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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 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 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 Design and operation of renewable energy microgrids under uncertainty towards green deal and minimum carbon emissions(Elsevier Ltd, 2024) Department of Chemical and Biological Engineering; Tatar, Su Meyra; Aydın, Erdal; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM); Graduate School of Sciences and Engineering; College of EngineeringThe regulations regarding the Paris Agreement are inevitable to keep the global temperature rise within 2 °C. Moreover, integrating renewable energy-based equipment and adopting new ways of producing energy resources, for example Power to Gas (PtG) technology, becomes essential because of the current environmental, economic, and political concerns. It is vital to supply the growing energy demand with the increasing population. Uncertainty must be considered in the transition phase since parameters regarding the electricity demand, carbon tax policies, and intermittency of renewable energy-based equipment have intermittent nature. A multi-period two-stage stochastic MILP model is proposed in this work where the wind speed, solar irradiance, temperature, power demand, carbon emission trading (CET) price, and CO2 emission limit are considered uncertain parameters. Three stochastic case studies with scenarios including different combinations of the aforementioned uncertain parameters are investigated. Results show that more renewable energy-based equipment with higher rated power values is chosen as the sanctions get stricter. In addition, the optimality of PtG technology is also investigated for a specific location. Implementing the CO2 emission limit as an uncertain parameter instead of including CET price as uncertain results in lower annual CO2 emission rates and higher net present cost values. © 2023 Elsevier LtdPublication Metadata only IL-modified MOF-177 filler boosts the CO2/N2 selectivity of Pebax membrane(Elsevier, 2024) Department of Chemical and Biological Engineering; Habib, Nitasha; Tarhanlı, İlayda; Şenses, Erkan; Keskin, Seda; 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); Koç University Boron and Advanced Materials Application and Research Center (KUBAM) / Koç Üniversitesi Bor ve İleri Malzemeler Uygulama ve Araştırma Merkezi (KUBAM); Graduate School of Sciences and Engineering; College of EngineeringMixed matrix membranes (MMMs) having ionic liquid (IL) modified metal-organic frameworks (MOF) as fillers present a broad potential for enhancing the separation properties of the polymers. Here, we incorporated an IL, 1butyl-1-methyl-pyrrolidinium tricyanomethanide [BMPyr][TCM], into MOF-177 and used the corresponding composite as filler in Pebax polymer to fabricate IL/MOF-177/Pebax MMMs at different filler loadings. These MMMs along with those prepared by using pristine MOF-177 as a filler were then tested for CO2/N2 separation by measuring their CO2 and N2 permeabilities at 35 degrees C and 1 bar. The [BMPyr][TCM]/MOF-177/Pebax MMM having 10 wt.% filler loading showed remarkable improvements in both CO2 permeability (137 f 2.0 Barrer) and CO2/N2 selectivity (622 f 105) compared to the neat Pebax membrane having corresponding performance values of 98.0 f 2.0 Barrer and 64.5 f 6.0, respectively. This simultaneous improvement in both CO2 permeability and CO2/N2 selectivity breaks the trade-off limitation of polymer membranes. Besides, the MMMs having 10 and 15 wt.% loadings of fillers were located well above the updated Robeson's upper bound, demonstrating the great promise of [BMPyr][TCM]/MOF-177/Pebax MMMs for CO2/N2 separation.Publication Metadata only High-throughput computational screening of MOF adsorbents for efficient propane capture from air and natural gas mixtures(AIP Publishing, 2024) Department of Chemical and Biological Engineering; Erçakır, Göktuğ; Aksu, Gökhan Önder; Keskin, Seda; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of EngineeringIn this study, we used a high-throughput computational screening approach to examine the potential of metal-organic frameworks (MOFs) for capturing propane (C3H8) from different gas mixtures. We focused on Quantum MOF (QMOF) database composed of both synthesized and hypothetical MOFs and performed Grand Canonical Monte Carlo (GCMC) simulations to compute C3H8/N2/O2/Ar and C3H8/C2H6/CH4 mixture adsorption properties of MOFs. The separation of C3H8 from air mixture and the simultaneous separation of C3H8 and C2H6 from CH4 were studied for six different adsorption-based processes at various temperatures and pressures, including vacuum-swing adsorption (VSA), pressure-swing adsorption (PSA), vacuum-temperature swing adsorption (VTSA), and pressure-temperature swing adsorption (PTSA). The results of molecular simulations were used to evaluate the MOF adsorbents and the type of separation processes based on selectivity, working capacity, adsorbent performance score, and regenerability. Our results showed that VTSA is the most effective process since many MOFs offer high regenerability (>90%) combined with high C3H8 selectivity (>7 x 103) and high C2H6 + C3H8 selectivity (>100) for C3H8 capture from air and natural gas mixtures, respectively. Analysis of the top MOFs revealed that materials with narrow pores (<10 angstrom) and low porosities (<0.7), having aromatic ring linkers, alumina or zinc metal nodes, typically exhibit a superior C3H8 separation performance. The top MOFs were shown to outperform commercial zeolite, MFI for C3H8 capture from air, and several well-known MOFs for C3H8 capture from natural gas stream. These results will direct the experimental efforts to the most efficient C3H8 capture processes by providing key molecular insights into selecting the most useful adsorbents.Publication Metadata only Atomically dispersed zeolite-supported rhodium complex: selective and stable catalyst for acetylene semi-hydrogenation(Academic Press Inc., 2024) Su Yordanli, Melisa; Hoffman, Adam S.; Hong, Jiyun; Perez-Aguilar, Jorge E.; Saltuk, Aylin; Akgül, Deniz; Demircan, Oktay; Ateşin, Tülay A.; Aviyente, Viktorya; Gates, Bruce C.; Bare, Simon R.; Department of Chemical and Biological Engineering; Zhao, Yuxin; Bozkurt, Özge Deniz; 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 EngineeringSupported rhodium catalysts are known to be unselective for semi-hydrogenation reactions. Here, by tuning the electronic structure of supported mononuclear rhodium sites determined by the metal nuclearity and the electron-donor properties of the support, we report that atomically dispersed HY zeolite-supported rhodium with reactive acetylene ligands affords a stable ethylene selectivity > 90 % for acetylene semi-hydrogenation at 373 K and atmospheric pressure, even when ethylene is present in a large excess over acetylene. Infrared and X-ray absorption spectra and measurements of rates of the catalytic reaction complemented with calculations at the level of density functional theory show how the catalyst performance depends on the electronic structure of the rhodium, influenced by the support as a ligand that is a weak electron donor.Publication Metadata only Review: cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide(Frontiers Media Sa, 2024) Nussinov, Ruth; Yavuz, Bengi Ruken; Arıcı, M. Kaan; Jang, Hyunbum; Department of Chemical and Biological Engineering; Demirel, Habibe Cansu; Tunçbağ, Nurcan; Department of Chemical and Biological Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Sciences and Engineering; College of EngineeringThe connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.