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 A Machine Learning Framework for Advanced Analytical Detection of CD36 Using Immunosensors Below Limit of Detection(Elsevier Ltd, 2026) Yeke, M.C.; Gelen, S.S.; Fil, H.; Yalcin, M.M.; Gumus, A.; Yazgan, I.; Odaci, D.We introduce a machine learning (ML)-based regression framework for quantitative electrochemical analysis, representing a paradigm shift from traditional univariate methods to a multivariate approach. Conventional analysis is constrained by reducing the entire signal to a single peak current feature to define a linear range and calculate a limit of detection (LOD). In contrast, our methodology treats the Differential Pulse Voltammetry (DPV) curve as time-series data, creating a high-dimensional fingerprint by systematically evaluating multiple data windows with varying widths around the main signal peak to identify the most informative segment. To validate this approach, a biosensor was developed by immobilizing Anti-CD36 antibodies on polydopamine-modified screen-printed carbon electrodes for the detection of CD36, a key protein in metabolism and immunity. Measurements were collected across 12 concentrations, including blank samples, spanning a range of 0 to 25 ng/mL. Following data augmentation, nine different regression models were evaluated, with the top-performing models achieving near-perfect prediction accuracy (R2>0.99) across this entire range. This high accuracy across the full concentration spectrum quantitatively demonstrates the method's ability to operate without relying on traditional concepts like linear range or LOD, enabling reliable detection at ultra-low levels. Furthermore, the immunosensor exhibited high selectivity against common interferents and excellent recovery in human serum. This methodology represents a significant advancement in analytical electrochemistry, providing a transferable approach for enhancing sensitivity in biomarker detection with potential applications in clinical diagnostics and biomedical research. The codes and dataset are made publicly available on GitHub to support further research: https://github.com/miralab-ai/biosensors-AI. © 2026 The Author(s)Article Geothermal Drying in Agricultural Sector - Worldwide Examples(Elsevier Ltd, 2026) Tomaszewska, B.; Baba, A.; Akkurt, G.G.; Mukti, M.; Helvaci, H.U.; Bielec, B.; Operacz, A.Agricultural drying is traditionally used to preserve fruits and vegetables which mostly relied on energy-intensive processes usually powered by fossil fuels. In this review, we explore an innovative and sustainable alternative: using geothermal energy to dry produce. The paper reviews the main technical aspects related to the use of geothermal energy in drying fruits and vegetables. We delve into the technical details of two leading methods, hot air drying and refractive window drying, highlighting their advantages, drawbacks, and the critical factors that influence the quality of the final product. By examining real-world applications from countries as diverse as Iceland, the USA, Greece, Turkey, Macedonia, Kenya, Serbia, El Salvador, Guatemala, Mexico, Thailand, Poland, and the Philippines, this paper showcases how geothermal energy can be directly applied in drying operations—whether through standalone systems operating between 60 °C and 97 °C or integrated cascade systems wherever geothermal resources are used for power generation and in the form of the waste heat for drying purposes, can be considered as important direction. Due to a lack of actual information on the economic aspects of geothermal drying, in addition to outlining the technical merits of geothermal drying, we also discuss economic considerations and potential challenges to provide a roadmap for future projects. Moreover, the authors underlined several aspects that can contribute to the failure or limited success of geothermal drying projects. Ultimately, adopting geothermal drying not only reduces greenhouse gases (GHS) emissions but also lessens dependence on costly, polluting fossil fuels, paving the way for a greener, more energy-efficient future in food preservation. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Article Floating Pontoons to Reduce Wave Overtopping at a Vertical Seawall: An Experimental Study(Elsevier Ltd, 2026) Eroglu, N.; Ozbahceci, B.Coastal flooding caused by extreme wind, wave and water level conditions is an increasing concern, particularly for historical coastal cities where conventional flood defenses may be unsuitable due to aesthetic and cultural constraints. Floating structures have gained attention for their adaptability to sea level rise, yet previous studies have mainly examined wave transmission rather than their capacity to reduce wave overtopping. This study presents the first experimental investigation to directly measure wave overtopping for floating pontoons placed in front of a vertical seawall. Tests were conducted in a controlled wave flume environment to evaluate the effects of pontoon geometry, mooring type, and distance from the seawall on overtopping performance. The results show that floating pontoons can significantly reduce wave overtopping. Overtopping reductions of 75–98 % was achieved, with the most effective configuration combining high freeboard and large draft (1.5 m prototype scale). Wave transmission was also measured and compared with existing prediction formulas. When the transmitted wave height is used in EurOtop (2018) formula, overtopping rate is overestimated particularly when the relative crest freeboard exceeds 0.75 as differences in wave steepness, spectral period and directional spreading induced by the floating pontoon are not captured by the formula. To improve predictive capability, a new influence coefficient (γ<inf>fp</inf>) is proposed to modify Eq. 7.5 in EurOtop (2018) for cases involving pile-guided floating pontoons. These findings provide new experimental evidence on wave–structure interaction and highlight the potential of floating pontoons as effective, adaptable, and visually compatible flood mitigation solutions for vulnerable coastal regions. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Article An AI-Based Solution for Warehouse Safety: Video Surveillance System Based Anomaly Detection in Equipment-Human Interactions with Vanilla Autoencoder(Elsevier Ltd, 2025) Elçi, T.; Unlu, M.The significant growth of the logistics sector in recent years has resulted in the expansion of warehouse operations and an increased use of equipment, leading to a rise in workplace accidents. These incidents are predominantly attributed to factors such as carelessness, fatigue, high work intensity, individual behaviors, lack of experience, insufficient training, and employee negligence. To enhance warehouse safety, it is essential to implement a system capable of real-time prediction of human-equipment interactions. This study proposes a comprehensive video surveillance framework designed to improve occupational safety in warehouse environments. The system integrates key components, including object detection, object tracking, action recognition, and alarm classification, to effectively reduce risks and prevent accidents.The system employs YOLOv7, a deep learning model with the ability to quickly and accurately detect objects in a single network pass, as the object detection methodology, and DeepSORT, an algorithm for object tracking that assigns unique identifiers to each object and utilizes deep learning techniques to improve tracking performance. The action detection component of the system introduces a novel approach by analyzing and identifying actions and movements while detecting anomalies and potential risks. By leveraging features such as the speed, tags, movement direction, and coordinate data of individuals and equipment, the system estimates alarm levels and generates corresponding alarms, providing an innovative and dynamic solution for real-time risk assessment. The system, tested to demonstrate technological capabilities such as real-time responsiveness and high operational success rates, is designed to predict accidents in warehouse environments, generate alarms, and significantly reduce the risk of occupational accidents. © 2025 The Franklin Institute. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.Article Citation - Scopus: 5Risk Analysis for Groundwater Intakes Based on the Example of Neonicotinoids(Elsevier Ltd, 2024) Pietrzak,D.; Kania,J.; Kmiecik,E.; Baba,A.Neonicotinoids are a class of broad-spectrum insecticides that are dominant in the world market. They are widely distributed in the environment. Understanding the sources, distribution, and fate of these contaminants is critical to mitigating their effects and maintaining the health of aquatic ecosystems. Contamination of surface and groundwater by neonicotinoids has become a widespread problem worldwide, requiring comprehensive action to accurately determine the mechanisms behind the migration of these pesticides, their properties, and their adverse effects on the environment. A new approach to risk analysis for groundwater intake contamination with emerging contaminants was proposed. It was conducted on the example of four neonicotinoids (acetamiprid, clothianidin, thiamethoxam, and imidacloprid) in relation to groundwater accessed by a hypothetical groundwater intake, based on data obtained in laboratory tests using a dynamic method (column experiments). The results of the risk analysis conducted have shown that in this case study the use of acetamiprid and thiamethoxam for agricultural purposes poses an acceptable risk, and does not pose a risk to the quality of groundwater extracted from the intake for food purposes. Consequently, it does not pose a risk to the health and life of humans and other organisms depending on that water. The opposite situation is observed for clothianidin and imidacloprid, which pose a higher risk of groundwater contamination. For higher maximum concentration of neonicotinoids used in the risk analysis, the concentration of clothianidin and imidacloprid in the groundwater intake significantly (from several to several hundred thousand times) exceeds the maximum permissible levels for drinking water (<0.1 μg/L). This risk exists even if the insecticides containing these pesticides are used according to the information sheet provided by the manufacturer (lower maximum concentration), which results in exceeding the maximum permissible levels for drinking water from several to several hundred times. © 2024 Elsevier LtdArticle Citation - WoS: 21Citation - Scopus: 23Lithium: an Energy Transition Element, Its Role in the Future Energy Demand and Carbon Emissions Mitigation Strategy(Elsevier Ltd, 2024) Chandrasekharam,D.; Şener,M.F.; Recepoğlu,Y.K.; Isık,T.; Demir,M.M.; Baba,A.Energy transition elements (Li, Ni, Co, Fe, Cu) are gaining importance due to their ability to provide energy and play an important role as primary energy sources. Because of the energy density and power density, Li-ion batteries have the edge over other batteries. Li is distributed in various rock-forming minerals and brines, and geothermal waters. Though lithium-bearing minerals are spread over a broad geographic region, these minerals are confined to certain countries with substantial economic potential. Li is extensively used in batteries, and battery-driven vehicles are growing exponentially to meet the carbon reduction goal of the Paris agreement in 2015 and signed by more than 50 percent of the countries. Nearly 55 million cars supported by Li batteries are expected to roll out by 2030. While this is the demand, its occurrence and concentration/extraction processes are not keeping pace with this demand. The extraction of Li from its ore is an energy-intensive process involving many fossil fuel-based energies. To recover one ton of Li metal, nearly 5 to 6 tons of CO2 is emitted. The CO2 emissions of 28 kWh LFP, NMC, and LMO batteries vary from 5600 to 2705 kg CO2-eq. The end-of-life emissions of an internal combustion engine (ICE) vehicle are 400 kg CO2/vehicle, while Li Battery supports 500 kg/vehicle. The quantity of Li required for a 24 kWh average capacity leaf battery is about 137 g/kWh. While emissions are associated with the manufacturing of the batteries, emissions are also associated with a way that while they are recharged as the recharging source is fossil fuel-based energy. The best option to meet zero net carbon emissions by 2050, as envisaged by International Energy Agency (IEA), is to recover Li from geothermal brines and use geothermal energy for recharging. While hydrothermal energy sources are site-specific, enhanced geothermal system (EGS) based geothermal energy is not site-specific and is found wherever high radiogenic granites are available. High radiogenic granites are widely distributed, and heat recovered from EGS sources can provide clean energy and heat. Extraction of lithium from geothermal waters and using geothermal energy for recharging the batteries will drastically reduce CO2 emissions. It will drive the world towards Net Zero Emissions (NZE) scenario in the future. This is being practiced in Turkey. Future research should develop technology to recover Li from geothermal fluids with low concentration and support EGS development. © 2024 Elsevier LtdArticle Citation - WoS: 12Citation - Scopus: 12Enhancing a Bio-Waste Driven Polygeneration System Through Artificial Neural Networks and Multi-Objective Genetic Algorithm: Assessment and Optimization(Elsevier Ltd, 2024) Hajimohammadi Tabriz,Z.; Taheri,M.H.; Khani,L.; Çağlar,B.; Mohammadpourfard,M.This paper aims to study the feasibility of municipal sewage sludge utilization as an energy source in a polygeneration system. This system offers distinctive benefits such as contribution to the principled removal of sewage sludge, simultaneous utilization of raw and digested sludge in different parts of the system, and production of renewable hydrogen from bio-waste. 4E (energy, exergy, exergoeconomic, and environmental) analyses, are performed to understand the system performance comprehensively. Then, parametric studies are examined the impact of changing the values of main parameters on the system operation. Afterward, a multi-objective optimization based on a genetic algorithm is carried out to achieve optimal values, considering a trade-off between the exergy efficiency and the total cost rate. Meanwhile, this work harnesses the potential of artificial neural networks to expedite complex and time-consuming optimization processes. According to the results, the gasifier exhibits the highest rate of exergy destruction, and the primary cost of consumption is attributed to its heat supply. The multi-objective optimization findings show that the optimum point has an exergy efficiency of 38.26 % and a total cost rate of 58.17 M$/year. The hydrogen production rate, energy efficiency, and net power generation rate for the optimal case are determined as 1692 kg/h, 35.24 %, and 4269 kW, respectively. Also, the unit cost of hydrogen in the optimal case is obtained 1.49 $/kg which offers a cost-effective solution for hydrogen production. © 2024 Hydrogen Energy Publications LLCArticle Citation - WoS: 9Citation - Scopus: 8Enhanced Performance and Cycling Behavior in Symmetric Supercapacitors Developed by Pure Hfb2 and Hfb2-Sic Composites(Elsevier Ltd, 2024) Paksoy,A.; Yıldırım,İ.D.; Arabi,S.; Güngör,A.; Erdem,E.; Balcı-Çağıran,Ö.Boron-based materials have attracted growing interest as promising candidates for energy storage applications. This study focuses on synthesizing pure HfB2 powders through a straightforward method involving the mechanical activation of a powder mixture comprising hafnium tetrachloride (HfCl4), boron (B), and magnesium (Mg). The HfB2 powders were mechanically alloyed with varying amounts of SiC powders to create HfB2-based composite structures. The chemical and microstructural properties of the synthesized samples were assessed using XRD, SEM/EDX, and DLS characterization techniques. Supercapacitor device performances of all resulting powders as symmetrical electrodes were systematically investigated. The test results revealed that the pure HfB2 electrode material exhibited a pseudocapacitor behavior, whereas composite powders exhibited battery-like behavior. Composite powders, demonstrated enhanced supercapacitor performance surpassing that of pure powder in terms of energy density and cycle efficiency. The pure HfB2 electrode displayed the highest power density (95 Wkg−1) among all samples: Its distinctive pseudocapacitor behavior results in the highest power density, providing valuable insights into the intricate relationship between composition and electrochemical performance in boron-based supercapacitor materials. Moreover, these results propose that by synthesizing composite powders, the charge storage mechanism can be altered and used to improve the energy density. © 2024 Elsevier B.V.