Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Robust CVD Polymer Encapsulation for Thermally and Chemically Resistant Fluorescent Sensor Nanoprobes(Elsevier Ltd, 2026) Karabıyık, M.; Cihanoğlu, G.; Ebil, Ö.Semiconductor quantum dots (QDs) are attractive fluorophores for sensor applications due to their narrow emission bandwidths and high photostability; however, their performance is often limited by insufficient chemical and thermal durability under operating conditions. In this study, a solvent-free encapsulation strategy based on initiated chemical vapor deposition (iCVD) is proposed to enhance the stability of QD-based sensor nanoprobes. Cross-linked poly (glycidyl methacrylate-co-ethylene glycol dimethacrylate) (ECOP) thin films were conformally deposited as encapsulation layers onto CdTe QD-functionalized poly(GMA) sensor surfaces. The encapsulated nanoprobes were evaluated under chemically aggressive environments (water, salt water, toluene, and sulfuric acid) and elevated temperatures. Following exposure to aggressive solvents, both the polymer film thickness variation and QD fluorescence intensity change remained below 10 %, confirming the robustness of the cross-linked network. Also, thermal durability tests showed stable fluorescence performance after annealing at 250 °C, with structural and optical changes remaining within the accepted 10 % threshold. The results demonstrate that coatings deposited using iCVD exhibit conformal coverage and enhanced stability. This enables reliable protection of QD-based sensor nanoprobes without compromising optical performance. This study presents a promising method to extend the operational lifetime and environmental durability of QD-integrated sensor platforms by using chemically and thermally stable polymer encapsulation. © 2026 Elsevier LtdArticle Influence of Fluorine on Structural and Electrical Properties of VO2 Thin Films Deposited by Magnetron Sputtering(Elsevier Ltd, 2025) Akyurek, Bora; Cantas, Ayten; Demirhan, Yasemin; Ozyuzer, Lutfi; Aygun, GulnurThis study investigates whether fluorine-based thermal gel used during electrical measurements of vanadium oxide (VO2) films influences the structural, morphological, or compositional integrity of the films. High-quality VO2 films with a resistance ratio change of about 10(4) for metal-insulator transition were deposited by magnetron sputtering. During electrical characterization, VO2 film was heated from room temperature to similar to 370 K with a fluorine-based thermal gel usage to achieve better heat contact between the film and substrate holder. Structural and chemical properties were assessed through XRD, Raman, XPS, SEM, and energy dispersive spectroscopy imaging. XRD revealed diffraction peaks consistent with monoclinic VO2 confirming that the crystal lattice remains the same although fluorine based thermal gel was used. Raman spectra exhibited vibrational modes indicating that the phonon structure of VO2 was preserved despite fluorine gel usage. XPS results showed only a minor F 1s signal (2.8%) limited only to the film surface. SEM and EDS analyses further confirmed that surface morphology and elemental composition remained belonging to VO2 film. These findings demonstrate that the usage of fluorine-based thermal gel results in only a minimal surface interaction, thereby preserving intrinsic material properties of VO2 and supporting a potential usage for future device fabrication applications.Article Citation - WoS: 3Citation - Scopus: 3On Digital Twins in Bioprocessing: Opportunities and Limitations(Elsevier Ltd, 2025) Shariatifar, Mehrdad; Rizi, Mohammadsadegh Salimian; Sotudeh-Gharebagh, Rahmat; Zarghami, Reza; Mostoufi, NavidIntegrating Digital Twins (DTs) in bioprocessing has become a prominent focus within the industry. Despite the challenges associated with implementing this technology in the field, the bioprocessing sector is interested in utilizing it. This is due to its potential to enhance process efficiency and overall profitability. The adoption of DTs is driven by the prospect of online monitoring, control, and optimization, enabling the products with precise and desired characteristics. To realize this objective, researchers propose a novel strategy for implementing DTs in bioprocessing. This involves the development of a hybrid model that combines first principal models and Machine Learning (ML) algorithms. This approach effectively addresses the limitations of previous methods and establishes a closed control loop system, continuously monitoring the system and adjusting input variables to achieve optimal outcomes. This study comprehensively explores various aspects of DTs. Firstly, it discusses the concept and characteristics of DTs, along with an examination of the advantages and challenges associated with their implementation. Secondly, it comprehensively analyzes key factors that directly influence DT implementation, including sensors, data collection, and models. Thirdly, it reviews the implications of Digital Solutions (DS) and DT in downstream and upstream bioprocessing. By providing theories, case studies, and practical frameworks, this work seeks to motivate both researchers and industry practitioners to adopt DT methodologies, thereby facilitating the emergence of enhanced precision, operational efficiency, and economic viability within biomanufacturing.Article Novel Neural Style Transfer Based Data Synthesis Method for Phase-Contrast Wound Healing Assay Images(Elsevier Ltd, 2024) Erdem,Y.S.; Iheme,L.O.; Uçar,M.; Özuysal,Ö.Y.; Balıkçı,M.; Morani,K.; Ünay,D.Recent advancements in the field of image synthesis have led to the development of Neural Style Transfer (NST) and Generative Adversarial Networks (GANs) which have proven to be powerful tools for data augmentation and realistic data generation. While GANs have been widely used for both data augmentation and generation, NST has not been employed for data generation tasks. Nonetheless, the simpler structure of NST compared to GANs makes it a promising alternative. In this research, we introduce an NST-based method for data generation, which to the best of our knowledge, is the first of its kind. By taking advantage of simplified architecture of NST models attributed to the utilization of a real image as the style input, our method enhances performance in data generation tasks under limited resource conditions. Additionally by utilizing patch-based training and high-resolution inference process high quality images are synthesized with limited resources. Furthermore multi-model and noised input is utilized for increased diversity with the novel NST-based data generation approach. Our proposed method utilizes binary segmentation maps as the condition input, representing the cell and wound regions. We evaluate the performance of our proposed NST-based method and compare it with a modified and fine-tuned conditional GAN (C-GAN) methods for the purpose of conditional generation of phase-contrast wound healing assay images. Through a series of quantitative and qualitative analyses, we demonstrate that our NST-based method outperforms the modified C-GAN while utilizing fewer resources. Additionally, we show that our NST-based method enhances segmentation performance when used as a data augmentation method. Our findings provide compelling evidence regarding the potential of NST for data generation tasks and its superiority over traditional GAN-based methods. The NST for data generation method was implemented in Python language and will be accessible at https://github.com/IDU-CVLab/NST_for_Gen under the MIT licence. © 2024 Elsevier LtdArticle Citation - WoS: 2Citation - Scopus: 4Multi-Scale Analysis of the Adhesive Bonding Behavior of Laser Surface-Treated Carbon Fiber Reinforced Polymer Composite Structures(Elsevier Ltd, 2024) Nuhoglu,K.; Aktas,E.; Tanoglu,M.; Barisik,M.; Esenoglu,G.; Martin,S.; Iris,M.E.Laser surface treatment has considerable potential to provide high-quality adhesive-joining of carbon-fiber-reinforced polymer (CFRP) composites by removing contaminants and the top polymer layer and increasing the surface roughness without damaging the fibers. Yet, predicting the failure strength and mechanism of the laser surface-treated adhesively bonded joints under static and cyclic loads is important to designing reliable structures. In this study, a multi-scale Finite Element Analysis (FEA) of the adhesively bonded CFRP composite structures was developed to accurately predict the failure load and damage growth. Numerical simulations of the single lap joint (SLJ) specimen was executed, employing the cohesive zone modeling (CZM) technique between adjacent surfaces to simulate the bonding behavior of the secondary bonded CFRP parts. Using the homogenization procedure, the micro-scale simulation of the contact region of the laser-treated adherent surface and adhesive was performed to extract traction separation law (TSL) parameters. The mechanical interlocking contribution of the laser surface treatment was imported to the macro-scale FEA, analyzing the representative volume element (RVE) of the bonding interface region. We presented that the multi-scale analysis estimated the experimentally measured mechanical behaviour, strength values, and failure modes successfully with a negligible error (7 %). © 2024 Elsevier LtdArticle Citation - WoS: 19Citation - Scopus: 21Multi-Objective Evolutionary Optimization of Photovoltaic Glass for Thermal, Daylight, and Energy Consideration(Elsevier Ltd, 2023) Taşer,A.; Kazanasmaz,T.; Kundakcı Koyunbaba,B.; Durmuş Arsan,Z.The potential of fenestration systems is increased by incorporating photovoltaic technology into windows. This recently developed technology enhances the ability to generate energy from the building façade, improve the thermal and daylight performance of buildings, and visual comfort of occupants. Integrating an evolutionary optimization algorithm into this technology is one of the possible sustainable solutions to enhance building performance and minimize environmental impact. This paper uses a genetic evolutionary optimization algorithm to explore the optimum performance of photovoltaic glass in an architecture studio regarding annual energy consumption, energy generation, and daylight performance. Design variables include a window-to-wall ratio (i.e., window size and location) and amorphous-silicon thin-film solar cell transparency to generate optimum Pareto-front solutions for the case building. Optimization objectives are minimizing annual thermal (i.e., heating and cooling) loads and maximizing Spatial Daylight Autonomy. Optimized results of low-E semi-transparent amorphous-silicon photovoltaic glass applied on the façade show that the spatial daylight autonomy is increased to 82% with reduced glare risk and higher visual comfort for the occupants. Photovoltaic glass helped reduce the selected room's seasonal and annual lighting loads by up to 26.7%. Lastly, compared to non-optimized photovoltaic glass, they provide 23.2% more annual electrical energy. © 2023 International Solar Energy Society