Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    A Comparative Study on Experimental and FEA-Based Simulation of Dry Sliding Wear Behavior of Boronized AISI 304 Stainless Steel at Elevated Temperatures
    (Pleiades Publishing Ltd, 2025) Güden, Mustafa; Kucuk, Yilmaz; Khosravi, Farshid; Gunen, Ali; Karakas, Mustafa Serdar; Guden, Mustafa; 01. Izmir Institute of Technology; 03. Faculty of Engineering; 03.10. Department of Mechanical Engineering
    In this study, the influence of boronizing on the high-temperature wear behavior of AISI 304 was examined experimentally and with FEA simulation. Boronizing, conducted at 950 degrees C for 3 h using the powder-pack boronizing technique, showed an approximately 7-fold increase in hardness compared to untreated sample. Boride layer characterization was performed using XRD, SEM, and EDS line analyses. Wear tests were performed at ambient temperatures of 25, 250, and 500 degrees C. While the wear rates of the untreated sample increased dramatically with increasing temperature, those of the boronized samples were significantly limited. FEA simulation using the Johnson-Cook fracture model demonstrated a high degree of consistency with the experimental wear profiles and this alignment enables reliable wear predictions. The oxide layer formation was observed on the worn surface of boronized samples during the tests at elevated temperatures, resulting in less plastic deformation.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 4
    Identifying Factors Controlling Cellular Uptake of Gold Nanoparticles by Machine Learning
    (TAYLOR & FRANCIS LTD, 2023) Bilgi, Eyüp; Bilgi, Eyüp; Öksel Karakuş, Ceyda; 03.01. Department of Bioengineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    There is strong interest to improve the therapeutic potential of gold nanoparticles (GNPs) while ensuring their safe development. The utility of GNPs in medicine requires a molecular-level understanding of how GNPs interact with biological systems. Despite considerable research efforts devoted to monitoring the internalisation of GNPs, there is still insufficient understanding of the factors responsible for the variability in GNP uptake in different cell types. Data-driven models are useful for identifying the sources of this variability. Here, we trained multiple machine learning models on 2077 data points for 193 individual nanoparticles from 59 independent studies to predict cellular uptake level of GNPs and compared different algorithms for their efficacies of prediction. The five ensemble learners (Xgboost, random forest, bootstrap aggregation, gradient boosting, light gradient boosting machine) made the best predictions of GNP uptake, accounting for 80-90% of the variance in the test data. The models identified particle size, zeta potential, GNP concentration and exposure duration as the most important drivers of cellular uptake. We expect this proof-of-concept study will foster the more effective use of accumulated cellular uptake data for GNPs and minimise any methodological bias in individual studies that may lead to under- or over-estimation of cellular internalisation rates.