2 research outputs found

    Enhanced Photoelectrochemical Response of the ZnO/V<sub>2</sub>O<sub>5</sub> Heterojunction Via Improved Visible-Light Absorption and Charge Carrier Separation

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    Planar ZnO/V2O5 heterojunctions show excellent photocurrent density and are easy to prepare using a simple physical vapor deposition technique. First, ZnO thin-film photo-electrodes were prepared by radio frequency sputtering, and then their thickness was optimized for improved photoelectrochemical (PEC) response, resulting in an optimum photocurrent density value of ∌0.7 mA/cm2 at 0.61 V vs Ag/AgCl for ZnO thin films deposited for 15 min. Thereafter, ZnO/V2O5 heterojunctions were fabricated by depositing V2O5 thin films for different deposition durations of 10, 20, and 30 min onto ZnO thin-film samples which were already optimized to further improve the PEC performance. The structural, optical, and morphological properties of pristine and heterojunction thin-film samples were investigated by X-ray diffraction, Raman spectroscopy, UV–visible spectroscopy, field-emission scanning electron microscopy, and energy-dispersive X-ray spectroscopy techniques. The maximum photocurrent density value of 1.56 mA/cm2 at 0.61 V vs Ag/AgCl was obtained for ZnO/V2O5 heterojunction photoanodes, where the top V2O5 layer was deposited by RF magnetron sputtering process which occurred for 20 min. The ZnO/V2O5 heterojunction photocurrent density was nearly twice as compared with that of the pristine ZnO photo-electrode. This improved PEC response of the ZnO/V2O5 heterojunction was due to enhanced visible-light absorption and the formation of a staggered n–n heterojunction, which facilitated the separation of electron–hole pairs at the photo-anode/electrolyte junction

    Machine Learning and Taguchi DOE Combined Approach for Modeling Dynamic Ultrasound-Assisted Fresh-Cut Leafy Green Sanitation

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    Chlorine-based fresh produce sanitation is a dynamic process, and sanitation efficiency is limited due to chlorine degradation. Here, ultrasound was coupled with a benchtop sanitation system to enhance chlorine sanitizer efficiency in fresh-cut leafy green sanitation. Taguchi design of experiments (DOE) and machine learning (ML) were combined to model the relationship between sanitation condition parameters and sanitation outcomes. Multiple ML algorithms were fitted, tuned, and compared for performance using 127 experimental trials (training-to-validation ratio = 3:1). Gaussian process regression (GPR) models showed the best performance in predicting sanitation outcomes of chemical oxygen demand (COD, R2 = 0.73), remaining Escherichia coli O157:H7 on the leaf surface (“Surface Microbe”, R2 = 0.88), and E. coli O157:H7 concentration in sanitation water (“Water Microbe”, R2 = 1.00). Cut size and agitation speed were identified as the most critical input parameters. An initial free chlorine concentration over 20 mg/L was recommended to minimize the E. coli O157:H7 concentration in sanitation water. This work showcases the combined approach of ML and DOE in optimizing fresh-cut produce sanitation. Moreover, it provides a solution for overcoming the difficulties of modeling multiple controllable and uncontrollable factors with reduced experimental runs
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