ҎubMed

Development of an effective preconcentration strategy for the trace determination of nickel in thyme tea extracts by FeSbO4 nanoparticle-based dispersive solid phase extraction (DSPE)

Thu, 01 Jan 2026 00:00:00 GMT

Development of an effective preconcentration strategy for the trace determination of nickel in thyme tea extracts by FeSbO4 nanoparticle-based dispersive solid phase extraction (DSPE) Büyük, Muhammed Ali; Serbest, Hakan; Bakırdere, Sezgin In the present study, an accurate, rapid and green analytical method based on FeSbO4 nanoparticle-assisted dispersive solid phase extraction (DSPE) was developed to determine trace levels of nickel (Ni) by flame atomic absorption spectrophotometry (FAAS). The fabrication of the FeSbO4 nanoparticles was achieved under hydrothermal synthesis conditions and utilized for the first time as sorbent materials for the preconcentration/separation of Ni ions. A univariate optimization approach was applied to determine the optimal conditions for the extraction parameters. Under the optimal conditions, the limit of detection and the dynamic linear range were found to be 0.81 µg L-1 and between 4.0 and 40 µg L-1, respectively. The sensitivity of FAAS was improved by 137.8-fold. Thyme tea samples were spiked to assess the applicability of FeSbO4-DSPE-FAAS, and the percent recovery results between 84.8% and 120.6% obtained with the matrix matching calibration strategy demonstrated that the developed method is feasible for determining nickel ions in thyme tea and similar matrices with high accuracy. The Eco-Scale index was evaluated to determine the greenness of the developed preconcentration protocol, and a net score of 88 was calculated, indicating excellent environmental characteristics.

Development of a dispersive solid phase extraction method based on copper-metal organic framework nanoparticles for the determination of lead at trace levels in stream samples

Thu, 01 Jan 2026 00:00:00 GMT

Development of a dispersive solid phase extraction method based on copper-metal organic framework nanoparticles for the determination of lead at trace levels in stream samples Karakebap, Kübra; Serbest, Hakan; Turak, Fatma; Bakırdere, Sezgin In this study, a facile, rapid, and cost-effective method was developed for the determination of lead ions using copper-metal organic framework (Cu-MOF) nanoparticles based dispersive solid phase extraction (DSPE) in slotted quartz tube-flame atomic absorption spectrometry (SQT-FAAS) system. Cu-MOF nanoparticles were used as sorbents, and SQT was used to increase the residence time of lead atoms in the light path. The limit of detection (LOD) and limit of quantification (LOQ) of the Cu-MOF-DSPE-SQT-FAAS system were calculated as 7.1 µg L-1 and 23.5 µg L-1 under optimum experimental conditions, respectively. The regression coefficient (R2) was found to be 0.9967, and the linear operating range was determined between 15 and 300 µg L-1. Thanks to the developed method, a 103.7-fold improvement was achieved for the sensitivity of the traditional FAAS system by comparing the slopes of the linear calibration plot equations. The feasibility of the proposed method was investigated by spiking experiments with utilizing the stream water samples. The good recovery results obtained in the range of 90.8% to 127.1% demonstrated the applicability of the developed method to river water samples with high accuracy and precision. Cu-MOF structures have been employed for the first time for the preconcentration of Pb ions, and their prominent surface properties suggest that they may also be applicable for other analytical processes for different analytes.

Development and evaluation of a virtual reality game targeting Central auditory processing skills in children using cochlear implants: a randomized controlled trial

Thu, 01 Jan 2026 00:00:00 GMT

Development and evaluation of a virtual reality game targeting Central auditory processing skills in children using cochlear implants: a randomized controlled trial Tabak, Şeyma Nur; Meral Çetinkaya, Merve; İralı, Ali Efe Purpose: To develop a virtual reality (VR)-based restaurant simulation game targeting central auditory processing skills for children aged 10-18 with bilateral cochlear implants (CIs) and to evaluate its usability and effectiveness on speech understanding in noise. Materials and methods: This randomized controlled study had two phases. In Phase 1, a six-level VR restaurant game was developed and usability tested. This stage involved children with normal hearing and CIs, parents, a game developer, audiologists. The usability was examined using expert opinions, observation, game evaluation forms, the System Usability Scale, interviews, group score comparisons. In Phase 2, the game's effectiveness was tested in 24 children with bilateral CIs randomly assigned to intervention (n = 12) and control (n = 12) groups. The intervention group continued traditional rehabilitation and additionally received VR-based training for four weeks. The control group continued traditional rehabilitation only for four weeks. All participants were administered the Turkish Hearing in Noise Test (HINT) pre- and post-intervention as the primary outcome measure of speech understanding in noise, results analysed via repeated-measures ANOVA. Results: The game was usable and engaging. Normally hearing children scored higher than children with CIs on prototype levels (p<.05). Significant group × time effects were obtained in Turkish HINT under quiet and noise conditions (p<.05). The intervention group showed significantly greater improvement than controls (p<.05). Conclusion: This study demonstrated the usability of Cooklear, the first Turkish VR-based aural rehabilitation game for paediatric bilateral CI users, showing short-term improvements in speech understanding in noise. Further studies should examine broader auditory processing skills.

Optimisation of Maslow's hierarchy of needs-based survey form for nursing home residents using machine learning

Thu, 01 Jan 2026 00:00:00 GMT

Optimisation of Maslow's hierarchy of needs-based survey form for nursing home residents using machine learning Alici, Nedim; Altuncu, Damla; Cengiz, Enes; Aksoy, Hasan The global increase in the elderly population necessitates accurate assessment of the needs of individuals residing in nursing homes. This study aims to optimise a 30-item questionnaire developed on the basis of Maslow's Hierarchy of Needs by employing machine learning algorithms. As one of the pioneering applications in Turkey, the research was conducted with 310 participants from four nursing homes. Data were analysed using Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Linear Regression (LR), and Support Vector Machine (SVM). F-Test results identified the most significant variables, reducing the number of items from 30 to 10. Within the comparative analyses, the GPR model outperformed the others by yielding the lowest mean error metrics (RMSE = 0.252, MSE = 0.064, MAE = 0.195) and the highest predictive accuracy (R2 = 0.86). Findings indicate that the physiological, social, and psychological needs of older adults can be assessed through shorter, more reliable questionnaires. This study offers academic and practical contributions to elderly care and interior design.