Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - Scopus: 1Investigation of LiCoO2 Thin Films Grown Under Relatively Low Substrate Temperature for All Solid State Lithium Ion Battery Applications(Pergamon-elsevier Science Ltd, 2025) Ozcan, Polatkan; Esen, Nazlican; Cantas, Ayten; Ozyuzer, Lutfi; Ozdemir, Mehtap; Kosiel, Kamil; Aygun, GulnurThis study examines the effect of substrate temperature on the surface, structural and chemical properties of LiCoO2 (LCO) thin films deposited via magnetron sputtering. LCO thin films were grown for the purpose of being a cathode layer for all-solid-state lithium-ion batteries (ASSLIBs). Achieving crystalline LCO films at low substrate temperatures is advantageous for integration with flexible and temperature-sensitive substrates as well as minimizing energy consumption, which is highly important for industrial applications. In this work, LCO thin films were deposited on titanium-coated soda lime glass (SLG/Ti) at the substrate temperature ranging from room to 350 degrees C. Structural characterizations by XRD analyses confirmed that LCO thin films have (104) crystal orientation, which is critical for efficient lithium-ion transportation. SEM, Raman, and XPS analyses were used for further chemical and structural characterizations of grown LCO thin films. These analyses showed that LCO thin film grown at relatively low substrate temperature of 250 degrees C is a better growth condition when compared to others. Crystallization orientation (104) of LCO thin films is highly important for the potential usage of ASSLIBs technologies without any need of elevated temperatures. Moreover, results support the low-temperature adaption processes for applications like wearable electronics, offering safer and more sustainable solutions for future energy storage systems.Article Citation - WoS: 1Citation - Scopus: 1Dynamic and Stochastic Optimization of Algae Cultivation Process(Pergamon-elsevier Science Ltd, 2025) Kivanc, Sercan; Beykal, Burcu; Deliismail, Ozgun; Sildir, HasanThis study offers a realistic representation of system dynamics which accounts for light intensity, biomass, substrate, and nitrogen concentration, by employing stochastic programming techniques to account for spatial and temporal variations for algae growth. The optimization task focuses on lipid productivity and selectivity, which are crucial factors in the context of algal biofuel production. Different scenarios from likely and unlikely cases of model parameters were evaluated. Optimal initial conditions for key variables such as nitrogen, substrate, light, biomass, lipid, and surface light intensity are calculated, considering the uncertainty of the parameters as well as other governing equations. The results show that a remarkable 11.18% increase in lipid productivity compared to a reference scenario. Furthermore, in the stochastic case, our results highlight that uncertainty has a disproportionately large effect on biomass in comparison to lipid concentration, providing valuable insights into the behavior of the system under varying conditions. This provides a comprehensive exploration of the parameter uncertainty on lipid productivity and algal growth.Article Citation - WoS: 5Citation - Scopus: 5Automated Deep Learning Model Development Based on Weight Sensitivity and Model Selection Statistics(Pergamon-elsevier Science Ltd, 2025) Yalcin, Damla; Deliismail, Ozgun; Tuncer, Basak; Boy, Onur Can; Bayar, Ibrahim; Kayar, Gizem; Sildir, HasanCurrent sustainable production and consumption processes call for technological integration with the realm of computational modeling especially in the form of sophisticated data-driven architectures. Advanced mathematical formulations are essential for deep learning approach to account for revealing patterns under nonlinear and complex interactions to enable better prediction capabilities for subsequent optimization and control tasks. Bayesian Information Criterion and Akaike Information Criterion are introduced as additional constraints to a mixed-integer training problem which employs a parameter sensitivity related objective function, unlike traditional methods which minimize the training error under fixed architecture. The resulting comprehensive optimization formulation is flexible as a simultaneous approach is introduced through algorithmic differentiation to benefit from advanced solvers to handle computational challenges and theoretical issues. Proposed formulation delivers 40% reduction, in architecture with high accuracy. The performance of the approach is compared to fully connected traditional methods on two different case studies from large scale chemical plants.Article Citation - WoS: 6Citation - Scopus: 6Effect of the Synthesis Method and Particle Size on Bczt Electrocaloric Properties(Pergamon-elsevier Science Ltd, 2025) Temel, Helin; Avci, Tubanur; Okatan, M. Baris; Alkoy, Sedat; Misirlioglu, I. Burc; Mensur, EbruIn this study, the electrocaloric properties of BCZT ceramics fabricated through different processing methods: solid-state and sol-gel were investigated. The calcination process was done for BCZT powders obtained by sol-gel process at 900 degrees C for 2 h and by solid-state calcination method at 1200 degrees C for 6 h. BCZT-SG ceramics exhibited higher Delta T values, particularly at lower temperatures (similar to 0 degrees C-20 degrees C), and a stronger response to the electric field, suggesting a more efficient domain structure due to sol-gel processing. Notably, BCZT-SGH samples demonstrated the most complex and pronounced electrocaloric behavior, with dual Delta T peaks around 0 degrees C and 50 degrees C, and the highest Delta T of 2.5 K at 80 kV/cm and 50 degrees C, surpassing values in the literature. Especially, high Delta T results at 0 degrees C allows using this material in the extreme conditions. These results emphasize the significant role of processing techniques in tailoring the structural, dielectric, and electrocaloric properties of BCZT ceramics for high-performance energy applications.Article Citation - WoS: 4Citation - Scopus: 5A Smart Building Energy Management Incorporating Clustering-Based Tariffs in the Presence of Domestic Solar Energy, Battery, and Electric Vehicle(Pergamon-elsevier Science Ltd, 2024) Alilou, Masoud; Mohammadi-ivatloo, Behnam; Mohammadpourfard, MousaSmart buildings play a crucial role in optimizing energy management within the power network. As end-users of the power network, they have the ability to not only reduce economic costs for householders but also modify the technical indices of the power network. To promote efficient device management in smart homes (SH), demand response programs are recommended for consumers. This research investigates the application of clusteringbased electricity pricing strategy aimed at effectively managing the energy devices of a residential smart home. The utilized method categorizes the electricity tariff into five rates according to the clustering of the realtime pricing program. Ward's clustering method is utilized to cluster and determine new electricity tariffs. The primary goal of the energy management program is to minimize the building's energy cost, which is accomplished through the utilization of the multi-verse optimizer. The smart home consists of essential and manageable appliances, a photovoltaic panel (PV), a sodium-sulfur (NaS) battery, and an electric vehicle (EV). The initial parameters of the PV and EV are modeled stochastically by their probability distribution functions and calculated using the Latin hypercube sampling algorithm. The smart building's performance is assessed by taking into account various demand response programs. The numerical results present that the application of the clusteringbased management method has resulted in a significant reduction of 23-43 % in the electricity cost of smart homes. Additionally, the smart home exhibits a more linear consumption pattern when considering the electricity tariffs based on the clustering approach.