M.S. Theses
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Item Modeling the effect of sulfur loading on the electrochemical performance of a lithium-sulfur battery(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Uzun, Oğuztan.; Eroğlu Pala, Damla.Lithium-sulfur (Li-S) batteries are a promising solution for the efficient energy storage demand due to their high theoretical specific energy. Also, Li-S battery has inexpensive raw materials that are naturally abundant and non-toxic. For these advantages, they are considered an alternative to Li-ion batteries. However, they also have disadvantages, such as low cycle life and considerable self-discharge. The complex electrochemical reactions in Li-S batteries need to be better understood and defining the significant parameters and their effects are required to overcome these challenges. So different studies are focusing on finding the optimum cathode design parameters. This study investigates the effect of one of the critical cathode design parameters, sulfur loading, on the electrochemical performance of a Li-S battery. Although sulfur is an electrochemically active material in the cell, it negatively affects cell performance at high loadings since it is insoluble and insulating. In order to increase the conductivity and surface area, carbon is typically used in the cathode. However, an inert material like carbon can lower the energy density. So, the optimum cathode design for high S utilization and high energy density is still under investigation because of the Li-S battery’s complex mechanisms. For estimating the effect of S loading, computational algorithms were used in this study; simplified zero-dimensional and more complex one-dimensional electrochemical models were selected, and the models' response in predicting the impact of S loading on the discharge performance was compared. The zero- dimensional model could not capture the effect of S loading on the discharge capacity; however, the one-dimensional model successfully predicted the experimental trends. Furthermore, a sensitivity analysis was done on both models for different model parameters to discuss the reasons for the differences between the models.Item A study on dispersion improvement of active sites on supported metal catalysts(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Taş, Enes Emre.; Aksoylu, Ahmet Erhan.The current work aims to enhance metal dispersion on supported metal catalysts synthesized via dry impregnation (DI) method by focusing on 10 wt.% Ni/γ-Al2O3 catalysts as a specific example. Impregnated samples underwent calcination at varying temperatures (500°C, 700°C, and 900°C) and subsequent reduction at 800°C for 1 h under pure H2 flow. Dispersion levels were determined by using the static H2 chemisorption method. Additionally, N2 physisorption, Raman, XRD and XPS analyses were performed on selected samples. The study includes the use of γ-Al2O3, precalcined Al2O3 at 920°C, and 1 wt.% La- doped Al2O3 as support materials, for which the PZC values were measured as 8.49, 8.42, and 7.82, respectively, using the salt addition method. pH shift models were utilized to determine the final pH values of impregnated slurry and to adjust the pH of the precursor solution. The behavior of nickel species in the impregnating solutions was modeled based on the changing pH, employing stability constants of potential compounds. Suitable complexing agents were selected according to the support material's surface charge across the pH scale to obtain Ni complexes/chelates. UV-Visible Spectrophotometry was used for the analysis of solutions. Results demonstrate that the surface distribution of metallic Ni is the primary factor determining dispersion levels at calcination temperature of 500°C. Conversely, at 700°C and 900°C, the formation of NiAl2O4 becomes the predominant factor, as confirmed by XPS and XRD analyses. NTA and CA complexes yield the highest dispersion results at lower calcination temperatures (500°C and 700°C), while the EDTA complex exhibits better results for sample calcined at 900°C. The highest dispersion level achieved using the Ni-CA complex for sample calcined at 500°C is 9.2624%, whereas the lowest dispersion is observed with the same support and solution when it is calcined at 900°C, with a dispersion of 2.5622%. These findings indicate that dispersion is influenced by a combination of different factors that should be evaluated holistically.Item Effect of electrolyte-to-sulfur ratio on the performance of lithium-sulfur batteries with different electrolyte systems(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Fırtın, Ayça.; Eroğlu Pala, Damla.Lithium-sulfur (Li-S) batteries, one of the most promising alternatives for next-generation battery systems, have been a top research topic due to their high theoretical specific capacity and energy density. In Li-S batteries, battery performance is closely tied to certain materials and cell design parameters, particularly electrolyte design, due to the complex mechanisms in the cell. Herein, the effect of the electrolyte-to-sulfur (E/S) ratio, a critical electrolyte design parameter, on the performance of the Li-S batteries is investigated with various electrolyte systems. In order to understand the effect of the design parameters on battery performance, at first, experimental characterization is conducted by using galvanostatic cycling and electrochemical impedance spectroscopy (EIS). In the experimental study, seven different types of electrolytes with four different E/S ratios of 6 µL/mg, 9 µL/mg, 16 µL/mg, and 23 µL/mg are used. In general, the lowest E/S ratio, 6 µL/mg, has a better performance compared to the highest E/S ratio because the high amount of electrolyte escalates the polysulfide shuttle mechanism, affecting the battery performance adversely. In addition, EIS characterization is used to investigate the resistance of cells with an E/S ratio of 6 µL/mg and 9 µL/mg. Then, zero-dimensional and one-dimensional electrochemical models are developed to mechanistically study the effect of the E/S ratio on the discharge profile. The 0-D model can capture the expected trend of the variance of the discharge profile with changing E/S ratio; the capacity decreases significantly with decreasing E/S ratio. To model the impact of the E/S ratio on the discharge profile of Li-S for different electrolyte systems, selected model parameters were changed systematically. At the different values of kS8, both the 0-D and 1-D models predict other trends for the dependence of discharge profiles on the E/S ratio. Consequently, both models can capture the effect of the E/S ratio on the discharge behavior for different electrolyte systems implicitly through the variation of kS8. It is seen clearly from this study that the impact of the electrolyte amount on battery performance changes according to the types of electrolytes.Item Machine learning analysis of photocatalytic CO2 reduction on perovskite materials(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Zırhlıoğlu, İrem Gülçin.; Yıldırım, Ramazan.The purpose of this study is to construct a database from the experimental studies about CO2 reduction on perovskite materials from published articles, then to extract information from this dataset to predict CO2 production yields and the bandgap of the perovskites by using machine learning methods such as decision tree (DT), random forest (RF), gradient boosting (XGBoost), association rule mining (ARM), and linear regression (LR). By using Web of Science, relevant articles were examined, and 61 articles were selected for data extraction; 309 samples with 29 features (14 numerical and 15 categorical) were collected; these features included properties of perovskites such as bandgap, elemental information, and conditions of the experiments such as reaction temperature, phase of reaction collected as the features. Before the machine learning applications, pre-processing steps were applied to the dataset for cleaning and organizing. For the missing bandgap values, linear regression was applied for prediction from the available data. The biased and the highly absent features were eliminated while the missing values of others were filled with the mod or mean of the dataset. The ML methods were applied using two separate databases which were for gas and liquid phase reactions. 133 out of 309 samples with 30 features were used for gas phase dataset while the remaining 176 samples with 29 features were for liquid phase. 17 missing band gap values were predicted using linear regression with the R-square and RMSE were found as 0.75 and 0.36 respectively for validation set. With DT, the accuracy for test set was obtained 0.76 for gas phase and 0.84 for liquid phase. In the RF predictions, R- square and RMSE were found to be 0.64 and 24.5, respectively for test set in gas phase while they were 0.49 and 221.0 in liquid phase. Bandgap was the most important feature for gas phase while the most important feature for the liquid phase was found to be the cocatalyst. Finally, in the XGBoost, R-square and RMSE for test set in gas phase were 0.65 and 14.75, respectively and for liquid phases, they were 0.79 and 145.6.Item Understanding the attention deficit hyperactivity disorder-gut axis by metabolic network analysis(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Taş, Ezgi.; Ülgen, Kutlu Ö.Attention de cit hyperactivity disorder (ADHD) is a neurodevelopmental disorder diagnosed with hyperactivity, impulsivity, and a lack of attention inconsistent with the patient's development level. People with ADHD frequently experience gastrointestinal (GI) dysfunction, suggesting a possible role of the gut microbiome. The present research aims to determine a set of biomarkers for ADHD by reconstructing a model of the gut-microbial community and understanding the e ects of metabolites produced by gut microbiota on the human brain. Genome-scale metabolic models (GEM), considering the relationship between gene-protein-reaction associations, are used to simulate metabolic activities in gut organisms. The production rates of dopamine and serotonin precursors and the key short-chain fatty acids which a ect the health status are determined under three diets (Western, Atkins', Vegan) and compared with healthy subjects. Elasticities are calculated to understand the sensitivity of exchange uxes to changes in diet and bacterial abundance at the species level. A pre-prepared human brain model with 812 metabolites, 994 reactions, 671 genes, and 71 metabolic pathways is used as the healthy brain model. Genes NOS1 and SLC6A3 are deleted from the healthy model to simulate an ADHD brain. In order to achieve an integrated gut-brain model, a three-compartment model (gut, blood, and brain) is curated. The presence of Bacillota, Actinobacteria, Bacteroidetes, and Bacteroidota may be possible gut microbiota indicators of ADHD. This type of modeling approach, taking microbial genome-environment interactions into account and how the metabolites they produced interact with other organs in the human body, aids in understanding gastrointestinal mechanisms behind ADHD and improving the quality of life for patients.Item Design, construction and testing of various reactor geometries for gas-phase photocatalytic carbon dioxide reduction(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Rezqalla, Batool Abdalla.; Yıldırım, Ramazan.This thesis proposes a comprehensive preliminary design of a gas- phase photocatalytic carbon dioxide reduction system with water. The aim was to design, construct and operate a reaction system using titania-based photocatalytic materials and various sources of light. To accomplish this, the lab-scale process system was constructed from scratch, which was followed by calibration of the equipment, synthesis of the photocatalyst material, and initial operation of the process as a whole under varying operating conditions. The main operational variables were the type of light source, the weight of photocatalyst, the inlet gas flowrates and the water heating of the water bubbler, which was used to generate moisture and transport it with CO2 into the system. The reactions were carried out over photocatalysts containing single (Pt or Cu) and bimetallic (Pt + Cu) co-catalyst loading onto TiO2 with or without coating with ionic liquid after metal loading. Two tubular and one balloon photoreactor geometries were tested, and the balloon photoreactor geometry proved to be the most effective. Syngas was obtained as the main product using the single metal photocatalysts, while syngas and methane were obtained using the bimetallic photocatalyst. Using single metal loaded TiO2, the maximum product yield obtained was 11.79 (with Pt) and 83.72 μmol/(h.gcat) (with Cu) for H2 and CO, respectively. With the bimetallic photocatalyst, those values increased to 164.76, 1757.29, and 60.26 μmol/(h.gcat) for H2, CO, and CH4, respectively. Establishing better gas-photocatalyst contact in the photoreactor design and enhancing the properties of the photocatalyst by applying various modifications were proposed for future studies to further improve the system performance.Item Modeling of reverse water-gas shift reaction in membrane integrated microreactors(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) İnce, Mert Can.; Avcı, Ahmet Kerim.Synthesis gas (syngas) production by reverse water-gas shift (RWGS) reaction is modeled in a membrane integrated microchannel reactor. Process intensification is performed by in-situ steam separation via a hydrophilic α-Al2O3 supported sodalite membrane (SOD), which allows selective transport of H2O and H2 molecules. CuO/ZnO/Al2O3 (CZA) catalyst is considered as a layer that is washcoated to the inner walls of the rectangular shaped reaction channels of the microreactor. Pure H2, readily available as a reactant, is used as the sweep gas in the permeate channel. Two dimensional, steady–state, isothermal operation of the intensified reactor is quantified by the momentum and mass conservations in the entire flow domain, membrane material transfer and catalytic reaction. The reactor is operated at 523 K, 5-15 bar and inlet molar H2:CO2 ratio = 2-4 and the effects of inlet velocities of the reactive mixture (H2+CO2) and sweep gas (H2), reactor pressure, molar inlet H2:CO2 ratio and flow partitioning on per cent CO2 conversion, amount of CO2 converted and the synthesis gas composition are studied. Membrane assisted efflux of H2O from/ influx of H2 into the reaction channel significantly improves the performance. A non-isothermal study is conducted to demonstrate that the microchannel system has the characteristics of near-isothermal conditions. The effect of flow direction on the reactor operation is found to be negligible. Sweep gas inlet velocity affected the performance metrics significantly. Integration of steam selective membrane and increasing sweep velocity to six times of the reaction channel inlet velocity showed an increase in CO2 conversion from 16.1% to 51.6%. Sizing studies point out that 3.6 m3 multichannel reactor can process H2 input from a 1 MW commercial electrolyzer. The current reactor is also benchmarked with an equivalently operated packed-bed membrane reactor.Item Optimizatıon-driven data-based constraints identification via explicit mathematical and implicit machine-learning-based constitutives(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Aladağ, Abdullah.; Akman, Şükrü Uğur.The major aim of “data-based constraint identification” is to identify feasible regions within which a process can be operated. Our approach is based on the quantitative-feasibility information of sample points metamorphosed into single- and multiple- mathematical equations constituting the data-based constraints. We firstly devise an “overall objective function” which is capable of identifying feasible regions with multiple- constitutive inequality constraints by resorting to the technique of “constraint aggregation”. We then equip our algorithm with the “form-specific constitutives” build via the generic mathematical description of some plausible inequality constraints such as the bound, linear, circular, and ellipsoidal, as single or aggregated multiple constitutives. We then build the “form-specific” and “form-free” constitutives via the “design matrix” approach, also as single or aggregated multiple constitutives. We devise the “implicit neural constitutives” as well via some Machine Learning algorithms such as Neural Networks and Extreme Learning Machines, as single implicit or aggregated multiple implicit constitutives. All of these data-based constitutive constraints are generic such that they can identify N-dimensional feasible regions. We solve the demonstrative examples with the Differential Evolution or Covariance Matrix Adaptation Evolution Strategy global optimizers. Via many diversified examples, including several chemical-engineering related ones, we show that our algorithm can identify joint, disjoint, convex, or nonconvex regions or their combinations. We also apply classification techniques, such as Probabilistic Neural Network, k-Nearest Neighbour, Support Vector Machine, Gaussian Process Regression, and Regression Trees to constraint identification. Our algorithm is also successful in identifying constraints from image boundaries, i.e., in “image-to-constraints” conversion tasks.Item Effect of electrolyte properties on lithium-sulfur battery performance(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Yüksel, Kağan.; Eroğlu Pala, Damla.Lithium-sulfur (Li-S) batteries take attraction due to their promising theoretical gravimetric and volumetric energy densities, besides the natural abundance, low toxicity, and low cost of sulfur. The properties of the electrolyte significantly affect the electrochemical performance since the cathode kinetics in a Li-S cell includes the formation of lithium polysulfide intermediates soluble in the electrolyte. Herein, the effect of electrolyte constituents, namely the solvent and the salt, on discharge performance was investigated experimentally and theoretically. First, the effect of salt and solvent type on the discharge capacity, cycle life, capacity retention, and cell resistance of a Li-S cell were experimentally characterized by using galvanostatic cycling and electrochemical impedance spectroscopy (EIS) methods for sulfolane and triglyme solvents, and LiTFSI, LiTF, and LiClO4 salts at different electrolyte-to-sulfur (E/S) ratios. LiTFSI salt leads to the best cycling performance in sulfolane-based electrolytes, with higher discharge capacities at all cycles for almost all E/S ratios. On the other hand, LiTF salt- containing cells have superior capacity retention at low E/S ratios for triglyme-based electrolytes. The discharge performance of a Li-S battery is highly influenced by both the solvent and the salt type, particularly at low E/S ratios. Given this, 1M LiTFSI in a sulfolane electrolyte system appears to be promising for achieving high performance at low E/S ratios. A zero-dimensional and a one-dimensional model were also built to investigate the effect of electrolyte properties on the discharge performance of a Li-S battery. In both models, the effect of electrolyte type and properties on discharge performance is implicitly described by the variation of chosen model variables. In the 0-D model, the discharge capacity can only be controlled by the S8 precipitation rate constant. In contrast, the influence of electrolyte characteristics on discharge capacity in the 1-D model may be controlled by the S8 precipitation rate parameters, kS8 and KspS8, as well as the diffusion coefficients. Both models can predict the effect of electrolyte characteristics on discharge performance, but the one-dimensional model contains more factors that influence the discharge curve.Item Machine learning analysis of data collected from published literature on photocatalytic reforming of glycerol(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Karakoyun, Rüveyda.; Yıldırım, Ramazan.In this thesis, the aim is to extract knowledge from the data that was collected from published literature about photocatalytic reforming of glycerol. 791 data points were collected from 93 articles. This data was cleaned, organized, and prepared for the machine learning methods. Random forest and ANN (Artificial Neural Network) were used as machine learning techniques. By using them, the models for band gap and hydrogen production rates were constructed. Cross validation was applied to all models to prevent overfitting. For hydrogen production rate model, the missing values for band gap were filled with the predicted values of ANN of band gap. In random forest, feature importance was determined and the variables with the highest effect on the result were found. For band gap, the most important variables were weight percent of cocatalyst, percent of semiconductor and calcination temperature and duration. For hydrogen production rate, the most significant variables were photocatalyst load, band gap, glycerol concentration, weight percent of cocatalyst and pH. In random forest, the best model was determined by changing test/train split and k values in k-fold cross validation for various tree number and number of samples in a leaf node. For band gap model, 0.25 test/train split and 4-fold with 41 trees and 1 sample was the best model with RMSE (Root Mean Square Error) of 0.234 and R-squared of 0.73. For hydrogen production rate model, 0.25 test/train split and 5-fold with 81 trees and 2 samples was the best model with RMSE of 1.09 x 104 and R- squared value of 0.71. For ANN, test/train split ratio, k value for k-fold cross validation, the number of neurons and activation function were changed to find the best model. For band gap, 52 neurons and ReLU function gave the best model with RMSE of 0.282 and R-squared value of 0.70 with 0.3 test/train split and 4-fold cross validation. For hydrogen production rate model, 0.25 test/train split ratio, 7-fold cross validation, 63 neurons and ReLU function gave the best model with RMSE of 1.47 x 104 and R-squared value of 0.60.Item Modeling of ammonia synthesis in a wall-coated membrane microchannel reactor(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2023., 2023) Küçük, Emre.; Avcı, Ahmet Kerim.Ammonia (NH3) synthesis is modeled in a micro–structured membrane reactor (MR) comprising reaction, cooling and NH3 separation functions in the same volume. The proposed MR involves permeate and reaction channels segregated by layers of zirconia supported ZnCl2 immobilized molten–salt (IMS) membrane selective to NH3 transport. While H2+N2 is fed to reaction channels washcoated with an iron–based catalyst, permeate channels host N2 as the sweep gas which also regulates reaction temperature. The in–situ cooled MR is modeled by considering mass, momentum and energy conservation in the fluid phases of the reaction and permeate channels, reaction in the catalyst layer and NH3 transport across the membrane, whose thermal stability limit of 623 K is set as the maximum reactor temperature. Upon N2 sweep dosing equal to 50 × the molar H2+ N2 input at H2/N2=3, 613 K, 50 bar and 1.5×103 m 3 kg cat 1 s 11, MR can deliver ~47% N2 conversion which exceeds 40 and 13.5% of the pertinent thermodynamic barrier and the membraneless case, respectively. Despite the exothermic heat release, co–current partitioning of the streams ensures operability below 623 K. Increasing space velocity and H2/N2 ratio, and decreasing inlet temperature and pressure inhibit reactor performance. Using molar sweep–to–reactive mixture ratios <50 is penalized by the violation of the specified maximum temperature.Item Molecular dynamics study of the interactions between thymoquinone and lipid bilayers(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Ülgey, Esra.; İleri Ercan, Nazar.Thymoquinone, a phytochemical with antitumor activity, and its derivative aminothymoquinone were investigated using Molecular Dynamics (MD) Simulations to understand their interactions with simple and complex bilayer models. MD simulations of the models were performed using all atom (AA) and coarse-grained (CG) force fields, i.e., the OPLS-AA and MARTINI 3. Although the resolution of the molecules decreased during coarse-graining, the chemical and thermodynamic properties of the molecules were mostly retained. The bond and dihedral distributions validated the matching of AA and CG models, and the free energy calculations showed the reproducibility of new models apart from the agreement with the experimental logP values with less than 10% of error. AA and CG thymoquinone models were used with DOPC and POPC bilayers and the systems were compared with the sole bilayers. The structural properties of bilayers including area per lipid, bilayer thickness, order parameters, and lateral diffusion coefficients were computed. The interaction of CG molecules with two different normal and cancer membrane models was also investigated through the orientation of the molecules in the bilayers, the density distribution, and radial distribution function (RDF) in addition to the methods used for simple bilayer. Both molecules resulted in bilayer thinning with decreased bilayer thicknesses but increased the area per lipid values. Similarly, both molecules decreased the ordering of the bilayers, but the effect was slightly more significant with thymoquinone in normal membrane models. While thymoquinone diffused inside the model membranes, aminothymoquinone preferred to reside near the head groups of lipids. Overall, both molecules interacted similarly, in general, with the lipids of the model membranes, apart from small differences.Item Subsets of slow dynamic modes reveal global information sources as allosteric sites(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Altıntel, Bengi.; Haliloğlu, Türkan.Allostery is a crucial biological regulation mechanism, and dynamic information flow offers a framework to characterize allosteric interactions in causal links. Here, using a novel application of the Transfer Entropy (TE) calculations based on the Gaussian Network Model (GNM), it has been demonstrated how the dissection of dynamic information into subsets of slow dynamic modes reveals various layers of multi-directional allosteric pathways that are intrinsic in a particular protein structure. The degree of collectivity (Col) in the information transfer of residues with their TE values (TECol score) in these subsets of slow modes identifies particular residues as potent effectors and global information sources having a strong dynamic capacity to collectively disseminate information to other residues in the protein structure. These information source residues are linked to known active and allosteric sites, as demonstrated by aspartate transcarbamoylase (ATCase), Na+ /K+ -adenosine triphosphatase (Na+ /K+ -ATPase), and human transient receptor potential melastatin 2 (TRPM2), along with a dataset of 20 proteins. These specific residues provide feasible binding sites for structure-based rational drug design since they together affect/control others and direct pathways of allosteric communication.Item A study on mathematical modeling of direct synthesis of dimethyl ether in structured membrane reactors(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Köybaşı, Hayrettin Hasan.; Avcı, Ahmet Kerim.Catalytic transformation of CO2 into dimethyl ether (DME) is modeled in a membrane microchannel reactor. The reactor geometry includes an α-Al2O3 supported SOD membrane layer, capable of in-situ H2O removal, residing between a reaction channel and a parallel permeate channel. Reaction channels are washcoated with a physical mixture of Cu–ZnO/Al2O3 (CZA) and HZSM–5 catalysts, which are responsible for the synthesis and dehydration of methanol respectively. CO2-H2 reactant mixture (H2/CO2=3) is dosed to the reaction channel at space velocity=6x103 ml gcat-1 h-1 whereas, pure H2 at identical temperature and pressure, is fed to the permeate channel to sweep permeated steam. A steady-state isothermal reactor model based on the mass and momentum conservation equations is solved numerically using ANSYS software. Computed performance metrics show minimal deviation from the reported experimental data at 483–543 K, 30–50 bar. Incorporation of energy conservation to the model results in near-constant temperature profile and almost identical reactor performance, which validates isothermal operation. Owing to steam effux from and H2 influx to the reactive stream, membrane integration increases CO2 conversion (~60%) and DME yield (~30%) by a factor of >3 compared to membraneless operation. The benefits become more significant at higher temperature and pressures. Sending sweep H2 counter-currently offers superior H2O removal. Relative inlet velocity of the permeate inlet (vrat) affects membrane mass transfer dramatically. Adopting higher CZA/HZSM-5 mass ratio improves both performance metrics and the reactor capacity. With a ~7 m3 reactor system, 2.76x102 tons of DME can be synthesized from 1x103 tons of CO2 annually, provided that 1MW electrolyzer provides the required H2.Item Structural dynamics of ABC transporters(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Ersoy, Akarun Ayça.; Haliloğlu, Türkan.The ATP-binding cassette transporters (ABC transporters) translocate sub strates across membranes by ATP binding and hydrolysis. To understand the com plex structural dynamics, transporters were investigated on three case studies: het erodimeric multidrug exporter TmrAB, cystic fibrosis transmembrane conductance regulator (CFTR), and manganese ion importer PsaBC. Anisotropic Network Model Langevin Dynamics (ANM-LD) simulations were performed between four different con formations of TmrAB. The analysis for each transition revealed the allosteric couplings between the ATP binding in nucleotide binding domains (NBD) and extracellular gates on transmembrane domains (TMD). Transfer Entropy (TE) calculations showed that TMDs and NDBs of TmrAB become drivers at different stages during the translocation cycle. The ANM-LD simulations of dephosphorylated to phosphorylated CFTR transi tion revealed that the initial movements in the NBDs are followed by the movement of the gating areas in TM. The causal relationships between these movements were sup ported by TE calculations. CFTR potentiator drug Ivacaftor has been shown to have a global effect, mimicking the effects of ATP binding and gating residues. Corrector drug Lumacaftor has a local effect on TM1, 2, 3, and 6. The dynamics of PsaBC was characterized using TE calculations, and testable predictions were made. Four possi ble gating residues were identified along with an allosteric pocket on TM8. Finally, TE calculations of many ABC transporters showed the differences and similarities be tween different types. Type I transporters were shown to be controlled by TMDs in the inward facing (IF) conformation, and NBDs and substrate binding protein (SBP) in the outward facing (OF) conformation. Type II transporters were always driven by NBDs, regardless of conformation. Type IV transporters showed more variability, but for most, TMDs are driving for IF and NBDs are driving for OF conformations. Dynamic characterization methods show potential in classifying ABC transporters.Item Reconstruction of metabolic brain model with the aid of physiologically based pharmacokinetic modelling in the presence of autism spectrum disorder(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Erimli, Sena.; Ülgen, Kutlu Ö.Autism spectrum disorder (ASD) is a neurodevelopmental disorder with only symptomatic medical treatment with increasing prevalence. This study aims to reconstruct a genome-scale metabolic brain model that is expanded via genes and reactions related to ASD and observe changes in dopamine D2 receptors in the presence of antipsychotics. For this aim, iMS570, iNL403, and MODEL1608180000 are merged. After standardization, duplicate reaction removals, deletion of zero flux reactions, and expanding model with ASD related genes and reactions, the final model consists of 1638 reactions, 1358 metabolites, and 756 genes. For further investigations of the autism-specific brain model, transcriptome gene expression data GSE28475 is prepared and integrated. Autism-specific brain model shows mitochondrial and glutaminergic dysfunctions. Final 18360 unique genes are assigned as up-or down-regulated based on threshold determined by downregulated SHANK3 expression and 30% of averages of gene expression data. Additionally, the physiology-based pharmacokinetic (PBPK) model of risperidone and its metabolite paliperidone is simulated. Both drugs are antipsychotics that are used in ASD symptoms. First, simulations are performed to observe population density, CYP2D6 subtypes, ethnicity, total hepatic clearance, and P-gp concentration changes for 2 mg risperidone. PBPK simulation results of experimental articles were in the range of literature findings. Receptor occupancy simulations with chronic dosing underestimate plasma concentrations, but results are still in the range. With down-regulation of the SLC6A3 dopamine transporter gene, which is used to mimic DRD2 gene, proportional to receptor occupancy findings, the effect of risperidone on autism-specific brain model is investigated. Glutaminergic neurotransmission is decreased below healthy brain level, and mitochondrial dysfunction was relieved but still not in the healthy range. Reconstruction of ASD-specific brain model with PBPK modeling enhancement is promising work to better under disease mechanism.Item A preliminary work on design and development of sour water gas shift catalysts for synthetic natural gas process(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Özata, Hasretnur.; Aksoylu, Ahmet Erhan.The main goal of this study is to design and develop a model sour water gas shift catalyst showing high performance in terms of activity, stability & selectivity and conducting performance screening by mimicking the syngas composition of the potential coal gasifier under ideal, sulfur-free conditions. According to the literature, dry powder entrained-bed type gasifier seems potential gasifier for the Synthetic Natural Gas (SNG) production processes, and it contains low steam/CO ratio, below 0.8. On the other hand, classical sour water gas shift (SWGS) catalysts work under high steam/CO ratio, specifically, above 2 or 3. In this context, from literature, one of the potential SWGS catalysts which is suitable for dry-powder syngas composition was chosen as a reference. Activated carbon supported KCoRe as the model SWGS catalysts were developed and these catalysts were tested under ideal, sulfur-free syngas composition. The effect of reaction conditions (i.e., reaction temperature and steam to carbon monoxide ratio) and catalyst preparation & support pre treatment methods (i.e., impregnation method, air & nitric acid pre-treatment on the Activated Carbon support) were investigated on the prepared catalyst. Activity and selectivity were determined in terms of conversions of CO and H₂, respectively. Experimental results showed that reaction conditions are very important parameters on the performance of the catalysts. At low steam/CO ratios, it is necessary to increase the temperature in order to get high activity. The effect of the impregnation method may vary from support to support since impregnation depends on the surface chemistry of the support material. Co-impregnation method may not be suitable for air oxidized AC supported catalysts. Both air and nitric acid treatments are helpful to increase the performance of the catalysts by increasing the oxygen bearing surface groups on the AC support.Item Supercapacitors based on functionalized carbon materials(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Korkut, Ayşe Saliha.; Uralcan, Betül.Electrical double layer capacitors store energy in the form of electrical charges at the interface between an electrolyte and a high surface area electrode. As their energy storage mechanism relies on physical interactions, EDLCs have high power densities, unlike batteries, which are often limited by the slow charge- and mass- transfer kinetics. Additionally, EDLCs can sustain millions of charging/discharging cycles. Nevertheless, they suffer from low energy densities. For a high energy density EDLC, it is critical to maintain a compact architecture with large ion-accessible surface area while also ensur ing low ion transport and electrical resistance. We incorporate carbon quantum dots into thermally exfoliated graphene oxide sheets in the presence of a room temperature ionic liquid to form conductive carbon networks with improved ion transport networks, enhancing ion transport kinetics and storage. This yields an electrode in which both the carbon quantum dots and the ionic liquid serve as spacers to effectively separate the thermally exfoliated graphene oxide sheets, while the ionic liquid also functions as the electrolyte and carbon quantum dots provide a conductive network. Using this approach, we achieve a gravimetric capacitance of 165 F/g at 30/70 wt% CQD/TEGO composition with 4.5 M EMIM-BF4 electrolyte at 20 mV/s. The electrodes demon strate 70 % capacitance retention at 500 mV/s. When a 3.4 M EMIM-BF4 electrolyte is used instead, capacitance reaches 206 F/g, and the electrodes retain 70% of its ca pacitance at 500 mV/s. This demonstrates that we can simultaneously improve both energy and power density by tailoring the electrolyte composition.Item Investigation of medulloblastoma metabolism by tissue-specific genome-scale brain metabolic model and identification of therapeutic targets(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Özbek, İlkay İrem.; Ülgen, Kutlu Ö.Medulloblastoma (MB) is the most prevalent pediatric brain tumor arising in the cerebellum. Since conventional therapies decrease life quality and cause deleterious effects on children, computer models are urgently required to simulate cancer phenotypes and determine potential therapeutic targets with minimum side effects on healthy cells. In the present study, metabolic alterations specific to MB were reflected on the brain genome-scale metabolic model by employing transcriptome data. Moreover, the relation between metastasis and the Warburg effects and the pathways utilized by MB without carbon source were investigated. Flux sampling analysis was also performed to detect statistically different reactions in healthy and MB cases. Regulation, flux coupling, and essentiality analyses were conducted as well to find therapeutic targets for MB. Additionally, the antimetabolites which might lessen the use of substrates in cells by causing competitive inhibition were identified by using similarity scores and conducting FBA. To investigate sphingolipid metabolism in depth, 79 reactions were newly included in the MB model. Consequently, the MB model captured metabolic characteristics of MB successfully as confirmed by experimental studies. It was found that targeting proteins/enzymes related to fatty acid synthesis, mevalonate pathway in cholesterol synthesis and inhibition of cardiolipin production, and tumor inducing sphingolipid metabolites might be beneficial therapeutic strategies for MB. Furthermore, the suppression of GABA catalyzing and succinate-producing enzymes simultaneously might be a potential solution for metastatic MB. Using oleic acid as an antimetabolite owing to its structural similarity to linoleate and its downregulation in MB might be also a promising approach for this life-threatening disease.Item Optimization of energy density in supercapacitors by utilizing a hybrid artificial neural networks-genetic algorithm based optimization algorithm(Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Kaya, Duygu.; Uralcan, Betül.; Aydın, Erdal.Energy storage systems are required to meet the increased energy demand and re duce the need for fossil fuels. Energy storage devices are of interest to renewable energy systems and electric vehicles to provide a permanent energy supply. Supercapacitors, in particular, are eligible systems for energy storage owing to their unique properties such as very long cycle life, high reversibility, and high power density. Nonetheless, they have limited energy density. The ultimate goal is to increase the energy density of supercapacitors while maintaining high power density. In this thesis, hybrid artificial neural network-genetic algorithm (ANN-GA) model is utilized to increase the capac itance of supercapacitors. Several data preprocessing, feature selection, and machine learning algorithms are performed to predict the capacitance of supercapacitor by using experimental data. It is observed that ANN is a powerful method to capture nonlinear relationships concerning the physical and operational features of supercapacitors. GA is a promising method that examines search space for the optimal solution. The trained neural network model is used as the fitness function for genetic algorithm to achieve maximum capacitance within the feasible range. Selection, crossover, and mutation procedures are implemented in the reproduction step of GA to offer elaborate search space. In a nut shell, this study takes a step towards the rational design of superca pacitors by implementing a hybrid ANN-GA as an optimization tool to improve the capacitance. The results indicate that obtained optimal design parameters agree with the literature while improving the capacitance of supercapacitors significantly.