquid (IL)/MOF composites as adsorbents for flue gas separation. The most important features that affect the CO2/N2 selectivity of IL/MOF composites were extracted using the ML results and utilized to computationally generate a new IL/MOF composite, [BMIM][BF4]/UiO-66. Experimentally measured CO2/N2 selectivity of this new composite matched well with the ML-predicted one. In the last part, we focused on water adsorption and performed GCMC and density functional theory (DFT) calculations to explore the effect of framework flexibility on the water adsorption properties of a MOF. Results emphasized the significance of considering the structural flexibility of the MOF for water adsorption. The results of this thesis will provide molecular-level understanding of the gas adsorption and diffusion behavior of MOFs and facilitate the design of new MOFs and MOF-based composites for various gas separation applications at reduced time and cost.
Cellulose nanocrystals: Extraction from wood waste and their thermo-responsive composites with block copolymer vesicles
An essential goal for achieving a circular economy is the reuse of different waste types. Wood waste is a growing issue since it comprises a significant portion of total solid waste. The valorization of wood has many benefits over current waste management methods, including incineration and landfilling, and it has the potential to achieve a more sustainable environment. Cellulose nanocrystal (CNC) is a high-value and promising sustainable material characterized by its high surface area, aspect ratio, biocompatibility, abundance, and outstanding mechanical properties. These nanoparticles have been used in a wide range of applications, from rheology modifiers to biosensors, hydrogels, energy storage, flocculants, and drug delivery systems. However, CNC extraction starting from bulk wood chips and the resulting hardwood and softwood CNC properties have not been investigated in detail. Also, since CNCs lack stimuli-responsiveness, turning CNCs into smart systems remains challenging. In the first part of this thesis, CNCs were isolated from pine (softwood) and oak (hardwood) wood chips obtained from a waste stream of a panel production facility. The waste wood chips were alkali-treated and bleached to remove non-cellulosic content. The resulting nanocrystals were compared in terms of morphology and physicochemical properties. Although they underwent the same chemical process, CNCs from hardwood and softwood wastes displayed different properties, especially in size, zeta potential, and crystallinity. The results showed that hardwood CNC has higher crystallinity compared to softwood CNC. Moreover, hardwood CNCs have smaller hydrodynamic diameters with greater zeta potential than softwood CNCs. In the second part, we aimed to create a thermo-responsive nanocomposite system constructed with wood-derived CNCs and temperature-responsive reversible polymersome-forming PEO-PPO-PEO block copolymers, specifically Pluronic L121. The morphology, phase behavior, and mechanical properties of the composite gels were investigated in detail. Two different CNC concentrations (4 wt. % and 5 %) were studied by varying the L121 concentration from 1% to 20% to understand the effect of unimers and polymersomes on the CNC network. The results showed that dilute and high concentrations of Pluronic L121 have different effects on the CNC gelation behavior. Adding Pluronic L121 up to 5% softened the composite below the transition temperature. The composite became stronger with L121 addition from 10 to 20% and a gel network was obtained above the transition temperature. Interestingly, the CNC hydrogel network became more deformable and resistant to microstructural breakdown at large strains due to the inclusion of large vesicles. The results demonstrate that CNC-Pluronic L121 hydrogels showed thermo-reversible rheological behavior, making them potential candidates for developing stimuli-responsive functional materials for biomedical applications.
The Catalyst Development for Space Propulsion Applications
Monopropellant rocket systems work by generating a combination of high-pressure hot gases through an exothermic decomposition reaction of a propellant compound. Afterward, the products are accelerated through a converging–diverging nozzle to provide the desired thrust. These systems can generate thrust in the range of 0.1 to 500 N, with a moderate specific impulse of up to 250 s. In small missiles and satellite engines that require low thrust, monopropellant engines are preferred. This thermal decomposition process has a large activation energy, meaning that the compound does not decompose spontaneously at ambient temperatures and must be suitably heated. A special catalyst can lower the activation threshold to speed up the reaction. As a monopropellant, hydrazine (N2H4) is most often used because the successful development of the Shell S405 catalyst provides high efficiency, with a specific impulse (Isp) value of 237 s. However, its carcinogenic and toxic nature causes transportation and handling problems. Therefore, green alternatives are receiving increasing attention nowadays. Their lower toxicity and safety precautions result in lower costs for manufacturing, handling, and storage. One of the most promising choices among the green monopropellants is highly concentrated hydrogen peroxide (H2O2). Although highly concentrated hydrogen peroxide has a lower Isp value (179s) than hydrazine, H2O2 is ecologically friendly because its decomposition products include oxygen and steam. The development of an appropriate catalyst and a reliable system for green monopropellants are key aspects of catalytic decomposition in thruster systems. The main aim of this thesis is to demonstrate the utilization of an effective and reliable monopropellant thruster. As a chemical approach, the importance of the support and active material of the catalyst is studied, and their properties are analyzed with characterization techniques and reaction kinetic experiments. Then, they are used in the monopropellant thruster as an engineering approach; the catalyst bed is analyzed based on temperature and pressure changes over time during decomposition in the thruster system. In this thesis, the aluminum oxide-supported manganese oxide catalyst is synthesized and tested. A microcrystalline cellulose-templated alumina catalyst support was prepared, and a parametric study was conducted to determine the optimum preparation conditions. The chosen micro cellulose-templated aluminum oxide was 15 wt% of micro cellulose to alumina at a calcination temperature of 900 °C. Macropores were formed on the MnOx/alumina catalyst surface. Thanks to these pores, hot gas was allowed to discharge from the pores without breaking the catalyst. In addition, by increasing the calcination temperature, catalysts with a longer life have been designed for thruster operations. Therefore, the unstable performance of thrusters caused by cracking of the pellet catalyst can be solved by adjusting the porosity of the catalyst. The primary goal of previous studies was to explore methods to improve the mechanical stability of the catalyst within the thruster, which resulted in increased strength but only moderate catalytic activity. As a secondary aim, efforts have now shifted toward enhancing the catalyst's reaction activity while maintaining its improved mechanical properties. The research continued by improving the loading of active material through double impregnation techniques and intermediate heating at 325 °C. According to the BET analysis, high-concentration precursor solutions cause more material to accumulate on the support, leading to pore blockage and a decrease in specific surface area. This observation was also correlated with the diminution of visible pores in SEM images. Increasing the molarity of the manganese precursor and adjusting the synthesis process resulted in significant improvements in catalyst performance, as evidenced by higher MnOx loading and enhanced apparent reaction rates in kinetic and thruster tests at first however, after a while, all catalysts show similar performance. Beyond catalyst synthesis, the study explored various factors affecting thruster performance, including catalyst mass, particle size, the concentration of hydrogen peroxide and the amount of stabilizer in hydrogen peroxide and radiation in space environments. The decomposition of H2O2 using a manganese oxide catalyst exhibits first-order kinetics; however, external mass transfer limitations affect the observed reaction rate. Testing various catalyst amounts (250 mg to 1500 mg) demonstrated a proportional increase in reaction rate, emphasizing the significance of catalyst concentration. The particle size of the catalyst was also crucial in the catalytic bed of the thruster because size can also cause flow instabilities during thruster tests. According to the test results, larger particles (700 µm) resulted in higher pressure drops compared to smaller particles (300 µm), which provided more stable performance with lower pressure loss and greater thrust, indicating their superior effectiveness in thruster applications. Moreover, the presence of stabilizers, such as sodium pyrophosphate, was found to negatively impact catalyst efficiency by inhibiting H2O2 decomposition. Tests with lower-phosphate peroxide grades showed improved catalytic activity, emphasizing the importance of propellant purity in thruster performance. Also, from 87.5wt to 78wt% hydrogen peroxide concentrations are examined, and the catalysts are shown to breakdown them efficiently. Although lower decomposition temperatures resulted in minor thrust losses, these are unlikely to impair the overall performance of the propulsion system. Experiments also examined the effects of cold starts and preheating on thruster operation. Preheating the catalyst bed to 150 °C was found to provide optimum thrust performance by reducing thrust response times. Subsequently, gamma radiation was applied to both hydrogen peroxide and the catalyst to examine the effects of space radiation exposure. Ionizing radiation can cause hydrogen peroxide to decompose into oxygen, reducing its concentration and impacting thruster performance. However, tests on 88% hydrogen peroxide exposed to 158.4 Gray showed no significant change in concentration. While radiation can degrade stabilizers such as sodium pyrophosphate, the changes were minimal. Catalysts were also tested for radiation effects, showing some shifts in manganese oxidation states (increase in Mn²⁺, decrease in Mn⁴⁺); however, the catalyst's structure and functionality remained stable in thruster and kinetic tests, with only a slight reduction in apparent reaction rates. In summary, this thesis presents a comprehensive study of catalyst development for hydrogen peroxide decomposition in monopropellant thrusters. The findings demonstrate significant advancements in catalyst stability, efficiency, and performance, providing valuable insights for future applications in space propulsion systems
Understanding toxic gas adsorption in MOFs via high-throughput computational screening and machine learing
Determining the best metal organic frameworks (MOFs) for a specific application is getting harder while thousands of them have been experimentally synthesized and hundreds of thousands of them have been computationally generated. Increasing demand for more efficient gas separation systems also brings about a more complicated, time consuming, and dangerous experiment environment. In this thesis, we focused on high-throughput computational screening of hybrid (QMOF) and experimental (CoRE MOF) MOF data sets for several separation and storage applications to overcome these limitations. Gas uptakes of MOFs were computed configurational biased Monte Carlo (CBMC), and grand canonical Monte Carlo (GCMC) simulations, and self-diffusivity of molecules in MOF structures was determined using molecular dynamics (MD) simulations at several conditions. Various adsorbent performance evaluation metrics, such as selectivity, working capacity, adsorbent performance score, and percent regenerability, were used to identify the best adsorbent candidates. In the first part, we examined volatile organic compounds (VOCs) capture from air, and our results showed that more than one-third of our MOFs have higher (butane) C4H10 selectivities than commercial zeolite MFI. The top five MOFs have C4H10 selectivities between 6.3"×" 103-9"×" 103 (3.8"×" 103-5"×" 103) at 1 bar (10 bar). Analysis of the structure-performance relations demonstrated that MOFs with mediocre porosity (0.4-0.6) and narrow pore sizes (6-9 Å) tend to have high C4H10 selectivities. Radial distribution function analyses of the top materials revealed that C4H10 molecules predominantly localize near the organic linkers of the MOFs. In the second part, we focused on (propane) C3H8 capture with MOFs and our results demonstrate that (vacuum-temperature swing adsorption) VTSA is the most effective process for many MOFs offering high regenerability (>90%), exceptional C3H8 selectivity (>7×103), and high C2H6+ C3H8 selectivity (>100). Top-performing MOFs are characterized by narrow pores (<10 Å), low porosities (<0.7), aromatic ring linkers, and alumina or zinc nodes. These MOFs outperform commercial zeolite MFI for air separation and surpass several commercial MOFs for natural gas streams. In the last two parts, we showed that CO uptakes (self-diffusivities) of CoRE MOFs and hMOFs range from 0.02 to 2.28 mol/kg (1.1×10-6 to 2.5×10-3 cm2/s) and 0.45 to 3.06 mol/kg (2.6×10-7 to 3.6×10-3 cm2/s), respectively, at 1 bar and 298 K. At low pressures (0.1-1 bar), Henry's constant for CO (KH,CO) is the primary determinant of performance, whereas at higher pressure (10 bar), structural factors like surface area (Sacc) and porosity (ϕ) become more significant. For CO diffusivity, results reveal that heat of adsorption (Q0st,CO) is the most important feature. Our analysis identified the top-performing adsorbents, revealing that MOFs with the highest CO uptakes typically feature narrow pores (4.5-7.2 Å), aromatic rings, carboxylic acids, halogens, and rare metals such as Li. Contrary to the uptake results, MOFs with high PLD values (>12 Å) and larger pore volume (>1.2 cm3/g) tend to have higher CO diffusivity values, DCO >10-4 cm2/s. Our results demonstrate that the utilization of machine learning algorithms and HTCS methodology with molecular simulation techniques exhibit reliable and rapid guidance to feature experimental and computational studies.
Machine learning based optimization of thermal cracking furnace in a visbreaker unit
Machine learning (ML) is a branch of artificial intelligence that leverages advanced algorithms to autonomously learn from large datasets, recognize patterns, and make accurate predictions with minimal human intervention. In the oil and gas industry, where refining operations are highly complex, ML-based approaches offer significant advantages over conventional mechanistic models, which often struggle to capture the dynamic nature of refinery processes. One critical unit in a refinery is the Visbreaker, which plays a key role in reducing the production of residual oil during crude oil distillation while increasing the yield of valuable middle distillates, such as naphtha and fuel oil. It operates by thermally breaking down large hydrocarbon molecules in residual oils through high-temperature heating in a furnace, generating lighter hydrocarbons like LPG and gasoline. However, managing the Visbreaker presents challenges, particularly coking of furnace tubes when processing heavy residual feeds. This coke buildup can lead to frequent shutdowns for maintenance, disrupting operations and reducing efficiency. This thesis focuses on ML-based optimization models for refinery processes, particularly the Visbreaker, utilizing real-time sensor data for enhanced decision-making. Machine learning algorithms, including Decision Trees, Random Forests, and Artificial Neural Networks (ANNs), were employed to predict critical parameters such as furnace coil temperatures. The robustness of these models was validated using 500 days of historical data. Additionally, ML models were developed to estimate the remaining operational time before a shutdown. Building upon these predictive models, an AI-driven optimization framework, using an ANN-based genetic algorithm (ANN-GA), was developed to recommend optimal operating conditions. By integrating ML models with real-time data, this study enables proactive decision-making and optimization. Operators can anticipate operational bottlenecks like coking and adjust parameters in advance, ensuring the Visbreaker operates efficiently while minimizing risks. ML-based approaches thus provide a robust solution for managing refinery complexities, enhancing productivity, and improving sustainability.
Physics informed machine learning based optimization for chemical engineering applications
Neural networks offer a promising alternative to first-principles models in complex industrial systems, but their effectiveness is often limited by noisy data, scarce samples, and the computational demands of training and optimization. This work addresses these challenges by integrating physics-informed structures, transfer learning, and advanced training strategies for process modeling and optimization. Physics-informed neural networks were employed in surrogate optimization problems, where case studies—from a simple blending process to a crude oil distillation unit—showed consistent improvements in solution quality and optimization reliability. The integration of physics-informed training with piecewise linear approximations reduced CPU times, while predominantly achieving global optima. PINN training was also extended to recurrent neural networks for modeling dynamic parameters in a refinery wastewater treatment plant. Physics-informed LSTM and GRU models improved prediction accuracy, with the LSTM reducing MSE by over 20% and the GRU cutting COD prediction errors by 11% compared to standard models, while eliminating the need for fine-tuning during online validation. Transfer learning techniques were applied to address noisy and limited data conditions, where a hybrid physics-informed transfer learning model outperformed baseline models, reducing test and validation MSE by up to 27% and 59%, respectively. Finally, the semi-continuous specially ordered set based training (SOSX) algorithm was adapted for neural network training, achieving near-zero training error across various network configurations while significantly reducing CPU time compared to traditional MIP-based methods. These results highlight SOSX's scalability and applicability to large neural network configurations. Collectively, these approaches demonstrate how combining physics-based knowledge, architectural innovations, and optimization techniques can enhance the predictive performance and computational efficiency of neural networks in industrial and environmental processes.
Energy management and control strategies for proton exchange membrane fuel cells in transportation
Proton Exchange Membrane Fuel Cells (PEMFCs) are a promising technology for clean energy conversion, particularly in transportation applications, but their efficient operation requires addressing multiple challenges including water and thermal management, power allocation under degradation, and hybrid energy system control. This thesis is organized into five chapters, including an introduction and conclusion, with three core chapters focusing on key aspects of PEMFC system optimization. The first technical chapter investigates water and thermal management in PEMFC stacks, proposing a novel control framework that integrates a supervisory Model Predictive Controller (MPC) with local PID controllers for humidity regulation. Introducing the concept of Feasible Humidity Plots (FHP), this approach defines operational bounds for anode and cathode relative humidities, enabling robust water balance and temperature control. Simulation results demonstrate effective setpoint tracking and disturbance rejection, with resilience to model uncertainties and fuel cell aging. Building on this foundation, the second chapter develops a real-time optimization strategy for power sharing between two PEMFC stacks, accounting for degradation effects characterized by a time-varying electron transfer coefficient estimated via RLS-Kalman filtering. Incorporating hydrogen crossover impacts, the proposed method optimizes efficiency and hydrogen consumption, outperforming conventional equal distribution and daisy chain strategies. The framework's adaptability to multiple stacks and objective functions is validated through extensive simulations. The third core chapter addresses energy management in Fuel Cell Hybrid Electric Vehicles (FCHEVs), integrating multiple PEMFC stacks with a battery. A two-layer hierarchical control scheme is introduced, combining a Dual-Rate Economic MPC for splitting power between the slow fuel cell system and fast battery, with a secondary optimizer for degradation-aware stack power allocation. Simulation results confirm significant reductions in hydrogen consumption compared to benchmark strategies. Collectively, the results presented throughout the thesis advance the state-of-the-art in PEMFC system control and optimization, promoting sustainable and efficient fuel cell applications in both industrial and transportation sectors.
Leveraging molecular simulations and machine learning to assess gas adsorption and separation performances of MOF, COFs, IL/MOF, and IL/COF composites
Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), known for their high surface areas, high thermal and chemical stabilities and tunable properties, have emerged as promising candidates for adsorption- and membrane-based gas separations. Given the vast number of synthesized MOFs (>128000) and over a million computer-generated, hypothetical MOFs (hMOFs), computational methods are essential for evaluating the gas separation performance of these materials. This dissertation explores the gas adsorption and separation performances of MOFs, COFs, and their composites with polymers and ionic liquids (ILs) for applications, including CO2 capture, natural gas purification, and air separation. A multi-scale computational approach integrating molecular simulations, COSMO-RS calculations, density functional theory (DFT) calculations, and machine learning (ML) is employed to efficiently examine large material databases and uncover structure-property relationships. In the first part, we focused on the CH4/N2 separation performances of a total of 5034 MOFs and COFs, and several IL/MOF, MOF/polymer, and COF/polymer composites by performing grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. Our results showed that IL incorporation significantly enhances CH4/N2 selectivity, and both MOF and COF membranes outperform conventional polymers. In the second part, we extended this approach to IL/COF composites for CO2/N2 separation, revealing significantly improved selectivities, and CO2 permeabilities surpassing those of polymer and zeolite membranes. In the third part, we developed ML models trained on simulated CH4 and N2 adsorption data of 4612 synthesized MOFs and tested the transferability of these models on 98601 hMOFs. Our results revealed that many hMOFs exhibited high CH4 selectivities and working capacities while several outperforming synthesized MOFs. The fourth part involves an ML-integrated workflow to investigate 1322 different types of IL/ZIF-8 composites, covering the largest variety of ILs studied to date (8 cations and 35 anions) at various IL loadings. We performed GCMC simulations to compute CO2, CH4, and N2 adsorption properties of these composites, and the resulting simulated data were used to train ML models capable of predicting gas uptake in any IL/ZIF-8 composite based on the chemical and structural features of the ILs. Our results showed the high accuracy of the models through the comparison of ML predictions with experimental and simulation data. The last part focused on the development of ML models for CO2, O2, and N2 adsorption data of synthesized MOFs and transferring the information gained from these models to four different hMOF databases. As a result, CO2, O2, N2 adsorption and CO2/N2 and O2/N2 separation performances of ~130000 structures were predicted, which offers valuable insights for materials discovery. The results of this thesis will provide molecular-level understanding of gas adsorption and diffusion in MOFs and enable the rational design of novel MOFs and MOF-based composites for gas separation applications.
Systematic evaluation of photoinitiating systems for engineering tunable gelma hydrogels
Over the years, biomaterials have become central to the development of advanced biomedical therapies, offering customizable platforms for tissue engineering, regenerative medicine, and controlled drug delivery. Among these, gelatin, a denatured derivative of collagen, has emerged as a highly promising candidate owing to its inherent biocompatibility, biodegradability, and cell-interactive features that facilitate cell adhesion, migration, and remodeling. However, native gelatin suffers from lack of tunability, poor mechanical strength and rapid dissolution at physiological conditions, limiting its use in tissue engineering applications without further modification. To overcome these drawbacks, gelatin methacryloyl (GelMA) was developed by introducing methacryloyl groups onto gelatin backbones, thereby enabling light-mediated covalent cross-linking and yielding hydrogels with adjustable mechanical properties and high cytocompatibility, making GelMA an exceptionally versatile biomaterial for wide range of biomedical applications. While GelMA's photo-crosslinkability offers precise spatiotemporal control, its final properties are highly dependent on the photoinitiator system and its concentration. Despite the broad use of both UV- and visible-light-sensitive initiators, the field still lacks rigorous side-by-side evaluations under standardized conditions, limiting our understanding of how initiator chemistry shapes hydrogel performance. To address this, the present thesis study systematically compares the effects of three commonly used systems Eosin Y (EY), Lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP), and Ruthenium/Sodium Persulfate (Ru/SPS), on the mechanical, degradation, swelling, and cytocompatibility properties of 5% and 10% (w/v) GelMA hydrogels. A comprehensive experimental workflow was employed, including real-time rheometry, enzymatic degradation studies, swelling and porosity analysis, and NIH-3T3 fibroblast viability assays. GelMA synthesis yielded a high degree of methacrylation (~81%), ensuring consistent cross-linking performance. The study identified system-specific threshold photoinitiator concentrations beyond which mechanical or biological performance was compromised, thereby elucidating critical trade-offs among stiffness, stability, and cytocompatibility. Distinct polymerization kinetics and structure–function relationships were revealed: LAP enabled rapid UV crosslinking with broad cytocompatibility, EY supported uniform visible-light-mediated networks with controlled gelation, and Ru/SPS, despite cytotoxicity at elevated concentrations, provided a degradable and highly swellable scaffold ideal for transient biomedical applications. Collectively, this work provides an essential resource for rational photoinitiator selection and hydrogel design, offering actionable insights for the engineering of GelMA-based constructs tailored to specific biomedical applications.
Entegre moleküler simülasyonlar ve makine öğrenmesi yoluyla MOF'ların SF₆/N₂ ve CH₄/H₂ ayırma potansiyelinin ortaya çıkarılması
Metal-organic frameworks (MOFs), thanks to their large surface areas, high porosities, and tunable structural properties, have become strong candidates for adsorption and membrane-based gas separation applications. However, the ever-growing number of both experimentally synthesized MOFs (>128000) and hypothetical MOFs (hMOFs) that are generated computationally (>1 million) presents a major bottleneck for systematic performance evaluation through conventional methods. This thesis employs a multi-scale computational strategy that combines molecular simulations and machine learning (ML) to investigate the gas adsorption and separation properties of MOFs across large structural databases for SF₆/N₂ and CH₄/H₂ separations. In the first part, grand canonical Monte Carlo (GCMC) simulations were conducted on over 25000 synthesized and hypothetical MOFs to evaluate their SF₆/N₂ adsorption and separation performances. Simulation data for synthesized MOFs were then used to train ML models, which were transferred to hMOFs to predict gas uptakes, selectivities, working capacities, adsorbent performance scores and regenerabilities. The analysis revealed the key structural and chemical features responsible for the best performance. In the second part, a similar approach was applied to assess CH₄/H₂ separation performances of 126605 MOFs. Adsorption data obtained from GCMC simulations were used to develop predictive ML models based on chemical, structural, and energetic descriptors. These models were transferred to hMOFs to rapidly identify the promising candidates with superior CH₄ selectivity. The third part of the thesis focused on diffusion-based membrane separation, in which molecular dynamics (MD) simulations were performed to compute CH₄ and H₂ diffusivities of MOFs. ML models trained on simulation results successfully predicted diffusivity values across the entire database using easily computable descriptors. These predictions were further used to assess membrane performance in terms of selectivity and permeability, and structure-performance relationships were revealed via molecular fingerprinting analysis. The findings of this thesis demonstrate that integrating molecular simulations with ML enables high-throughput screening of MOFs for gas separation applications. The developed models and insights provide a foundation for accelerating the discovery and rational design of next-generation MOF-based adsorbents and membranes.
Multidisciplinary approaches in developing immune-evasive β-cell replacement therapies for type 1 diabetes
Type 1 Diabetes (T1D) is an autoimmune disorder characterized by the destruction of insulin producing pancreatic β-cells, leading to chronic hyperglycemia and associated severe complications. Current therapeutic strategies, including insulin therapy and islet transplantation, are limited by incomplete glycemic control, donor scarcity, and immune rejection. This thesis aims to overcome these challenges by integrating biomaterials, genetic engineering, and computational modeling to develop innovative β-cell replacement therapies. Firstly, Gelatin Methacryloyl (GelMA) hydrogels were optimized using visible-light photocrosslinking techniques. An Artificial Neural Network (ANN)- based computational model was developed to predict key hydrogel properties, significantly enhancing biomaterial design efficiency and efficacy for encapsulation and transplantation of β-cells. Secondly, genetic modifications were explored using CRISPR/Cas9 technology, initially targeting single genes (RNLS and HIVEP2) implicated in immune modulation and cellular protection. Despite successful gene editing, these single-gene knockout approaches failed to protect β-cells from immune rejection, highlighting the need for more comprehensive strategies. Consequently, a multi faceted genetic approach was developed, combining knockout of major histocompatibility complex genes (β2M and CIITA) with overexpression of immunomodulatory molecules (murine H2-Kᵇ, CD47, CD55) to comprehensively evade immune detection. Overall, this thesis demonstrates that integrating computational modeling, genetic engineering, and biomaterial science provides a powerful, synergistic platform for developing next-generation cellular therapies for T1D. The methodologies established have broad translational potential for addressing immune rejection and improving graft survival, ultimately paving the way toward clinically viable, personalized regenerative medicine approaches.
Supercritical ion exchange synthesis and kinetic modeling ofselective catalytic reduction catalysts for diesel and hydrogenengine aftertreatment systems with engine control unitoriented implementation
This thesis investigates the synthesis, characterization, kinetic modeling, and control-oriented implementation of catalysts for selective catalytic reduction of NOx by ammonia (NH3-SCR) applications in advanced aftertreatment systems, including hydrogen internal combustion engines (H2-ICEs) and diesel engines. Various zeolite frameworks (SSZ-13, ZSM-5, and MOR) were ion exchanged with Copper(II)trifluoroacetylacetonate (Cu(tfa)2) using Supercritical and Aqueous Ion Exchange (SCIE and AIE) methods. Catalytic activity assessments revealed that Cu/MOR synthesized via SCIE exhibited superior NO conversion compared to AIE. Furthermore, SCIE enabled site-selective copper exchange, by varying synthesis temperature (40–80 °C) and Cu(tfa)2 concentration, tuning the distribution of ZCuOH and Z2Cu species located on the 8 membered-rings and 6 membered-rings of SSZ-13. Spectroscopic techniques (UV–Vis and ATR-FTIR) revealed that ZCuOH species dominated at high SCIE temperatures, while Z2Cu became more prevalent with increased Cu precursor concentration. The NH3-SCR performance of a commercial Cu/CHA catalyst was also evaluated under H2-ICE relevant conditions, including 175-760 ppm of NOx and NH3, 1–20% H2O, 1–14% O2, and 500 ppm H2 across 150–490 °C. NH3 uptake decreased by ~40% as H2O content increased from 1% to 20%. In Standard SCR conditions (NO/NOx=1), low-temperature NOx conversion decreased notably with increasing water content—dropping by up to 30% at 200 °C when H2O increased from 1% to 20%. However, at higher temperatures, water exerted a promoting effect: NOx conversion improved with increasing H2O and consistently exceeded 99% above 250 °C at 60,000 h⁻¹ gas hourly space velocity. In Fast SCR conditions (NO2/NOx=0.5), the impact of water was less pronounced, and high NOx conversion (>95%) was achieved even at 200 °C. Co-fed hydrogen (500 ppm) had minimal effect below 400 °C but slightly impacted high temperature NOx efficiency and N2 selectivity. To enable real-time application in Engine Control Units (ECUs), a Reduced Order Model (ROM) of the NH3-SCR process was developed. The model was calibrated using synthetic gas bench data and validated against dynamometer tests with 7.5 L (close coupled) and 41 L (underfloor) commercial SCR reactors. The ROM accurately predicted transient NO, NH3, and N2O behavior under World Harmonized Test Cycle (WHTC) conditions with less than 5% error as well as fluid/solid thermal behavior, offering a robust solution for ECU-integrated urea dosing strategies.
İnterlökin-1 reseptör antagonisti için polimer konjugasyonunun optimizasyonu: verimlilik, ayrıştırılabilirlik ve fonksiyonel değerlendirme
This thesis builds a controlled, structure-guided comparison of PEGylation strategies for IL-1 receptor antagonist (IL-1Ra). Mapping accessible residues on IL-1Ra, thiol-selective (maleimide) versus lysine-directed (NHS) coupling strategies were evaluated across the same 5/10/20 kDa ladder at matched low stoichiometry, followed by a focused stoichiometry sweep for the preferred route. A near-native IL-1Ra backbone (M143V) was generated and 15N-labeled for HSQC NMR so that structural integrity and the specific impact of PEGylation could be evaluated independently of the marketed sequence. Across 5, 10, and 20 kDa, the thiol route gave a clean, predominantly di-PEG product, whereas lysine coupling produced heterogeneous mixtures across multiple sites and substitution levels. Increasing thiol stoichiometry above a low window reduced chromatographic separability without improving useful yield, identifying 10 kDa maleimide at low equivalents as a practical operating point. The M143V variant exhibited activity comparable to anakinra; 1H–15N HSQC of the 10 kDa thiol–PEG conjugate indicated preservation of a native-like fold with localized chemical-shift changes, and cell-based assays showed that PEGylation retained maximal inhibition while shifting the dose–response to higher concentrations. Together, the work delivers a reproducible analytical–structural–functional framework for selecting developable IL-1Ra conjugates, establishing a matched head-to-head design (chemistry × size × stoichiometry), and recommending thiol-selective 10 kDa PEG at low feed to obtain a clean di-adduct that purifies readily and preserves a native-like fold.
Cof world: Computational discovery of covalent organic frameworks for gas storage and separation applications by integrating molecular simulations and machine learning
Covalent organic frameworks (COFs) have emerged as a versatile class of porous crystalline materials for adsorption- and membrane-based gas separations owing to their high surface areas, structural diversities, and good thermal and chemical stabilities. Despite the rapidly growing number of synthesized COFs, only a limited fraction has been evaluated for industrially relevant separations, and the much larger space of computer-generated, hypothetical COFs (hypoCOFs) remains largely unexplored. This dissertation describes a high-throughput, multi-scale computational framework integrating grand canonical Monte Carlo (GCMC) simulations, molecular dynamics (MD) simulations, and machine learning (ML) to quantify separation performance of the COF spectrum and to establish transferable structure-property relationships for materials discovery. In the first part, we quantified adsorption-based and membrane-based H2/CO2 separation of ~300 synthesized COFs and ~5000 hypothetical COFs using GCMC and MD simulations. The results showed that many COFs surpass conventional adsorbents in adsorption selectivity and working capacity, while offering high regenerabilities. We also discovered that many COF membranes exceed the Robeson's upper bound thanks to intrinsically high H2 permeabilities. In the second part, we modelled a six-component gas mixture to mimic natural gas purification, to quantify adsorption-based separation potentials of ~600 synthesized COFs and ~3000 hypothetical COFs. Results showed that they can outperform several traditional adsorbents, zeolites, activated carbons, and carbon nanotubes. In the third and fourth parts, we introduced ML-integrated computational screening methodologies to screen ~70000 COF and hypoCOF materials for adsorption-based separations of equimolar CH4/H2 and CO2/CH4 gas mixtures under various cyclic adsorption conditions, such as pressure-swing adsorption (PSA), vacuum-swing adsorption (VSA), temperature-swing adsorption (TSA), and pressure-temperature swing adsorption (PTSA) conditions. Several hypoCOFs were discovered to achieve high adsorption selectivities and working capacities, outperforming synthesized COFs and MOFs. In the final two parts, we introduced the COF Space concept, aiming to fully explore the vast COF materials (~70 000 COFs and hypoCOFs). We established ML models that can rapidly and reliably predict the adsorption- and membrane-based separation performances of COFs and hypoCOFs, providing a computationally efficient alternative to conventional simulations. Using this ML-based screening strategy, we systematically mapped the entire COF material space in terms of (i) their adsorption properties for five different gases (CO2, CH4, H2, N2, and O2), (ii) their adsorption-based separation performance for six industrially relevant gas mixtures (CO2/CH4, CO2/N2, CO2/H2, CH4/H2, CH4/N2, and O2/N2), and (iii) their membrane-based separation performance for seven distinct gas pairs (CO2/CH4, CO2/N2, H2/CO2, H2/CH4, H2/N2, O2/N2, N2/CH4). The findings of this dissertation are therefore expected to directly inform rational COF design and the targeted development of new materials for diverse gas separation applications.
Kaspaz-1 için peptit bazlı ilaç tasarımı ve metabolik yol analizi
As one of the mostly studied protein in the literature, caspase-1 (ICE) attracts the attention of many scientists due to its crucial roles in inflammatory responses. It has other roles in the apoptotic path, for example, because of having more than 40 substrates. Increased expression of its substrates such as pro-IL-1beta results in inflammatory disorders. Consequently, inhibition and pathway studies related to caspase-1 have gained importance.Peptide based drug design for caspase-1 is performed and potent inhibitors are determined computationally. Bicylic (a molecule that contains two fused rings) and ketone structures with Asp and D-enantiomeric aminoacids are obtained as good inhibitors in accordance with previous experimental work. Moreover, multi-target drug determination in the caspase-1 pathway is made. Conformational factors in tripeptides are also taken into consideration with Viterbi Algorithm, which indicates whether a peptide can change its conformation from minimized state to bound state.Knockout analysis on the ICE pathway by Gaussian Network Model (GNM) shows knockouts of NLRP3, ASC, caspase-1, NF-kappaB, pro-IL-33, TLR4, TLR2, TRIF and MyD88 are effective.?
Silika aerojel polimer kompozitlerinin hazırlanması ve karakterize edilmesi
In this study, new nanotechnology-based high performance insulation systems for energy efficiency, nanostructured composites of silica aerogels with polymers are being developed as core materials for vacuum insulation panels in buildings. Monolithic composites of a wide variety of polymers with silica aerogels were synthesized by modification of the conventional sol-gel method to produce silica aerogels. The polymers used in the study are poly(ethylene block poly ethylene glycol) (PEPEG), poly(vinyl pyrrolidone) (PVP), poly(vinyl acetate) (PVAc) and poly(methyl vinyl ether) (PMVE). Characterization of the composite materials was performed by Fourier Transform Infrared - Attenuated Total Reflectance (FTIR-ATR) spectroscopy, Thermal Gravimetric Analysis (TGA) and by Nitrogen Physisorption using BET. Both transparent and opaque crack-free monolithic composites suitable for testing and use in VIPs were obtained. The presence of polymer in the composites was confirmed by IR spectroscopy and TGA. The composites were mesoporous materials with high surface areas around 800 m2/g and average pore sizes around 5 nm. Incorporation of polymers did not significantly change the pore size distribution and the specific surface area of the pure silica aerogels. The effects of the time of polymer addition at various stages of the conventional sol-gel process such as before/after the hydrolysis step and during the aging step, on the properties of the composites were investigated. Opacity was found to be correlated to the phase separation of the polymer from the reaction mixture. The effect of polymer content on the resulting properties was also investigated along with density, porosity and shrinkage calculations.
T-hücresi akut lenfoblastik lösemi için birbirine bağlı marker tanımlanması
T-cell acute lymphoblastic leukemia (T-ALL) is a very complex disease, resulting from proliferation of differentially arrested immature T-cells. The molecular mechanisms and the genes involved in the cause of T-ALL remain largely undefined. In this study, we found biomarkers to differentiate individuals with T-ALL from the non-leukemia/healthy ones, to discover markers that are not differential themselves but interconnect with highly differentially expressed genes, and to have a network-based view of T-ALL. Instead of applying only expression-based differential gene analysis and obtaining hundreds of candidate disease-causing genes, we integrated gene expression data of T-ALL and healthy samples with the human protein-protein interaction data in order to discover diagnostic biomarkers not as individual genes but as subnetworks. By using a network-based approach, we have identified 19 significant subnetworks, containing 102 genes (out of 409 genes). A given subnetwork contains up-to 12 genes. The classification/prediction accuracies of subnetworks are considerably high, as high as 98%. Some genes in the subnetworks were already known to be associated with T-ALL, but we found new ones to be involved in T-ALL development. The subnetworks were rich in transcription factors whose ectopic activation is known to be one of the reasons behind T-ALL. The Zinc-binding proteins are also abundant in subnetworks. Zinc levels are low in ALL-patients. Zinc supplement given to a T-ALL patient may increase the efficiency of chemotherapy. We recovered 6 tyrosine kinases which have important roles in T-cell survival, proliferation, and immune response. These important genes in our subnetworks may serve as an alternative to the traditional biomarkers used for the diagnosis of T-ALL. The aim of this study is also to help investigators to highlight potential disease gene candidates for further experimental validation.We also applied a typical hierarchical clustering method to most differential 100 and 200 genes between T-ALL and healthy samples. As opposed to the presumption that most differential 100 or 200 genes would classify the diseased samples better, our subnetworks achieved the same or, in some cases, higher classification accuracies. Doing the same/better job with 10 genes in a subnetwork, instead of 100 or 200 genes might be regarded as an accomplishment. In short, network-based classification techniques help us to identify biologically more meaningful subnetworks than expression-based techniques which return thousands of differential genes.
DNA tamirinin yüzey plazmon rezonansı ile gerçek zamanlı incelenmesi
DNA structure can be greatly affected by UV light exposure. The cyclobutane prymidine dimer (CPD) and 6-4 lesion formations along with the specific breaks on strands are the most common type of DNA damage caused by UV irradiation. Specific to UV-damaged DNA, CPD photolyase I and II construct two subfamilies of flavoproteins, and they have recognition and repair capabilities of CPD sites on both single stranded (ssDNA) and double stranded (dsDNA) DNA with the aid of blue light energy. The other types of flavoprotein family consist of cryptochromes (CRY), and the most commonly known types act as photoreceptors in plants, or circadian rhythm regulators in animals, but lack photorepair activity. Recently, it has been found that a specific type of cryptochrome also has photorepair activity on ssDNA. This protein, called cryptochrome-DASH (CRY-DASH), is yet to be studied for its binding to DNA with newly developed techniques. In this thesis, CRY-DASH-DNA interaction was investigated using Surface Plasmon Resonance (SPR) which is a common assay to characterize protein-DNA or protein-protein interactions. Next, interaction of UV damaged and undamaged DNA with CPD photolyase was then examined and compared with the interaction of damaged/undamaged DNA and CRY-DASH. SPR shows the immediate molecular binding of DNA to the surface and confirms the specific binding of photolyase and CRY-DASH with UV treated or UV untreated DNA by providing kinetic constants of binding. This study is significant for investigation of repair of lesions in the DNA structure using SPR.
Patates ADP glikoz pirofosforilaz enziminin allosterik özelliklerinin modulasyonu için önemli amino asitlerin belirlenmesi
ADP glucose pyrophosphorylase (AGPase) is a key regulatory enzyme of bacterial glycogen and plant starch synthesis as it controls carbon flux via its allosteric regulatory behavior. Whereas the bacterial enzyme is composed of a single subunit type, the plant AGPase is a heterotetrameric enzyme (?2ß2) with distinct roles for each of the two subunit types. The large subunit (LS) is involved mainly in allosteric regulation through its interaction with the catalytic small subunit (SS). Previously, critical amino acids of potato (Solanum tuberosum L.) LS that interact with SS in the native heterotetramer structure were identified both computationally and experimentally. In this study, we aimed to improve the heterotetrameric assembly of potato AGPase and to detect residues located on the interface involving the allosteric regulation of the enzyme with a reverse genetics approach. A mutant, ?2ß2 formation deficient, large subunit of potato AGPase named LSR88A was subjected to random mutagenesis using error prone PCR and screened for the capacity to form an enzyme restoring glycogen production in glgC- Escherichia coli, AGPase activity deficient, containing wild type SS by assessing iodine staining. Fifteen suppressor mutants were identified and sequence analysis of these mutants revealed that mutations are mainly clustered at subunit interface and nearby the subunit interface. Subsequently, R88A mutation was reversed with site directed mutagenesis to see the effect of these mutations in the absence of R88A mutation. Kinetic characterization showed that two random mutants, named RM2 and RM10, exhibit altered allosteric properties than the wild type. These results indicate that interfaces between the large and small subunits are significant for the allosteric properties of the AGPase. Obtaining stable and up-regulated AGPase variants will enable us to use these mutants to increase the starch yield in crop plants.
Küçük molekül ağırlıklı ilaçların proteinler ile etkileşimlerinin teorik ve deneysel olarak incelenmesi
In this study, interaction of small molecular weight drug compounds with proteins has been characterized. The binding constants of interactions were first theoretically calculated using molecular docking simulations. Next, these interactions were experimentally investigated via Surface Plasmon Resonance (SPR). The theoretical binding constants, KD, predicted from theoretical calculations have been compared with the experimental values obtained from SPR. As a model protein, Escherichia coli (E.coli) DNA photolyase was used due to its known crystal structure. Among the eight drugs analyzed, theoretical and experimental values have shown similar binding affinities between selected drug and protein pairs. The results obtained in this study may be significant to characterize the unknown interactions of existing drugs with various proteins.
İnterlökin 1 beta (IL-1ß) için peptid kökenli inhibitör dizaynı
Interleukin 1 beta (IL-1ß) is a pro-inflammatory cytokine which is activated intracellularly by the cleavage of Caspase-1 (Interleukin converting enzyme, ICE). Upon activation, IL-1ß is secreted to the extracellular region. Increased expression of IL-1ß is associated with several diseases such as rheumatoid arthritis and intestinal inflammation. Thus in controlling these kinds of diseases, blocking IL-1ß has great importance. IL-1ß has an embedded receptor (IL-1R) and an accessory protein (IL-1RAcP) in the cell surface. IL-1ß binds to its receptor and to the accessory protein which juxtaposes the intracellular domains of its receptors and causes more expression of IL-1ß. In this study, inhibition of active IL-1ß has been studied extensively by using computational docking tools and techniques. Binding residues of IL-1ß to its receptor were accepted as docking regions. Genetic Algorithm (GA) and Viterbi algorithm (VA) based on the Hidden Markov Model were applied to obtain the most suitable inhibitor candidate by considering its binding free energy and secondary structure conformation. As a result, tripeptide and pentapeptide candidates from Genetic Algorithm and thirty heptapeptide candidates from the Viterbi Algorithm were obtained. Elimination of the candidates was achieved by Molecular Dynamics Simulations by calculating binding free energies of the ligands. Most potent inhibitor for IL-1ß was observed to inhibit IL-1? as well. The unbinding process of ligands was investigated and their probability distribution graphs were examined.