Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Rice-Like, Hollow, and Rhombohedral Nano-Calcite Synthesis by Carbonization(Elsevier, 2026) Özdemir, Ekrem; Kılıç Özdemir, Sevgi; Ozdemir, Ekrem; 03.02. Department of Chemical Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyControlling the morphology and size of calcium carbonate (CaCO3) remains an essential challenge in the production of high-performance fillers and advanced functional materials. Here, we report a continuous carbonization strategy that enables the synthesis of monodisperse nano-calcite particles with tunable rice-like, hollow, and rhombohedral morphologies through precise control of CO2 dissolution into a flowing Ca(OH)2 solution under diffusion-limited conditions. A two-stage reactor was designed to decouple nucleation and growth by separating the gas-liquid interaction zone from a stabilization tank. pH and conductivity analyses revealed that crystallization is primarily governed by the CO2 dissolution kinetics rather than mixing intensity in the stabilization tank. SEM and XRD analyses demonstrate a distinct crystallization sequence such that initial formation of rice-like calcite, subsequent development of hollow nanoparticles through selective tip dissolution, and final transformation into rhombohedral calcite via dissolution-reprecipitation mechanism. The method provides a reproducible, template-free route for fabricating hollow CaCO3 nanoparticles, overcoming limitations of bubbletemplating and additive-mediated techniques. This scalable process provides a robust foundation for producing high-surface-area CaCO3 nanomaterials which have potential applications in thin films, ceramics, protective coatings, lightweight composites, thermal/acoustic insulation, adsorption, and catalysis, where tailored particle morphology and size can significantly enhance performance.Article Citation - WoS: 5Citation - Scopus: 4Identifying 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 TechnologyThere 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.