WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 13Citation - Scopus: 14Microstructural, Mechanical, Tribological, and Corrosion Behavior of Ultrafine Bio-Degradable Mg/CeO2 Nanocomposites: Machine Learning-Based Modeling and Experiment(Elsevier Sci Ltd, 2023-12) Deka, Surja; Mozafari, Farzin; Mallick, AshisThe present study investigated the microstructural, mechanical, tribological, and corrosion behavior of near-dense and low-volume fraction magnesium-cerium dioxide (Mg/CeO2) (x = 0.5, 1, and 1.5 vol.%) nanocomposites synthesized by in-situ hot extrusion assisted powder metallurgy (PM) process. Results showed a significant improvement in wear resistance for Mg/CeO2 nanocomposite compared to monolithic Mg at varied applied loads. Microindentation tests were performed to access the Vickers microhardness homogeneity along the extrusion direction. The corrosion analysis revealed that introducing ceria nanoparticles enhanced Mg's corrosion resistance and expedited the development of an apatite layer on the surface, providing enhanced protection. A feedforward neural network and Long Short-Term Memory (LSTM) network were also developed to characterize nanocomposites' wear and corrosion behavior.Article Citation - WoS: 22Citation - Scopus: 29Automated Quantification of Immunomagnetic Beads and Leukemia Cells from Optical Microscope Images(Elsevier Sci Ltd, 2019-03) Uslu, Fatma; Icoz, Kutay; Tasdemir, Kasim; Yilmaz, BulentQuantification of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quantification of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identification and machine learning based methods were implemented to quantify targets in the images recorded by a bright field microscope. Images acquired by a 40x or a 20x objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40x objective and 79.7% for the 20x objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection. (C) 2019 Elsevier Ltd. All rights reserved.Article Citation - WoS: 9Citation - Scopus: 9A Comprehensive Experimental and Modeling Study of the Strain Rate- and Temperature-Dependent Deformation Behavior of Bio-Degradable Mg-Ceo2 Nanocomposites(Elsevier Sci Ltd, 2024-02) Deka, Surja; Mozafari, Farzin; Mallick, AshisA comprehensive study was undertaken on the temperature-dependent and strain rate-sensitive deformation behavior of near-dense low-volume fraction magnesium-cerium dioxide (Mg-CeO2) nanocomposites synthesized by powder metallurgy technique. The process involved ball milling of elemental powders -> cold compaction -> sintering in an inert atmosphere, and in-situ hot extrusion. The Mg-CeO2 nanocomposites displayed strain rate and temperature sensitivity, exhibiting higher yield strength, superior compressive characteristics, greater hardness, and improved ductility compared to pure Mg and most commercial Mg alloys. Furthermore, a thorough micro-structural investigation was conducted to characterize the distributions of ceria nanoparticles, grain refinement degree, ceria-magnesium interface, formation of deformation twins and interfacial bonding between the reinforcement and matrix. The present study has proposed two modeling approaches, the Johnson-Cook (J-C) constitutive model and a machine learning-assisted model, to predict the mechanical behavior of monolithic Mg and Mg-CeO2 nanocomposites. The models effectively explained the deformation behavior under various strain rates and temperatures.