Chemical Engineering / Kimya Mühendisliği

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  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Automated Deep Learning Model Development Based on Weight Sensitivity and Model Selection Statistics
    (Pergamon-elsevier Science Ltd, 2025) Yalcin, Damla; Deliismail, Ozgun; Tuncer, Basak; Boy, Onur Can; Bayar, Ibrahim; Kayar, Gizem; Sildir, Hasan
    Current sustainable production and consumption processes call for technological integration with the realm of computational modeling especially in the form of sophisticated data-driven architectures. Advanced mathematical formulations are essential for deep learning approach to account for revealing patterns under nonlinear and complex interactions to enable better prediction capabilities for subsequent optimization and control tasks. Bayesian Information Criterion and Akaike Information Criterion are introduced as additional constraints to a mixed-integer training problem which employs a parameter sensitivity related objective function, unlike traditional methods which minimize the training error under fixed architecture. The resulting comprehensive optimization formulation is flexible as a simultaneous approach is introduced through algorithmic differentiation to benefit from advanced solvers to handle computational challenges and theoretical issues. Proposed formulation delivers 40% reduction, in architecture with high accuracy. The performance of the approach is compared to fully connected traditional methods on two different case studies from large scale chemical plants.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Shallow Shell Ssta63 Resin: a Rapid Approach To Remediation of Hazardous Nitrate
    (Royal Society of Chemistry, 2024) Cendik, Elif; Saygi, Mugenur; Recepoğlu, Yaşar Kemal; Arar, Ozgur
    This study examines the potential of Purolite Shallow Shell (TM) SSTA63 anion exchange resin for mitigating nitrate ion (NO3-) contamination in aqueous environments. Through systematic experimentation, including dosage optimization, pH dependency, kinetic and desorption studies, we investigate the sorption behavior and practical applications of the resin. Results indicate that the resin effectively removes NO3- ions, with maximum efficiency achieved within 10 minutes. When 0.025 g of resin was used, 75% of NO3- was removed, whereas with 0.05 g, 89% was removed, and with 0.1 g of resin, 95% was removed. At pH 1, approximately 50% of NO3- ions were removed, with removal efficiency reaching 97% between pH 4 and 10. Sorption isotherms affirm the suitability of the Langmuir model for the current investigation. The monolayer maximum sorption capacity (qmax) value was found to be 53.65 mg g-1. The resin demonstrates robust desorption capabilities using 0.1 M hydrochloric acid (HCl), effectively desorbing NO3- above 99%, indicating easy NO3- desorption and resin regeneration. The presence of coexisting ions such as chloride (Cl-), sulfate (SO42-), and phosphate (PO43-) showed a minimal impact on NO3- removal in individual binary mixtures, with efficiencies exceeding 93%, suggesting a strong selectivity of the resin towards NO3-. Purolite SSTA63 anion exchange resin exhibited a high affinity for NO3- ions, even over other competing ions, despite the general trend of ion exchange resins to favor ions with a higher atomic number and valence. Overall, this resin presents a promising solution for NO3- removal, with implications for water treatment and environmental remediation. This study explores the potential of Purolite Shallow Shell (TM) SSTA63 anion exchange resin for mitigating nitrate ion (NO3-) contamination in aqueous environments.
  • Article
    Citation - WoS: 15
    Citation - Scopus: 16
    A Machine Learning Ensemble Approach for Predicting Solar-Sensitive Hybrid Photocatalysts on Hydrogen Evolution
    (IOP Publishing, 2024) Bakır, Rezan; Orak, Ceren; Yuksel, Asli
    Hydrogen, as the lightest and most abundant element in the universe, has emerged as a pivotal player in the quest for sustainable energy solutions. Its remarkable properties, such as high energy density and zero emissions upon combustion, make it a promising candidate for addressing the pressing challenges of climate change and transitioning towards a clean and renewable energy future. In an effort to improve efficiency and reduce experimental costs, we adopted machine learning techniques in this study. Our focus turned to predictive analyses of hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO3 (GLFO), graphene-supported LaRuO3 (GLRO), and graphene-supported BiFeO3 (GBFO), examining their correlation with varying levels of pH, catalyst amount, and H2O2 concentration. To achieve this, a diverse range of machine learning models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, and AdaBoost-each bringing its strengths to the predictive modeling arena. An important step involved combining the most effective models-Random Forests, Gradient Boosting, and XGBoost-into an ensemble model. This collaborative approach aimed to leverage their collective strengths and improve overall predictability. The ensemble model emerged as a powerful tool for understanding photocatalytic hydrogen evolution. Standard metrics were employed to assess the performance of our ensemble prediction model, encompassing R squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The yielded results showcase exceptional accuracy, with R squared values of 96.9%, 99.3%, and 98% for GLFO, GBFO, and GLRO, respectively. Moreover, our model demonstrates minimal error rates across all metrics, underscoring its robust predictive capabilities and highlighting its efficacy in accurately forecasting the intricate relationships between GLFO, GBFO, and GLRO values and their influencing factors.
  • Article
    Simultaneous Topology Design and Optimization of Pde Constrained Processes Based on Mixed Integer Formulations
    (Elsevier, 2024) Ertürk, Emrullah; Deliismail, Özgün; Şıldır, Hasan
    Simultaneous topological design and optimization of complex processes that are described by partial differential equations is a challenging but promising research area. Widely adopted nested and sequential approaches are mostly applicable based on heuristic solutions, hindering the theoretical improvement potential due to decentralized decision-making in subsequent stages with a significant number of trial-and-error procedures. This study introduces a mixed integer formulation addressing the governing equations and case-dependent topological constraints at each discretization point, enabling solutions through rigorous solvers under process-related constraints and objectives. Nonlinear expressions in the formulations are further tailored using piecewise linear approximations, still representing the major nonlinear trends through a mixed-integer linear nature to favor global optimality and benefit from computational advancements, when needed. Heat and Stokes flow problems are used as case studies to demonstrate the applicability of the methodology. © 2024 Elsevier B.V.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 3
    Atomic-Scale Insights Into Carbon Dioxide Hydrogenation Over Bimetallic Iron-Cobalt Catalysts: a Density Functional Theory Study
    (MDPI, 2023) Tuncer, Dilan; Kızılkaya, Ali Can
    The conversion of carbon dioxide to fuels and chemicals is a promising long-term approach for mitigating CO2 emissions. Despite extensive experimental efforts, a fundamental understanding of the bimetallic catalytic structures that selectively produce the desired products is still lacking. Here, we report on a computational surface science approach into the effect of the Fe doping of Co(111) surfaces in relation to CO2 hydrogenation to C1 products. Our results indicate that Fe doping increases the binding strength of surface species but slightly decreases the overall catalytic activity due to an increase in the rate-limiting step of CO dissociation. FeCo(111) surfaces hinder hydrogenation reactions due to lower H coverages and higher activation energies. These effects are linked to the Lewis basic character of the Fe atoms in FeCo(111), leading to an increased charge on the adsorbates. The main effect of Fe doping is identified as the inhibition of oxygen removal from cobalt surfaces, which can be expected to lead to the formation of oxidic phases on bimetallic FeCo catalysts. Overall, our study provides comprehensive mechanistic insights related to the effect of Fe doping on the catalytic behavior and structural evolution of FeCo bimetallic catalysts, which can contribute to the rational design of bimetallic catalysts.
  • Review
    Citation - WoS: 30
    Citation - Scopus: 33
    Molecular Separation by Using Active and Passive Microfluidic Chip Designs: a Comprehensive Review
    (Wiley, 2023) Ebrahimi, Aliakbar; Didarian, Reza; Shih, Chih-Hsin; Nasseri, Behzad; Ethan Li, Yi-Chen; Shih, Steven; İçöz, Kutay; Tarım, Ergün Alperay; Akpek, Ali; Çeçen, Berivan; Bal Öztürk, Ayça; Güleç, Kadri; Tarım, Burcu Sırma; Tekin, Hüseyin Cumhur
    Separation and identification of molecules and biomolecules such as nucleic acids, proteins, and polysaccharides from complex fluids are known to be important due to unmet needs in various applications. Generally, many different separation techniques, including chromatography, electrophoresis, and magnetophoresis, have been developed to identify the target molecules precisely. However, these techniques are expensive and time consuming. “Lab-on-a-chip” systems with low cost per device, quick analysis capabilities, and minimal sample consumption seem to be ideal candidates for separating particles, cells, blood samples, and molecules. From this perspective, different microfluidic-based techniques have been extensively developed in the past two decades to separate samples with different origins. In this review, “lab-on-a-chip” methods by passive, active, and hybrid approaches for the separation of biomolecules developed in the past decade are comprehensively discussed. Due to the wide variety in the field, it will be impossible to cover every facet of the subject. Therefore, this review paper covers passive and active methods generally used for biomolecule separation. Then, an investigation of the combined sophisticated methods is highlighted. The spotlight also will be shined on the elegance of separation successes in recent years, and the remainder of the article explores how these permit the development of novel techniques. © 2023 The Authors. Advanced Materials Interfaces published by Wiley-VCH GmbH.
  • Article
    Citation - WoS: 6
    Citation - Scopus: 6
    Inverse Effects of Lanthanide Co-Doping on the Photocatalytic Hydrogen Production and Dye Degradation Activities of Cu Doped Sol-Gel Tio<sub>2</Sub>
    (Elsevier, 2023) Yurtsever, Husnu Arda; Erzin, Kubilay; Ciftcioglu, Muhsin; Yurtsever, Hüsnü Arda; Erzin, Kubilay; Çiftçioğlu, Muhsin
    Copper doped and lanthanide-copper co-doped titania powders were prepared by sol-gel technique and the effects of co-doping on the photocatalytic reduction and oxidation activities of titania were investigated in this work. Characterization studies indicated that a reduced structure was formed due to the presence of Ti3+ species in copper doped titania powder and a more stable structure was formed when lanthanides were used as co-dopants. Copper doped powder had a significantly higher activity in photocatalytic hydrogen production (1037 mu mol/g/h) than the co-doped powders (similar to 400 mu mol/g/h). The oxidation activities of co-doped powders however were determined to be about 2 times higher than that of the copper doped powder. The decrease in the reduction activity was attributed to the decrease in the number of Ti3+ sites, whereas the increase in oxidation activity was probably a result of the increase in the surface area and dye adsorption due to lanthanide co-doping.
  • Review
    Citation - WoS: 39
    Citation - Scopus: 37
    Engineered Liposomes in Interventional Theranostics of Solid Tumors
    (American Chemical Society, 2023) Kommineni, Nagavendra; Chaudhari, Ruchita; Conde, Joao; Cecen, Berivan; Chandra, Pranjal; Prasad, Rajendra; Tamburacı, Sedef
    Engineered liposomal nanoparticles have unique characteristicsas cargo carriers in cancer care and therapeutics. Liposomal theranosticshave shown significant progress in preclinical and clinical cancermodels in the past few years. Liposomal hybrid systems have not onlybeen approved by the FDA but have also reached the market level. Nanosizedliposomes are clinically proven systems for delivering multiple therapeuticas well as imaging agents to the target sites in (i) cancer theranosticsof solid tumors, (ii) image-guided therapeutics, and (iii) combinationtherapeutic applications. The choice of diagnostics and therapeuticscan intervene in the theranostics property of the engineered system.However, integrating imaging and therapeutics probes within lipidself-assembly liposome may compromise their overalltheranostics performance. On the other hand, liposomal systems sufferfrom their fragile nature, site-selective tumor targeting, specificbiodistribution and premature leakage of loaded cargo molecules beforereaching the target site. Various engineering approaches, viz., grafting,conjugation, encapsulations, etc., have been investigated to overcomethe aforementioned issues. It has been studied that surface-engineeredliposomes demonstrate better tumor selectivity and improved therapeuticactivity and retention in cells/or solid tumors. It should be notedthat several other parameters like reproducibility, stability, smoothcirculation, toxicity of vital organs, patient compliance, etc. mustbe addressed before using liposomal theranostics agents in solid tumorsor clinical models. Herein, we have reviewed the importance and challengesof liposomal medicines in targeted cancer theranostics with theirpreclinical and clinical progress and a translational overview.
  • Erratum
    Citation - WoS: 1
  • Article
    Citation - WoS: 17
    Citation - Scopus: 16
    Development of Cissus Quadrangularis-Loaded Poss-Reinforced Chitosan-Based Bilayer Sponges for Wound Healing Applications: Drug Release and in Vitro Bioactivity
    (American Chemical Society, 2023) Değer Aker, Sibel; Tamburacı, Sedef; Tıhmınlıoğlu, Funda
    Nowadays, antibiotic-loaded biomaterials have been widelyusedin wound healing applications. However, the use of natural extractshas come into prominence as an alternative to these antimicrobialagents in the recent period. Among natural sources, Cissus quadrangularis (CQ) herbal extract is usedfor treatment of bone and skin diseases in ayurvedic medicine dueto its antibacterial and anti-inflammatory effects. In this study,chitosan-based bilayer wound dressings were fabricated with electrospinningand freeze-drying techniques. CQ extract-loaded chitosan nanofiberswere coated on chitosan/POSS nanocomposite sponges using an electrospinningmethod. The bilayer sponge is designed to treat exudate wounds whilemimicking the layered structure of skin tissue. Bilayer wound dressingswere investigated with regard to the morphology and physical and mechanicalproperties. In addition, CQ release from bilayer wound dressings and in vitro bioactivity studies were performed to determinethe effect of POSS nanoparticles and CQ extract loading on NIH/3T3and HS2 cells. The morphology of nanofibers was investigated withSEM analysis. Physical characteristics of bilayer wound dressingswere determined with FT-IR analysis, swelling study, open porositydetermination, and mechanical test. The antimicrobial activity ofCQ extract released from bilayer sponges was investigated with a discdiffusion method. Bilayer wound dressings' in vitro bioactivity was examined using cytotoxicity determination, woundhealing assay, proliferation, and the secretion of biomarkers forskin tissue regeneration. The nanofiber layer diameter was obtainedin the range of 77.9-97.4 nm. The water vapor permeabilityof the bilayer dressing was obtained as 4021 to 4609 g/m(2)day, as it is in the ideal range for wound repair. The release ofthe CQ extract over 4 days reached 78-80% cumulative release.The release media were found to be antibacterial against Gram-negativeand Gram-positive bacteria. In vitro studies showedthat both CQ extract and POSS incorporation induced cell proliferationas well as wound healing activity and collagen deposition. As a result,CQ-loaded bilayer CHI-POSS nanocomposites were found as a potentialcandidate for wound healing applications.