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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Selection of ionic liquid electrolytes for high-performing lithium-sulfur batteries: an experiment-guided high-throughput machine learning analysis(Elsevier B.V., 2024) Kılıç, Ayşegül; Abdelaty, Omar; Yıldırım, Ramazan; Eroğlu, Damla; Department of Chemical and Biological Engineering; Zeeshan, Muhammad; Uzun, Alper; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); Graduate School of Sciences and Engineering; College of EngineeringThe polysulfide (PS) shuttle mechanism (PSM) is one of the most significant challenges of lithium-sulfur (Li-S) batteries in achieving high capacity and cyclability. One way to minimize the shuttle effect is to limit the PS solubilities in the battery electrolyte. Ionic liquids (IL) are particularly suited as electrolyte solvents because of their tunable physical and chemical properties. In this work, thousands of ILs are screened to narrow down potentially viable candidates to be used as electrolytes in Li-S batteries. To that end, the COnductor-like Screening Model for Realistic Solvents (COSMO-RS) calculations are performed over more than 36,000 ILs. An extensive database containing PS solubilities and other relevant properties is constructed at 25 °C. First, the effectiveness of the COSMO-RS calculations is experimentally tested with six different ILs having a wide range of solubility and viscosity values; a strong correlation between the PS solubility and battery performance is obtained. After specifying the target limits for promising ILs using the experimental battery performance data, machine learning (ML) tools are used to predict and identify the relationship between IL properties and PS solubilities and structural and molecular descriptors of ILs. The extreme gradient boosting (XGBoost) method successfully predicts the solubility and property values. Association rule mining (ARM) and the feature importance analysis show that anion descriptors are more dominant, whereas cations have less impact on the solubilities and properties of ILs. Finally, the imidazolium and pyridinium ILs with bis_imide and borate anion groups are identified as the most promising ones.Publication Metadata only Hydrothermal liquefaction of chlamydomonas nivalis and nannochloropsis gaditana microalgae under different operating conditions over copper-exchanged zeolites(Elsevier B.V., 2024) Borhan, E.; Haznedaroglu, Berat Z.; Department of Chemical and Biological Engineering; Yousefzadeh, Hamed; Uzun, Alper; Erkey, Can; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of EngineeringIn this study, two different green microalgae, Chlamydomonas nivalis (C. nivalis) and Nannochloropsis gaditana (N. gaditana), were cultivated in open ponds and the harvested wet biomass was converted to bio-crude by hydrothermal liquefaction (HTL) with/without catalyst. Catalytic HTL experiments were performed by using copper-exchanged zeolites including Cu-MOR, Cu-ZSM-5, and Cu-SSZ13, synthesized by recently developed supercritical ion exchange method using scCO2. The composition of all bio-crudes was analyzed by elemental analysis and GC/MS. First, the effects of different operating conditions on the yields of the products and the bio-crude composition were determined for non-catalytic process. Temperature, duration, and water/algae biomass ratio in the feed were the process parameters investigated in the ranges of 250–350 ºC, 10–60 min, and 5–20 wt%, respectively. For C. nivalis, 300 ºC, 60 min, and water/algae ratio of 4 were the optimum conditions which led to maximum bio-crude yield of 18.8 wt%, while 300 ºC, 30 min, and water/algae ratio of 9 were the optimum ones for N. gaditana at which the maximum bio-crude yield of 34.0 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, in line with the GC/MS results showing that the concentration of hydrocarbons and cyclic compounds increased in the presence of catalysts accompanied by a decrease in concentration of nitrogenous compounds.Publication Metadata only Physics-informed and data-driven modeling of an industrial wastewater treatment plant with actual validation(PERGAMON-ELSEVIER SCIENCE LTD, 2024) Esenboga, Elif Ecem; Cosgun, Ahmet; Kusoglu, Gizem; Department of Chemical and Biological Engineering; Köksal, Ece Serenat; Asrav, Tuse; Aydın, Erdal; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Graduate School of Sciences and Engineering; College of EngineeringData-driven modeling is essential in chemical engineering, especially in complex systems like wastewater treatment plants. Recurrent neural networks are effective for modeling parameters in wastewater treatment process such as dissolved oxygen concentration and chemical oxygen demand due to their nonlinear adaptability. However, traditional models face challenges such as the requirement for larger datasets and more frequent sampling, noisy measurements, and overfitting. To address this, physics-informed neural networks integrate physical knowledge for improved performance. In our study, we apply both approaches to a wastewater treatment plant, enhancing prediction performance. Our results demonstrate that physics-informed models perform successfully in offline and online validation, especially when standard methods fail. They maintain effectiveness without frequent updates. Yet, integrating physics-informed knowledge can introduce noise when standard methods suffice. This result points out the need for careful consideration of model choice in different scenarios.Publication Metadata only Suppression of segmental chain dynamics on a particle's surface in well-dispersed polymer nanocomposites(AMER CHEMICAL SOC, 2024) Kim, Jihyuk; Thompson, Benjamin R.; Tominaga, Taiki; Osawa, Takahito; Egami, Takeshi; Foerster, Stephan; Ohl, Michael; Faraone, Antonio; Wagner, Norman J.; Department of Chemical and Biological Engineering; Şenses, Erkan; Department of Chemical and Biological Engineering; College of EngineeringThe Rouse dynamics of polymer chains in model nanocomposite polyethylene oxide/silica nanoparticles (NPs) was investigated using quasielastic neutron scattering. The apparent Rouse rate of the polymer chains decreases as the particle loading increases. However, there is no evidence of an immobile segment population on the probed time scale of tens of ps. The slowing down of the dynamics is interpreted in terms of modified Rouse models for the chains in the NP interphase region. Thus, two chain populations, one bulk-like and the other characterized by a suppression of Rouse modes, are identified. The spatial extent of the interphase region is estimated to be about twice the adsorbed layer thickness, or approximate to 2 nm. These findings provide a detailed description of the suppression of the chain dynamics on the surface of NPs. These results are relevant insights on surface effects and confinement and provide a foundation for the understanding of the rheological properties of polymer nanocomposites with well-dispersed NPs.Publication Metadata only Acetylene ligands stabilize atomically dispersed supported rhodium complexes under harsh conditions(Elsevier Science Sa, 2024) Hoffman, Adam S.; Hong, Jiyun; Perez-Aguilar, JorgeE.; Bare, Simon R.; Department of Chemical and Biological Engineering; Zhao, Yuxin; Öztulum, Samira Fatma Kurtoğlu; Uzun, Alper; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); Graduate School of Sciences and Engineering; College of EngineeringFacile sintering of atomically dispersed supported noble metal catalysts at catalytically relevant temperatures, particularly under reducing conditions, poses a challenge for their practical applications. Some ligands, such as carbonyls, aid in improving the stability at the expense of severely suppressing the catalytic activity. Here, we demonstrate that substitution of the carbonyl ligands with reactive acetylene ligands can maintain the atomic dispersion of the supported mononuclear rhodium complex under harsh reducing conditions (>573 K), as confirmed by in -situ X-ray absorption near -edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopies. In contrast, the supported rhodium carbonyl complex aggregates into nanoclusters under identical conditions. Furthermore, our results indicate that the acetylene ligands provide this anti -sintering ability while retaining the hydrogenation activity.Publication Metadata only Stepwise conversion of methane to methanol over Cu-mordenite prepared by supercritical and aqueous ion exchange routes and quantification of active Cu species by H2-TPR(Elsevier, 2023) Sushkevich, Vitaly; van Bokhoven, Jeroen A.; Department of Chemical and Biological Engineering; Yousefzadeh, Hamed; Bozbağ, Selmi Erim; Erkey, Can; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of EngineeringCopper-exchanged mordenite prepared by supercritical ion exchange (SCIE) and aqueous ion exchange (AIE) were investigated in stepwise conversion of methane to methanol. Increasing the oxygen activation temperature and methane reaction time enhances the methanol yield of copper-exchanged mordenite prepared by SCIE (CuMORS). The reducibility of Cu-MORS was compared with those of Cu-MORA prepared by aqueous ion exchange (AIE) using H-2-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 yield data can be used to distinguish the active Cu sites from inactive ones based on their reduction temperature. The copper species responsible for methane activation were found to be reduced below 150 C by H-2 in both Cu-MORS and Cu-MORA. From the stoichiometry of the reaction of H-2 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 CuMORS 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.Publication Metadata only Boosting methylene blue adsorption capacity of an industrial waste-based geopolymer by depositing graphitic carbon nitride onto its surface: towards sustainable materials for wastewater treatment(Pergamon-Elsevier Science Ltd, 2024) Kaya-Ozkiper, Kardelen; Soyer-Uzun, Sezen; Department of Chemical and Biological Engineering; Uzun, Alper; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of EngineeringSurface characteristics of a geopolymer (GP) from an industrial waste, red mud (RM), and metakaolin (MK), were tuned by depositing urea-derived graphitic carbon nitride (g-C3N4) onto its surface. Methylene blue (MB) adsorption measurements demonstrated that the resulting g-C3N4/RM-MK-GP offers an excellent MB uptake capacity of 170.9 mg g-1, much higher than those of either the GP or the g-C3N4. Kinetics measurements revealed that chemisorption has an important effect on adsorption. The regenerability of g-C3N4/RM-MK-GP was studied for up to four consecutive cycles. Differences between the adsorption capacities of g-C3N4 and g-C3N4/RM-MKGP were investigated by combining the power of various characterization tools. Results pointed out that surface functional groups associated with g-C3N4, surface hydroxyl and silanol groups of RM-MK-GP, together with exchangeable charge balancing cations of geopolymeric framework provide a unique structure for g-C3N4/RMMK-GP. This study presents a versatile route to produce a sustainable, efficient, and cheap adsorbent for wastewater treatment.Publication Metadata only Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome(Nature Portfolio, 2023) Yavuz, Bengi Ruken; Arici, M. Kaan; Demirel, Habibe Cansu; Tsai, Chung-Jung; Jang, Hyunbum; Nussinov, Ruth; Department of Chemical and Biological Engineering; Tunçbağ, Nurcan; Department of Chemical and Biological Engineering; Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of Engineering; School of MedicineEpidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.Publication Metadata only Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction(Oxford University Press, 2024) Arici, Muslum Kaan; Department of Chemical and Biological Engineering; Tunçbağ, Nurcan; Department of Chemical and Biological Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Engineering; School of MedicineNetwork inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks.Publication Metadata only Theoretical insight into selectivity and catalytic activity of γ-(Al2O3) supported Ir(CO)2 complex for 1,3-butadiene partial hydrogenation(Elsevier, 2024) Akgul, Deniz; Ince, Deniz; Kozuch, Sebastian; Aviyente, Viktorya; Department of Chemical and Biological Engineering; Uzun, Alper; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of EngineeringSelective hydrogenation reactions of unsaturated hydrocarbons over metal oxide-supported transition metals like palladium, platinum, rhodium, or iridium complexes are common processes, but understanding the reaction mechanisms is still challenging, since the interactions between the reactants and the active center remain unclear. Herein, we have modeled the 1,3-butadiene hydrogenation mechanism in the presence of gamma-(Al2O3)2 supported iridium catalyst, initially present as [Ir(I)(CO)2]+. The origins of the selectivity and reactivity of the reduction reactions of butadiene to butenes has been elucidated by using density functional theory (DFT) within the framework of the energetic span model.