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Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/1338
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Article Citation - WoS: 20Citation - Scopus: 21Comparison of Photocatalytic and Adsorption Properties of ZnS@ZnO, CdS@ZnO, and PbS@ZnO Nanocomposites to Select the Best Material for the Bifunctional Removal of Methylene Blue(Amer Chemical Soc, 2025) Bayram, Umit; Ozer, Cigdem; Yilmaz, ErkanIn this study, photocatalytic- and adsorption-based removal processes were conducted, which are frequently preferred in wastewater treatment due to their ease of control and high removal efficiency. An innovative method aimed at wastewater treatment was developed by combining the advantages of these two distinct approaches within the same material. The study synthesized ZnO, ZnS, CdS, PbS, and their composite structures (ZnS@ZnO, CdS@ZnO, and PbS@ZnO) using a hydrothermal synthesis method. Characterization of the samples was performed through field emission-scanning electron microscopy (FE-SEM), FE-SEM-energy dispersive X-ray (FE-SEM-EDX), X-ray diffraction (XRD), Raman spectroscopy, and Fourier-transform infrared spectroscopy (FTIR) measurement. Additionally, the optical properties of all samples (absorption spectra and band gap) were investigated by using absorbance measurements obtained from ultraviolet (UV)-visible absorption spectroscopy. Although ZnO nanoparticles are among the materials with high photocatalytic properties (exhibiting a photodegradation efficiency of 95.8% in a short duration of 90 min), their adsorption properties are low. Therefore, with the aim of enhancing both the low adsorption values and the photocatalytic properties of pure metal sulfides (ZnS, CdS, PbS), nanocomposites ZnS@ZnO, CdS@ZnO, and PbS@ZnO with different morphologies were synthesized, and their photocatalytic and adsorption-based removal performances on methylene blue (MB) dye were investigated. FE-SEM images indicated that ZnS nanoparticles exhibit a spherical morphology, CdS nanoparticles have a flower-like morphology, and PbS nanoparticles display a dendritic-like structure. The results obtained from experimental studies demonstrated that the highest efficiency in both photocatalytic- and adsorption-based removal was achieved with the ZnS@ZnO nanocomposite. The degradation rates of MB were found to be 95.3, 90.5, and 89.4% for the heterojunction composites ZnS@ZnO, CdS@ZnO, and PbS@ZnO, respectively, over a time range of 0-480 min. The optimal amount of photocatalyst that could effectively degrade MB was determined to be 100 mg, and the reusability studies revealed that the ability of the ZnS@ZnO semiconductor heterojunction photocatalyst to decompose MB into simpler molecules was limited after the fourth cycle. The adsorption-based removal rates were 96.0, 30.5, and 19.4% for the heterojunction composites ZnS@ZnO, CdS@ZnO, and PbS@ZnO, respectively. Finally, parameters influencing the adsorption-based removal of MB, such as pH, mass, and contact time, were examined, indicating that the adsorption capacity of ZnS@ZnO remained unchanged after reaching a value of 40 mg<middle dot>g-1.Article Citation - WoS: 5Citation - Scopus: 16Raising Awareness of Sustainable Development Goals in Higher Education Institutions(Turkish Educational Admin Research & Development Assoc, 2024) Suklun, Harika; Bengu, ElifHigher education institutions play a crucial role in advancing sustainable development goals. They bear the responsibility of informing and encouraging all stakeholders, including faculty members, students, and industry partners, to collaborate towards achieving these goals. While many universities are integrating Sustainable Development Goals into their operations and educational programs, there is an increasing need to establish collaborative platforms with private sectors and nongovernmental organizations to further champion this agenda. Educating the future workforce is a key responsibility of these institutions, and they should actively raise students' awareness of these goals, enabling them to develop competencies related to sustainability. This study aims to explore how higher education institutions can effectively raise awareness of sustainable development goals. In addition, the research contributes to the literature by presenting a curriculum designed in a Turkish higher education institution to foster awareness of sustainable development goals. The findings hold the potential to significantly enrich existing literature on awarenessraising practices and the promotion of sustainability strategies, extending beyond higher education institutions to organizations at large.Article Citation - WoS: 4Citation - Scopus: 4Examining Tongue Movement Intentions in EEG-Based BCI With Machine and Deep Learning: An Approach for Dysphagia Rehabilitation(Sciendo, 2024) Aslan, Sevgi Gokce; Yilmaz, BulentDysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, and Kernel were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN (79.4%) and SVM (63.4%) exhibited lower accuracy rates compared to ensemble methods like AdaBoost, Bagging, and Random Forest, all achieving high accuracy rates of 99.8%. These ensemble techniques proved to be highly effective in handling complex EEG datasets, particularly in distinguishing between rest and imagination phases. Furthermore, the deep learning approach, utilizing CNN and Continuous Wavelet Transform (CWT), achieved an accuracy of 83%, highlighting its potential in analyzing motor imagery data. Overall, this study demonstrates the promising role of BCI technologies and advanced machine learning techniques, especially ensemble and deep learning methods, in improving outcomes for dysphagia rehabilitation.