Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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Access Right: Embargoed AccessAuthor: search.filters.author.Sözmen, Alper Baran

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  • Article
    Citation - Scopus: 3
    Development of Chrono-Spectral Gold Nanoparticle Growth Based Plasmonic Biosensor Platform
    (Elsevier, 2024) Sözmen, Alper Baran; Elveren, Beste; Erdoğan, Duygu; Mezgil, Bahadır; Baştanlar, Yalın; Yıldız, Ümit Hakan; Arslan Yıldız, Ahu
    Plasmonic sensor platforms are designed for rapid, label-free, and real-time detection and they excel as the next generation biosensors. However, current methods such as Surface Plasmon Resonance require expertise and well-equipped laboratory facilities. Simpler methods such as Localized Surface Plasmon Resonance (LSPR) overcome those limitations, though they lack sensitivity. Hence, sensitivity enhancement plays a crucial role in the future of plasmonic sensor platforms. Herein, a refractive index (RI) sensitivity enhancement methodology is reported utilizing growth of gold nanoparticles (GNPs) on solid support and it is backed up with artificial neural network (ANN) analysis. Sensor platform fabrication was initiated with GNP immobilization onto solid support; immobilized GNPs were then used as seeds for chrono-spectral growth, which was carried out using NH2OH at varied incubation times. The response to RI change of the platform was investigated with varied concentrations of sucrose and ethanol. The detection of bacteria E.coli BL21 was carried out for validation as a model microorganism and results showed that detection was possible at 102 CFU/ml. The data acquired by spectrophotometric measurements were analyzed by ANN and bacteria classification with percentage error rates near 0% was achieved. The proposed LSPR-based, label-free sensor application proved that the developed methodology promises utile sensitivity enhancement potential for similar sensor platforms. © 2024 The Author(s)
  • Article
    Citation - WoS: 8
    Citation - Scopus: 9
    Optimization of the Algal Species Chlorella Miniata Growth: Mathematical Modelling and Evaluation of Temperature and Light Intensity Effects
    (Elsevier, 2022) Sözmen, Alper Baran; Ata, Ayça; Övez, Bikem
    Growth of Chlorella miniata, a green microalga was investigated during this study under various temperature and light intensity values with the purpose of determining growth rate changes of the microalgae with cultivation parameters, experiments were carried out using airlift photobioreactors with a study volume of 6 L. Culturing conditions were between 66 and 385 μmol photon m−2 s−1 and between 14 and 30 °C for light intensity and ambient temperature, respectively. Acquired data were then used to test various mathematical models for coherency with experimental results. Aiba Model for light intensity and Skewed Normal Distribution Model for temperature parameters performed superior compared to the rest of the mathematical models used during the study. Utilizing both mathematical models a novel model was deduced to express effects of both light intensity and temperature parameters in combination on algal growth. Then the developed model was used to calculate the optimum growth condition of the species. The proposed mathematical model showed good coherency with experimental data and an average relative error of 1.97% for both temperature and light intensity effects on algal growth. The theoretical optimum temperature and light intensity for the maximum specific growth rate were calculated to be 22.43 °C and 291.5 μmol photon m−2 s−1 respectively.