Recep Tayyip Erdoğan Üniversitesi Kurumsal Akademik Arşivi
DSpace@RTEÜ, Recep Tayyip Erdoğan Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
A novel cloud point extraction method using a benzimidazole-based ligand for the preconcentration of toxic heavy metals in water and food samples
(Royal Society of Chemistry, 2026) Özdemir, Olcay; Duran, Celal; Mneteşe, Emre; Özdeş, Duygu
In the present research, a novel cloud point extraction (CPE) method was developed for the separation and preconcentration of toxic Cu(ii), Ni(ii), Pb(ii), and Cd(ii) ions in environmental water and vegetable samples, prior to their determination by flame atomic absorption spectrometry (FAAS). The ligand 2-(2-(4-fluorobenzyl)-1H-benzo[d]imidazole-1-yl)acetohydrazide (FB-BIAH) was employed for the first time as an analytical complexing agent for the aforementioned metal ions, and Triton X-114 (TX-114) was preferred as the nonionic surfactant. Within the scope of optimizing CPE conditions, experimental parameters, such as pH, FB-BIAH and surfactant amounts, and incubation temperature and time, as well as centrifugation speed and duration, were systematically investigated. The optimal conditions for the simultaneous quantitative recovery of analyte ions were established as pH 7.0, an FB-BIAH amount of 2.5 mg, a TX-114 quantity of 25 mg, an incubation temperature of 60 °C, and an incubation time of 30 min. Under optimal conditions, the proposed method was successfully applied to river water and seawater, as well as different vegetable samples such as lettuce, parsley, and pepper. The results obtained demonstrated that the analyte ions could be reliably determined in complex matrices and that the method exhibited high analytical performance. Due to its simplicity, rapid applicability, environmentally friendly nature, and cost-effectiveness, the proposed CPE-FAAS method represents a promising approach for the routine analysis of toxic heavy metals in environmental water and food samples.
Habits in the boil: how electric kettles reveal barriers and opportunities for sustainable energy transitions
(Springer, 2026) Cüce, Erdem; Cüce, Pınar Mert
Household energy practices significantly influence broader sustainability transitions, yet they often receive limited scholarly attention compared to large-scale systems. This paper critically examines the role of electric kettles as a lens into behavioural inertia, technological efficiency, and the psychological drivers shaping domestic energy use. Drawing on recent data from the UK and Türkiye, the study compares the efficiency, cost, and cultural factors influencing kettle versus stovetop boiling, demonstrating that despite their high thermal efficiency, electric kettles’ potential for energy savings is constrained by ingrained user habits. The paper explores how psychological, cultural, and practical barriers hinder optimal kettle use and outlines how innovative control technologies, design innovations, and behaviourally informed policy interventions could unlock significant cumulative savings. Findings underscore the importance of combining technical innovation with user-centred strategies to shift everyday practices, arguing that even small appliances play a pivotal role in achieving large-scale carbon reduction targets.
Role of HVAC in building energy consumption: a critical review
(Springer, 2026) Cüce, Pınar Mert; Cüce, Erdem
This review critically evaluates the energy performance of diverse HVAC systems, with a focus on their quantified consumption profiles and operational efficiencies across varying climatic zones and building types. Conventional split air conditioning units were observed to have the highest energy demand, reaching up to 18,549.6 kWh month−1 in university buildings, with air conditioning accounting for over 80% of total electricity use. In contrast, variable refrigerant flow (VRF) systems demonstrated superior part-load efficiency, with monthly consumption reduced to 9626.9 kWh and energy performance indices (ENPI) improved by 36.6%. Ground source heat pumps (GSHPs) operating under stable subsurface conditions achieved thermal outputs of 7.0–16.0 kW with COP values ranging from 1.34 to 4.7, while hybrid systems integrating desiccant wheels and evaporative cooling reported cooling capacities between 0.84 and 16.9 kW, and COPs reaching up to 35.2 under optimised conditions. Photovoltaic-assisted systems were capable of offsetting up to 80% of cooling energy demand, equating to approximately 3.5–4.0 kWh/day savings in residential settings. Control strategies such as night purge and adaptive setpoint scheduling yielded energy reductions of 17–26%, and the integration of economisers led to a 25.5% drop in total HVAC consumption. Advanced predictive models incorporating artificial intelligence achieved accuracy levels exceeding R2 = 0.98 across simulations of over 250,000 scenarios. These findings collectively underline the critical importance of selecting context-appropriate HVAC technologies and implementing intelligent, climate-responsive control to achieve substantial reductions in system loads, power input, and operational energy demand, thereby supporting global efforts towards sustainable and low-carbon buildings.
Dynamic stability analysis of 18650 cylindrical lithium-ion batteries on elastic foundation: result verification via machine learning algorithm
(World Scientific, 2026) Ma, Yilu; Bdour, Ahmed N.; Bouallegue, Belgacem; Yaylacı, Murat
This research aims to improve the electro-mechanical and vibration characteristics of cylindrical-lithium-ion batteries using advanced nanocomposite materials reinforced with graphene platelets (GPL); these are inserted as the reinforcement of the cathode layer of spirally cross-section batteries that yield better mechanical properties, thermal conductivity as a combined group, and vibrational stability when loaded dynamically. Also, the structure is surrounded by an elastic foundation. Analytical modeling is also investigated with the Rayleigh–Ritz technique to examine the structural and vibration behavior of the cylindrical batteries, accounting for the intricate relationships between material properties and geometry. Furthermore, a deep neural network (DNN) algorithm is modeled to serve as a predictive tool for the electro-mechanical and vibration response of the batteries operating under various service and environmental conditions. The DNN framework shows remarkable accuracy and efficiency with reliable predictions and low computational cost. The results show the promise of GPL nanocomposites to significantly increase the stability and life of lithium-ion batteries when exposed to mechanical shocks and thermal excursions. This analytical and computational framework provides a solid protocol for the design of the next volume of cylindrical lithium-ion batteries to improve performance. The results clearly position advanced material incorporation and artificial intelligence (AI)-guided predictive modeling to help push the integration of efficient and sustainable energy storage technologies. This work provided a basis for battery technology advancement that will promote better energy storage solutions in multiple applications.
Assessing the mariner's role and human reliability in the post-accident evidence collection process: The case of ship grounding using a BN–SLIM approach
(Elsevier, 2026) Kartal, Şaban Emre
Grounding accidents pose a significant risk to the environment, global logistics, maritime vessels and seafarers. A substantial proportion of literature pertaining to maritime accidents is dedicated to groundings, however to the best of knowledge, there is an absence of research papers investigating the processes of post-accident evidence collection. To address this gap, the present paper aims to identify an optimal evidence collection process for grounding accidents to evaluate the potential shortcomings and analyse the human factors contributing to deficiencies in this critical work. The evidence collection process on board is primarily coordinated by the master and supported by the officers, this study conceptually represents the mariner's collective role and reliability in managing post-accident documentation tasks. A fuzzy Bayesian Network (BN) is employed to model an ideal evidence collection framework and identify factors that may lead to failure in this intricate process. Subsequently, the human error probability (HEP) was determined with respect to the substantial tasks inherent in evidence collection process, by means of the Success Likelihood Index Method (SLIM). In addition, the performance-shaping factors (PSFs) that initiate errors must were identified. The BN results indicate that the most effective causes of evidence collection failure were the collection of weather reports, ECDIS records and VDR data. The SLIM findings suggest that tasks with the highest error probability will be encountered during the collection of bridge records, and pinpoints the importance of stress, leadership and knowledge as the most influential factors affecting this task. The proposed framework and the outcomes of this study may contribute to post-accident response strategies of ship operators, masters and maritime insurance providers by demonstrating the importance of human reliability in complex post-accident situations.
