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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access Database for CO2 separation performances of MOFs based on computational materials screening(American Chemical Society (ACS), 2018) Eruçar, İlknur; Department of Chemical and Biological Engineering; Altıntaş, Çiğdem; Avcı, Gökay; Harman, Hilal Dağlar; Azar, Ayda Nemati Vesali; Velioğlu, Sadiye; Keskin, Seda; Researcher; Post Doctorate Student; Department of Chemical and Biological Engineering; College of Engineering; N/A; N/A; N/A; N/A; N/A; 40548Metal-organic frameworks (MOFs) are potential adsorbents for CO2 capture. Because thousands of MOFs exist, computational studies become very useful in identifying the top performing materials for target applications in a time-effective manner. In this study, molecular simulations were performed to screen the MOF database to identify the best materials for CO2 separation from flue gas (CO2/N-2) and landfill gas (CO2/CH4) under realistic operating conditions. We validated the accuracy of our computational approach by comparing the simulation results for the CO2 uptakes, CO2/N-2 and CO2/CH4 selectivities of various types of MOFs with the available experimental data. Binary CO2/N-2 and CO2/CH4 mixture adsorption data were then calculated for the entire MOF database. These data were then used to predict selectivity, working capacity, regenerability, and separation potential of MOFs. The top performing MOF adsorbents that can separate CO2/N-2 and CO2/CH4 with high performance were identified. Molecular simulations for the adsorption of a ternary CO2/N-2/CH4 mixture were performed for these top materials to provide a more realistic performance assessment of MOF adsorbents. The structure-performance analysis showed that MOFs with Delta Q(st)(0) > 30 kJ/mol, 3.8 angstrom < pore-limiting diameter < 5 angstrom, 5 angstrom < largest cavity diameter < 7.5 angstrom, 0.5 < phi < 0.75, surface area < 1000 m(2)/g, and rho > 1 g/cm(3) are the best candidates for selective separation of CO2 from flue gas and landfill gas. This information will be very useful to design novel MOFs exhibiting high CO2 separation potentials. Finally, an online, freely accessible database https://cosmoserc.ku.edu.tr was established, for the first time in the literature, which reports all of the computed adsorbent metrics of 3816 MOFs for CO2/N-2, CO2/CH4, and CO2/N-2/CH4 separations in addition to various structural properties of MOFs.Publication Open Access Plasmon-coupled photocapacitor neuromodulators(American Chemical Society (ACS), 2020) Ülgüt, Burak; Çetin, Arif E.; N/A; N/A; Department of Molecular Biology and Genetics; Department of Electrical and Electronics Engineering; Department of Chemical and Biological Engineering; Melikov, Rustamzhon; Srivastava, Shashi Bhushan; Karatüm, Onuralp; Doğru-Yüksel, Itır Bakış; Jalali, Houman Bahmani; Sadeghi, Sadra; Dikbaş, Uğur Meriç; Kavaklı, İbrahim Halil; Nizamoğlu, Sedat; PhD Student; Researcher; PhD Student; PhD Student; Master Student; Faculty Member; Faculty Member; Department of Molecular Biology and Genetics; Department of Electrical and Electronics Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Sciences; College of Engineering; N/A; N/A; N/A; N/A; N/A; N/A; N/A; 40319; 130295Efficient transduction of optical energy to bioelectrical stimuli is an important goal for effective communication with biological systems. For that, plasmonics has a significant potential via boosting the light-matter interactions. However, plasmonics has been primarily used for heat-induced cell stimulation due to membrane capacitance change (i.e., optocapacitance). Instead, here, we demonstrate that plasmonic coupling to photocapacitor biointerfaces improves safe and efficacious neuromodulating displacement charges for an average of 185% in the entire visible spectrum while maintaining the faradic currents below 1%. Hot-electron injection dominantly leads the enhancement of displacement current in the blue spectral window, and the nanoantenna effect is mainly responsible for the improvement in the red spectral region. The plasmonic photocapacitor facilitates wireless modulation of single cells at three orders of magnitude below the maximum retinal intensity levels, corresponding to one of the most sensitive optoelectronic neural interfaces. This study introduces a new way of using plasmonics for safe and effective photostimulation of neurons and paves the way toward ultrasensitive plasmon-assisted neurostimulation devices.Publication Open Access Computational screening of MOF-based mixed matrix membranes for CO2/N2 separations(Hindawi, 2016) Department of Chemical and Biological Engineering; Keskin, Seda; Sümer, Zeynep; Master Student; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 40548; N/AAtomically detailed simulations were used to examine CO2/N-2 separation potential of metal organic framework- (MOF-) based mixed matrix membranes (mmms) in this study. Gas permeability and selectivity of 700 new mmms composed of 70 different mofs and 10 different polymers were calculated for CO2/N-2 separation. This is the largest number of MOF-based mmms for which computational screening is done to date. Selecting the appropriate mofs as filler particles in polymers resulted in mmms that have higher CO2/N-2 selectivities and higher CO2 permeabilities compared to pure polymer membranes. We showed that, for polymers that have low CO2 permeabilities but high CO2 selectivities, the identity of the MOF used as filler is not important. All mofs enhanced the CO2 permeabilities of this type of polymers without changing their selectivities. Several MOF-based mmms were identified to exceed the upper bound established for polymers. The methods we introduced in this study will create many opportunities to select the MOF/polymer combinations with useful properties for CO2 separation applications.Publication Open Access High-yield production of biohybrid microalgae for on-demand cargo delivery(Wiley, 2020) Akolpoğlu, Mukrime Birgul; Bozüyük, Uğur; Ceylan, Hakan; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; Kızılel, Seda; Doğan, Nihal Olcay; Sitti, Metin; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; College of Engineering; Graduate School of Sciences and Engineering; School of Medicine; 28376; N/A; 297104Biohybrid microswimmers exploit the swimming and navigation of a motile microorganism to target and deliver cargo molecules in a wide range of biomedical applications. Medical biohybrid microswimmers suffer from low manufacturing yields, which would significantly limit their potential applications. In the present study, a biohybrid design strategy is reported, where a thin and soft uniform coating layer is noncovalently assembled around a motile microorganism.Chlamydomonas reinhardtii(a single-cell green alga) is used in the design as a biological model microorganism along with polymer-nanoparticle matrix as the synthetic component, reaching a manufacturing efficiency of approximate to 90%. Natural biopolymer chitosan is used as a binder to efficiently coat the cell wall of the microalgae with nanoparticles. The soft surface coating does not impair the viability and phototactic ability of the microalgae, and allows further engineering to accommodate biomedical cargo molecules. Furthermore, by conjugating the nanoparticles embedded in the thin coating with chemotherapeutic doxorubicin by a photocleavable linker, on-demand delivery of drugs to tumor cells is reported as a proof-of-concept biomedical demonstration. The high-throughput strategy can pave the way for the next-generation generation microrobotic swarms for future medical active cargo delivery tasks.Publication Open Access Reply to comment on "database for CO2 separation performances of MOFs based on computational materials screening"(American Chemical Society (ACS), 2019) Department of Chemical and Biological Engineering; Altıntaş, Çiğdem; Velioğlu, Sadiye; Keskin, Seda; Researcher; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; N/A; 200650; 40548Publication Open Access Combining machine learning and molecular simulations to unlock gas separation potentials of MOF membranes and MOF/polymer MMMs(American Chemical Society (ACS), 2022) Department of Chemical and Biological Engineering; Harman, Hilal Dağlar; Keskin, Seda; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 40548Due to the enormous increase in the number of metal-organic frameworks (MOFs), combining molecular simulations with machine learning (ML) would be a very useful approach for the accurate and rapid assessment of the separation performances of thousands of materials. In this work, we combined these two powerful approaches, molecular simulations and ML, to evaluate MOF membranes and MOF/polymer mixed matrix membranes (MMMs) for six different gas separations: He/H-2, He/N-2, He/CH4, H-2/N-2, H-2/CH4, and N-2/CH4. Single-component gas uptakes and diffusivities were computed by grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, respectively, and these simulation results were used to assess gas permeabilities and selectivities of MOF membranes. Physical, chemical, and energetic features of MOFs were used as descriptors, and eight different ML models were developed to predict gas adsorption and diffusion properties of MOFs. Gas permeabilities and membrane selectivities of 5249 MOFs and 31,494 MOF/polymer MMMs were predicted using these ML models. To examine the transferability of the ML models, we also focused on computer-generated, hypothetical MOFs (hMOFs) and predicted the gas permeability and selectivity of 1000 hMOF/polymer MMMs. The ML models that we developed accurately predict the uptake and diffusion properties of He, H-2, N-2, and CH(4 )gases in MOFs and will significantly accelerate the assessment of separation performances of MOF membranes and MOF/polymer MMIMs. These models will also be useful to direct the extensive experimental efforts and computationally demanding molecular simulations to the fabrication and analysis of membrane materials offering high performance for a target gas separation.Publication Open Access High-throughput computational screening of the metal organic framework database for CH4/H-2 separations(American Chemical Society (ACS), 2018) Eruçar, İlknur; Department of Chemical and Biological Engineering; Altıntaş, Çiğdem; Keskin, Seda; Researcher; Department of Chemical and Biological Engineering; College of Engineering; N/A; 40548Metal organic frameworks (MOFs) have been considered as one of the most exciting porous materials discovered in the last decade. Large surface areas, high pore volumes, and tailorable pore sizes make MOFs highly promising in a variety of applications, mainly in gas separations. The number of MOFs has been increasing very rapidly, and experimental identification of materials exhibiting high gas separation potential is simply impractical. High throughput computational screening studies in which thousands of MOFs are evaluated to identify the best candidates for target gas separation is crucial in directing experimental efforts to the most useful materials. In this work, we used molecular simulations to screen the most complete and recent collection of MOFs from the Cambridge Structural Database to unlock their CH4/H-2 separation performances. This is the first study in the literature, which examines the potential of all existing MOFs for adsorption-based CH4/H-2 separation. MOFs (4350) were ranked based on several adsorbent evaluation metrics including selectivity, working capacity, adsorbent performance score, sorbent selection parameter, and regenerability. A large number of MOFs were identified to have extraordinarily large CH4/H-2 selectivities compared to traditional adsorbents such as zeolites and activated carbons. We examined the relations between structural properties of MOFs such as pore sizes, porosities, and surface areas and their selectivities. Correlations between the heat of adsorption, adsorbility, metal type of MOFs, and selectivities were also studied. On the basis of these relations, a simple mathematical model that can predict the CH4/H-2 selectivity of MOFs was suggested, which will be very useful in guiding the design and development of new MOFs with extraordinarily high CH4/H-2 separation performances.Publication Open Access Application of MD simulations to predict membrane properties of MOFs(Hindawi, 2015) Department of Chemical and Biological Engineering; Adatoz, Elda Beruhil; Keskin, Seda; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 40548Metal organic frameworks (MOFs) are a new group of nanomaterials that have been widely examined for various chemical applications. Gas separation using MOF membranes has become an increasingly important research field in the last years. Several experimental studies have shown that thin-film MOF membranes can outperform well known polymer and zeolite membranes due to their higher gas permeances and selectivities. Given the very large number of available MOF materials, it is impractical to fabricate and test the performance of every single MOF membrane using purely experimental techniques. In this study, we used molecular simulations, Monte Carlo and Molecular Dynamics, to estimate both single-gas and mixture permeances of MOF membranes. Predictions of molecular simulations were compared with the experimental gas permeance data of MOF membranes in order to validate the accuracy of our computational approach. Results show that computational methodology that we described in this work can be used to accurately estimate membrane properties of MOFs prior to extensive experimental efforts.Publication Open Access Composites of porous materials with ionic liquids: synthesis, characterization, applications, and beyond(Elsevier, 2022) Department of Chemical and Biological Engineering; Durak, Özce; Zeeshan, Muhammad; Habib, Nitasha; Gülbalkan, Hasan Can; Alsuhile, Ala Abdulalem Abdo Moqbel; Çağlayan, Hatice Pelin; Öztulum, Samira Fatma Kurtoğlu; Zhao, Yuxin; Haşlak, Zeynep Pınar; Uzun, Alper; Keskin, Seda; PhD Student; PhD Student; Faculty Member; 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 Engineering; Graduate School of Sciences and Engineering; N/A; N/A; N/A; N/A; N/A; N/A; N/A; N/A; N/A; 59917; 40548Modification of the physicochemical properties of porous materials by using ionic liquids (ILs) has been widely studied for various applications. The combined advantages of ILs and porous materials provide great potential in gas adsorption and separation, catalysis, liquid-phase adsorption and separation, and ionic conductivity owing to the superior performances of the hybrid composites. In this review, we aimed to provide a perspective on the evolution of IL/porous material composites as a research field by discussing several different types of porous materials, including metal organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, and carbonaceous-materials. The main challenges and opportunities in synthesis methods, characterization techniques, applications, and future opportunities of IL/porous materials are discussed in detail to create a road map for the area. Future advances of the field addressed in this review will provide in-depth insights into the design and development of these novel hybrid materials and their replacement with conventional materials.Publication Open Access Parameters influencing gene delivery efficiency of PEGylated chitosan nanoparticles: experimental and modeling approach(Wiley, 2022) Bozüyük, Uğur; Erkoç, Pelin; Karacakol, Alp Can; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; Kızılel, Seda; Sitti, Metin; Önder, Tuğba Bağcı; Doğan, Nihal Olcay; Cingöz, Ahmet; Şeker-Polat, Fidan; Nazeer, Muhammad Anwaar; Faculty Member; Faculty Member; PhD Student; PhD Student; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; College of Engineering; School of Medicine; 28376; 297104; 184359; N/A; N/A; N/A; N/AExperimentation of nanomedicine is labor-intensive, time-consuming, and requires costly laboratory consumables. Constructing a reliable mathematical model for such systems is also challenging due to the difficulties in gathering a sufficient number of data points. Artificial neural networks (ANNs) are indicated as an efficient approach in nanomedicine to investigate the cause-effect relationships and predict output variables. Herein, an ANN is adapted into plasmid DNA (pDNA) encapsulated and PEGylated chitosan nanoparticles cross-linked with sodium tripolyphosphate (TPP) to investigate the effects of critical parameters on the transfection efficiencies of nanoparticles. The ANN model is developed based on experimental results with three independent input variables: 1) polyethylene glycol (PEG) molecular weight, 2) PEG concentration, and 3) nanoparticle concentration, along with one output variable as a percentage of green fluorescent protein (GFP) expression, which refers to transfection efficiency. The constructed model is further validated with the leave-p-out cross-validation method. The results indicate that the developed model has good prediction capability and is influential in capturing the transfection efficiencies of different nanoparticle groups. Overall, this study reveals that the ANN could be an efficient tool for nanoparticle-mediated gene delivery systems to investigate the impacts of critical parameters in detail with reduced experimental effort and cost.