Aksaray Üniversitesi Kurumsal Akademik Arşivi
DSpace@Aksaray, Aksaray 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 telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Fabrication of smart chitosan composite beads for alleviate boron toxicity in a model plant (Lemna gibba): Characterization, toxicity assessment, and boron removal
(Elsevier Ltd, 2025) Yakar, Anıl; Türker, Onur Can; Çakmak, Emel; Yılmaz Baran, Nuray; Baran, Talat
Excessive boron (B) in aquatic ecosystems poses a significant threat to environmental health and biodiversity. In this respect, an attractive strategy should be evaluated to reduce B toxicity in the water environment and protect aquatic organisms. The study aims to reduce B-induced toxicity in a model plant, Lemna gibba, using smart chitosan-magnetic composite beads enriched with keratin, and further enhanced with boron-tolerant bacteria (Acinetobacter sp.). We tested different chitosan-magnetic composite beads for their B adsorption capacity, focusing on a specific type enriched with keratin for the first time in the literature. The effects of adding chitosan-magnetic composite beads in a test solution containing B mine effluent to alleviate B toxicity on L. gibba's growth parameters (frond number, biomass production, and EC50 value) were detailly evaluated in the experiment period. Accordingly, the chitosan-magnetic composite beads with keratin (Mag-Ch-K) demonstrated high B adsorption, with a maximum loading capacity of 2.875 mg/g at pH 7. The relative growth rate of L. gibba in a reactor containing Mag-Ch-K beads was measured to be approximately 2-fold (0.2065) higher than that of the control reactor (0.1212) without composite beads at 64 mg L−1 B concentration. More importantly, Mag-Ch-K bead significantly increased the plant's tolerance against B in the reactor matrix, as indicated by an EC50 value of 44.18 mg L−1 compared to 17.17 mg L−1 in the control. This study provides a promising approach to mitigate B toxicity in water bodies, offering a practical operation, high growth production, and preventing B pollution shock via modified bead with Acinetobacter sp. High B removal (76 %) was also achieved from reactors containing Mag-Ch-K-D through the high B-loading capacities and plant uptake. These dual benefits encourage designers to design chitosan and duckweed-based treatment systems for ecological conservation and pollution management in B-rich waters, such as B mine effluent pollution.
Comprehensive evaluation of machine learning models for real-world air quality prediction and health risk assessment by AirQ+
(Springer Science and Business Media Deutschland GmbH, 2025) Koçak, Ebru
This study extensively examines five distinct machine learning models used to predict hourly air particulate matter concentrations. The study used real-world data, including pollutant levels and various meteorological parameters, for model training and evaluation, making the study more reliable and effective. The study focused on capturing short-term trends in pollutant concentrations and meteorological conditions. Results showed varied model performances. The Ridge Regression model exhibited a moderate R2 value of 0.44 for PM2.5 prediction and an impressive R2 of 0.91 for PM10 prediction. Support Vector Regression showed strength in PM2.5 prediction (R2 = 0.83) but faced challenges in forecasting PM10. Random Forest and Extra Trees Regression demonstrated robust overall performance, particularly in PM10 forecasting (R2 = 0.75). Extreme Gradient Boosting displayed competitive results for both PM2.5 and PM10 (R2 = 0.80 and 0.81). Each model's identified strengths and limitations provide valuable insights for air quality management, offering a foundation for future research and the development of machine learning models in the continuous pursuit of accurate and timely air quality predictions. The AirQ+ model was used to estimate the health effects of PM2.5 exposure and predict the long-term mortality rates associated with PM2.5. The average estimated attributable proportion for all years is 10.2% (with a range of 6.5% to 13.2%). The results show differing trends in estimated mortality rates, underscoring the need for targeted interventions to reduce the public health risks associated with exposure to polluted air.
Microplastics in Soil Increase Cadmium Toxicity: Implications for Plant Growth and Nutrient Imbalance
(2025) Erdem, Halil; Gence, Cabir Çağrı; Öztürk, Mehmet; Buhan, Ekrem; Kholikulov, Shodi Turdukulovich; Kaya, Yağmur
The increasing presence of microplastics (MPs) and cadmium (Cd) in agricultural soils represents an emerging environmental challenge, necessitating urgent investigation due to their potential synergistic effects on soil and plant health. This study investigated how polyethylene microplastics (PE-MPs) affect Cd behavior in soil, focusing on both their individual and combined impacts on soil pH, Cd bioavailability, plant growth, and nutrient dynamics. MPs can act as carriers of Cd, enhancing its mobility within the soil–plant system. To achieve this, a pot experiment was conducted using soils treated with different doses of PE MPs (0%, 1%, and 2%, w/w) and Cd (20 mg Cd kg−1). Soil pH, DTPA-extractable Cd, plant growth parameters, Cd accumulation in roots and shoots, and mineral nutrient concentrations were measured. The results indicated that while Cd alone did not significantly alter soil pH, increasing MP doses statistically reduced soil pH and enhanced Cd bioavailability, with DTPA-extractable Cd rising by 14.4% to 25.4%. The combined application of MPs and Cd resulted in a 38% reduction in root yield and a 32% decrease in above-ground biomass. The presence of MPs exacerbated Cd uptake, leading to significantly higher Cd accumulation in both roots and shoots compared to Cd application alone. Moreover, the combined presence of MPs and Cd disrupted the nutrient uptake mechanisms, as evidenced by significant reductions in nitrogen (N) and phosphorus (P) concentrations in root and shoot tissues. These results indicate that MPs and Cd together disrupt soil chemical stability and compromise plant nutritional status. Thus, our findings emphasize that MPs not only serve as physical pollutants but also as vectors that intensify heavy metal contamination risks in agricultural ecosystems.
Development of DFO chelator-attached adsorptive membranes for selective removal of Fe3+ ions to obtain clean drinking water
(Elsevier B.V., 2025) Karakoç, Veyis; Gürkök Tan, Tuğba; Arda Küçük, Vedat; Odabaşı, Mehmet
Access to safe drinking water is one of the most significant concerns of today and foreseeable future. In order to solve this problem, scientists have been working intensively on the development of new technologies. In this study, poly(HEMA)-based porous polymeric membranes were synthesized by UV polymerization method to remove iron contamination in drinking water, which is among the main issues of water purification. The chelating agent desferroxamine (DFO) was covalently bonded to the synthesized poly(2-hydroxyethyl methacrylate-co-glycidyl methacrylate) polymeric membrane. The synthesized DFO-bound poly(HEMA-GMA) polymeric membrane was characterized by FTIR, SEM, elemental analysis, and BET surface area. The optimum removal performance for Iron (III) (Fe3+) ions from an aqueous solution of poly(HEMA-GMA)-DFO membranes was determined in a continuous flow system by varying pH, flow rate, ionic strength, and interaction times. According to the experimental results the maximum adsorption capacity of poly(HEMA-GMA)-DFO membranes for Fe3+ ions was found to be 25.7 mg Fe3+/g at pH:5.0, 1.5 ml/min flow rate, and 100 ppm concentration. The selectivity of the synthesized polymeric membrane for Fe3+ ions was determined as Fe3+>Zn2+ >Ni2+ by adsorption studies performed in the presence of Fe3+ions and Zn2+ and Ni2+ ions. Desorption studies of the membrane system were performed with 0.5 M (ethylenediaminetetraacetic acid) EDTA solution. As a result of desorption and adsorption studies that were repeated 10 times with the same polymer to determine the reuse behavior of the DFO chelator-bound membrane, it was observed that there was no significant decrease in the membrane performance for the removal of Fe3+ ions. Experimental studies revealed that, the use of synthesized poly(HEMA-GMA)-DFO membranes as adsorbents was found to be a promising method for the removal of Fe3+ ions.
Acoustic energy harvesting and modeling from distributed feedback quantum cascade laser based sensor system
(Elsevier B.V., 2025) Sarı, Filiz; Bayraklı, İsmail
This study analyzes acoustic energy harvesting from a distributed feedback quantum cascade laser (DFB-QCL)-based sensor system. The system integrates a DFB-QCL as an optical excitation source and a custom-designed photoacoustic resonator to generate and detect acoustic waves. A Cockcroft–Walton voltage multiplier (CWVM) converts the resulting electrical signal into direct current voltage. Capacitor tests for the VM are conducted under open-circuit and loaded conditions. Considering voltage conversion efficiency, mean voltage, and ripple, 22 µF capacitors are selected as optimal and used in all subsequent analyses. Experiments with up to four-stage VMs are conducted using ten load resistances. The fourth-stage VM delivers 6.4 mW of mean power under a 10 kΩ load, with an energy efficiency of 26.7%. These findings indicate the system's potential to power self-sufficient sensor networks and low-power electronic devices, especially in remote or inaccessible environments. A mathematical model is developed to describe the relationship between acoustic input, load resistance, and VM output. The model reflects the nonlinear characteristics derived from the time-domain analysis of the VM circuit and is constructed from experimental data. Its accuracy is validated using the mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2) metrics, yielding low error rates with R2 values ranging from 0.972 to 0.991. Mean voltage and power outputs are fitted by power series functions of load resistance, achieving goodness of fit above 99%. The high level of agreement between the fitted and modeled results demonstrates the model's reliability in representing stage-dependent system behavior.
