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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Hydrogels for 3D frameworks(CRC Press, 2023) Department of Chemical and Biological Engineering; Karaoğlu, İsmail Can; Yalçın, Esra; Gülzar, Ayesha; Göktan, Işılay; Kızılel, Seda; Department of Chemical and Biological Engineering; ; Graduate School of Sciences and Engineering; College of Engineering;Hydrogels are 3D polymer networks that can absorb large amounts of water, making them a promising construct for tissue engineering applications. This chapter focuses on the use of hydrogels in vascularization, ophthalmology-related diseases, and pancreatic islet transplantation. Hydrogels can support blood vessel growth, known as vascularization. This is an important aspect of tissue engineering, as the formation of new blood vessels is necessary for supplying nutrients and oxygen to the engineered tissue. In the field of ophthalmological tissue engineering, hydrogels can be used as a scaffold to support the growth of limbal stem cells to repair or replace damaged corneal tissue. They can help regenerate the cornea. In addition, hydrogels can be used in islet transplantation, which is a promising approach for treating diabetes. In this context, hydrogels can create a protective environment for the transplanted islets, helping them to survive and function properly in the body. By limiting the immune response, hydrogels can help to prevent the body from rejecting the engineered tissue, improving the chances of success for transplantation. In conclusion, hydrogels are promising materials for tissue engineering, particularly in ophthalmological diseases, islet transplantation, and vascularization. By providing a supportive environment for the growth of cells and tissues, hydrogels can help improve the success of these therapies, offering new hope for patients with various conditions. © 2024 selection and editorial matter, Ram K. Gupta and Anuj Kumar;individual chapters, the contributors.Publication Metadata only Machine learning based modeling and optimization of an industrial thermal cracking furnace(Elsevier B.V., 2024) Kaya, Gizem Kuşoğlu; Savran, Onur; Department of Chemical and Biological Engineering; Aydın, Erdal; Duvanoğlu, Melike; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of Engineering; Graduate School of Sciences and EngineeringMachine learning methods can capture the distinctive characteristics of a system without any prior knowledge of the process given enough actual data. In addition, they are well suited to represent systems that are complicated for first principles modeling and have many unmeasured disturbances. Accordingly, data-based modeling for the thermal cracking furnace is a promising study using actual process data set and various machine learning methods. The study's focus is on the machine learning prediction of time-series Controlled Variables (CV), which is a prerequisite for using an Advanced Process Control (APC) system in a petrochemical plant. The most crucial component of an APC system is the prediction of the controlled variables and the adjustment of those anticipated values to bring them within the user's chosen range (Lee et al., 2023). Predicting the controlled variables is our main goal in this investigation. We specifically used a variety of machine learning approaches to forecast future controlled variables by utilizing historical controlled variables. In this study, the cycle time of the furnace of a visbreaker unit and the temperature of the hottest zone of the furnace are modeled using different machine learning methods such as Support Vector Machines, Multiple Linear Regression, Decision Tree, Random Forest, and Artificial Neural Networks. Although the Random Forest model is good at predicting temperature and remaining day for the shut-down time, ANN model is used for process optimization purposes, by incorporating it in the fitness function of the genetic algorithm. When using a genetic algorithm (GA) to optimize a model for a specific task, the choice of the model as the fitness function is crucial. The fitness function evaluates how well a particular solution (set of model parameters or hyperparameters) performs the task at hand. The reason for using an Artificial Neural Network (ANN) as a fitness function in a genetic algorithm instead of a Random Forest (RF) is search space and differentiable nature of the ANN structure. Having estimated the cycle time by training the machine learning models, the inverse problem is attempted to solve such as calculating the optimal values of the features (controlled variables) for maximizing the operation time of the process within certain limits. This optimization problem is solved by sampling-based optimization methods formulating the trained machine learning models as fitness function. In this way, the necessary manipulated variables will be adjusted by the controller so that the unit can operate in the most efficient way.Publication Metadata only Challenges with atomically dispersed supported metal catalysts: controlling performance, improving stability, and enhancing metal loading(Elsevier, 2023) Kurtoğlu-Öztulum, Samira Fatma; Uzun, Alper; Department of Chemical and Biological Engineering; 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 EngineeringAtomically dispersed supported metal catalysts offer opportunities of maximum utilization of expensive noble metals and provide unprecedented catalytic properties. Thus, these novel materials have received tremendous attention, especially in the last decade. Notwithstanding their advantages and such rapidly growing interest, these novel materials face various challenges, which need to be overcome to make them industrially viable. One of these challenges is the limited ability to control their catalytic properties. Changing the ligand environment, which also includes the support, varying the metal nuclearity, and the use of promoters (also including ionic liquid sheets) have shown to offer broad opportunities for tuning the catalytic performance. The other challenges are mostly related with the limited stability of the active species under reaction conditions, which also limits the metal loadings on the support surfaces. Control of electronic structure on the metal sites and the use of functional groups on support surfaces have been shown to be effective in this direction. In this chapter, some of the recent approaches aiming at overcoming these challenges related with the atomically dispersed supported metal catalysts are presented.Publication Metadata only Determination of the correspondence between mobility (rigidity) and conservation of the interface residues(IEEE, 2010) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Makinacı, Gözde Kar; Faculty Member; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/AHot spots at protein interfaces may play specific functional roles and contribute to the stability of the protein complex. These residues are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions forming a network of interactions among themselves. Here, we investigate the organization of computational hot spots at protein interfaces. A list of proteins whose free and bound forms exist is examined. Inter-residue distances of the interface residues are compared for both forms. Results reveal that there exist rigid block regions at protein interfaces. More interestingly, these regions correspond to computational hot regions. Hot spots can be determined with an average positive predictive value (PPV) of 0.73 and average sensitivity value of 0.70 for seven protein complexes.Publication Metadata only Investigations of processing parameters that affect transparency of silica aerogels for thermal insulation systems(Amer Chemical Soc, 2014) Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Karayılan, Metin; Erkey, Can; Researcher; Faculty Member; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of Engineering; College of Engineering; N/A; 29633Publication Metadata only Design of stimuli-responsive drug delivery hydrogels synthesis and applications(Crc Press-Taylor and Francis Group, 2017) N/A; N/A; Department of Chemical and Biological Engineering; Aydın, Derya; Alipour, Mohammad; Kızılel, Seda; PhD Student; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 28376Stimuli-responsive hydrogels have become popular in medicine and Polymer science as useful 'smart' devices due to their various properties such as overall biocompatibility, high drug loading capacity, and controlled molecule delivery. By tuning the polymer side chains and degree of crosslinking, these gels may exhibit swelling/shrinking behaviour in response to environmental stimuli such as light, pH, chemicals, temperature, mechanical strain, and electrical field. Sensitivity of these hydrogels enables precise control over fundamental material properties such as physical structure, porosity, swelling behaviour, mechanical strength and drug permeability. Temperature and pH alterations are examples of physiological deviations that are commonly considered for the design of responsive hydrogels, specifically for site-specific controlled drug delivery. a class of hydrogels known as multi-responsive hydrogels can respond to more than one stimuli which make them tunable and controllable with improved biomimetic properties well-suited for controlled and site specific drug delivery. Despite all these attractive properties of stimuli-responsive hydrogels, slow response time may cause some limitations in practical applications. Reduced hydrogel thickness may decrease the response time of the gel to a stimulus; however, this may lead to mechanically fragile hydrogel structures. therefore, practical applications need significant improvement in hydrogel design to improve response time considering mechanical properties, biocompatibility, and biodegradability. This chapter highlights recent progress in the field of stimuli-responsive hydrogels, focusing primarily on drug delivery vehicles.Publication Metadata only Impregnation of Ru(cod)(tmhd)(2) into PDMS film in supercritical carbon dioxide(Trans Tech Publications Ltd, 2012) Ge, Minglan; Ding, Fuchen; Department of Chemical and Biological Engineering; Erkey, Can; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 29633Metallopolymer nanocomposites has attracted much attention recently. The impregnation of organometallic compound from the supercritical solution into the polymer matrix has several advantages. The impregnation process isotherm of bis(2,2,6,6-tetramethy1-3,5-heptanedionato) (1,5-cyclooctadiene) ruthenium (II) (Ru(cod)(tmhd)(2)) into polydimethylsiloxane (PDMS) film in supercritical carbon dioxide (scCO(2)) was investigated. The experiments for determining the isotherm were carried out at 40 degrees C and 10.34 MPa. It was found that the impregnation isotherm is linear up to the saturation concentration of the precursor in scCO(2) fluid phase. The slope of the linear curve defined equilibrium partition coefficient K provides a measure of the partitioning of Ru(cod)(tmhd)(2) between the PDMS film and scCO(2) fluid phase and it is constant under the same conditions. It showed that K is mainly govered by the density of scCO(2) and does not change much with temperature at a constant density of scCO(2).Publication Metadata only Use of gap metric for model selection in multi-model based control design: an experimental case study of PH control(Elsevier, 2000) Palazoglu, Ahmet; Romagnoli, J.A.; Galan, O.; Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526The gap metric concept is extended to multi-linear model-based control framework. The concept of distance between systems is used as a criterion to select a set of models that can explain the nonlinear plant behavior. Gap metric is used to analyze the relationships among candidate models, resulting in a reduced model set which provides enough information to design a H∞-robust controller.Publication Metadata only Structure based drug design for insulin degrading enzyme (IDE)(AICHE, 2010) Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Türkay, Metin; Kızılel, Seda; Kavaklı, İbrahim Halil; Dağlıyan, Onur; Dağyıldız, Ezgi; Çakır, Bilal; Faculty Member; Faculty Member; Faculty Member; Master Student; PhD Student; PhD Student; Department of Industrial Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 24956; 28376; 40319; N/A; N/A; N/AInsulin-degrading enzyme (IDE) is an allosteric Zn +2 metalloprotease involved in the degradation of many peptides including amyloid beta (Aβ), and insulin that play key roles in Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM), respectively. Crystal structure of IDE revealed that N-terminal of IDE has an exosite which serves as a regulation site by orientation of the substrates of IDE to the catalytic site. It is plausible to find small molecules that bind to the exosite of IDE and enhance its proteolytic activity towards different substrates. In this study, we have taken a computer-aided structure based drug design methods combined with experimental methods, one novel molecule that enhances the activity of human IDE was discovered. The novel compound, designated as D10 enhanced both IDE mediated proteolysis of substrate V and insulin degradation. This study describes the first examples of a computer-aided discovery of IDE regulators, showing that in vitro activation of this important enzyme with drug-like small molecules is attainable.Publication Metadata only Development of highly stable and luminescent aqueous CdS quantum dots with the poly(acrylic acid)/mercaptoacetic acid binary coating system(Amer Scientific Publishers, 2009) Lieberwirth, I.; Department of Chemistry; N/A; Department of Chemical and Biological Engineering; Acar, Havva Funda Yağcı; Çelebi, Serdar; Serttunalı, Nazlı İpek; Faculty Member; Master Student; Undergraduate Student; Department of Chemistry; Department of Chemical and Biological Engineering; College of Sciences; Graduate School of Sciences and Engineering; College of Engineering; 178902; N/A; N/AHighly stable and luminescent CdS quantum dots (QD) were prepared in aqueous solutions via in situ capping of the crystals with the poly(acrylic acid) (PAA) and mercaptoacetic acid (MAA) binary mixtures. The effect of reaction temperature and coating composition on the particle size, colloidal stability and luminescence were investigated and discussed in detail. CdS QDs coated with either PAA or MAA were also prepared and compared in terms of properties. CdS-MAA QDs were highly luminescent but increasing reaction temperature caused an increase in the crystal size and a significant decrease in the quantum yield (QY). Although less luminescent and bigger than CdS-MAA, CdS-PAA QDs maintained the room temperature size and QY at higher reaction temperatures. CdS-MAA QDs lacked long term colloidal stability whereas CdS-PAA QDs showed excellent stability over a year. Use of PAA/MAA mixture as a coating for CdS nanoparticles during the synthesis provided excellent stability, high QY and ability to tune the size and the color of the emission. Combination of all of these properties can be achieved only with the mixed coating. CdS coated with PAA/MAA at 40/60 ratio displayed the highest QY (50% of Rhodamine B) among the other compositions.