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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of Chemical and Biological EngineeringSubject: search.filters.subject.Mechanical engineering

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    A novel smart disinfection system using 3D-printed and electrically conductive composite hydrogel
    (Springernature, 2024) 0000-0002-8316-9623; 0000-0001-6624-3505; N/A; 0000-0002-3511-3887; 0000-0003-1600-7322; Gul, Seref; Department of Mechanical Engineering; Department of Chemical and Biological Engineering; N/A; N/A; N/A; Lazoğlu, İsmail; Kavaklı, İbrahim Halil; Velioğlu, Başak; Malik, Anjum Naeem; Khan, Shaheryar Atta; Faculty Member; Faculty Member; Researcher; PhD Student; PhD Student; Manufacturing and Automation Research Center (MARC); College of Engineering; College of Engineering; N/A; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 179391; 40319; N/A; N/A; N/A
    Smart materials are ushering in the era of smart and adaptable products. Hydrogels are a distinct class of smart materials that can be 3D-printed to produce smart and active structures that can be used as sensors and actuators. The development and characterization of a 3D-printable and electrically conductive composite hydrogel, as well as its application in the development of a smart disinfection system, are discussed in this article. The developed composite hydrogel has a maximum electrical conductivity of 145 S.m-1, is stable up to 200 degrees C, and has a 3D printable rheology. Virtuous of its electrical conductivity, the composite hydrogel was used to create a smart disinfection system. Various disinfection systems have been adopted for the disinfection of contaminated surfaces; however, most of these systems require human evacuation from the surroundings due to the hazardous nature of the virucide. The proposed system is designed to disinfect contaminated surfaces on common-use equipment and is capable of real-time activation through user interaction. It employs a thermal disinfection process at 60 degrees C for 5 min and becomes ready for the next user once its temperature drops below 55 degrees C. This system consumes 1.64 Wh of energy per disinfection cycle and is suitable for scenarios with fewer than 60 user interactions in an 8-h work shift.
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    Investigation of 3-D multilayer approach in predicting the thermal behavior of 20 Ah Li-ion cells
    (Pergamon-Elsevier Science Ltd, 2019) N/A; N/A; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Alipour, Mohammad; Esen, Ekin; Kızılel, Rıza; PhD Student; PhD Student; Researcher; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 114475
    Numerous research groups have adopted a 1D single-layer cell approach to model the thermal behavior of the Li-ion battery systems. However, as the size of a Li-ion cell increases, the 1D single-layer approach is not enough to determine the thermal behavior of the high capacity batteries. In this study, a multilayer approach is proposed to consider the effects of the number of layers on the thermal behavior of the cell. 3D electrochemical-thermal model with multilayer approach is designed and temperature predictions at various discharge rates are calculated. The results are validated at 30 degrees C for various discharge rates. Thermal behavior of the single-layer and multilayer cell approaches are compared with the experimental measurements. The results show that the error of estimates is halved if multilayer approach is applied. The proposed model is also used to study the effects of the number of layers on the temperature non-uniformity of the large sized Li-ion batteries. The results showed that multilayer cell approach represents the thermal behavior of the Li-ion cell more accurately. The study is promising for the development of an efficient thermal management system with a better prediction of the potential hot spots on the single cells and battery packs.