Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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  • Article
    3D Magnetic Nanocomposite Aerogel (3D-MANCA) for Humidity Sensing and Dye Adsorption Applications
    (Institute of Physics, 2026) Shah, N.; Tetik, H.; Lin, D.
    Introducing magnetic properties to aerogels not only opens new application areas but also enhances their performance in various applications. Herein, we report a novel 3D magnetic agar nanocomposite aerogel (3D-MANCA) with outstanding characteristics such as high porosity, magnetic property, rapid swelling behavior, and a unique stimuli-driven electrical conductivity. Agar and nanocellulose mixture were selected as the matrix material, while magnetic Fe<inf>3</inf>O<inf>4</inf> nanoparticles, CuO nanoparticles, and graphene nanopowder were incorporated as functional additives. 3D-MANCA obtained after a uni-directional freeze casting process exhibited a highly-ordered microporosity. It showed excellent magnetic properties and methylene-blue adsorption capability and a great performance as humidity sensor. © 2026 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.
  • Conference Object
    A Comparative Study of Attention-Augmented YOLO Architectures for Defect Detection in Fused Deposition Modelling
    (Institute of Electrical and Electronics Engineers Inc., 2025) Cezayirli, H.; Tetik, H.; Dede, M.I.C.; Phone, W.L.; Alkan, B.
    Additive manufacturing (AM), particularly fused deposition modelling (FDM), facilitates the fabrication of complex geometries with increasing flexibility and efficiency. Ensuring consistent print quality in FDM processes necessitates the development of accurate defect detection mechanisms. Attention-augmented YOLO (You Only Look Once) models have emerged as a promising solution for addressing this challenge. In this study, we systematically benchmark and evaluate the performance of YOLO architectures enhanced with attention mechanisms within the context of FDM 3D printing applications. The models were trained and evaluated using representative defect datasets. The attention-augmented models demonstrate improved detection performance. © 2025 IEEE.