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
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 âŒ0.7 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, UVâvisible 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 nân heterojunction,
which facilitated the separation of electronâhole pairs at
the photo-anode/electrolyte junction
Machine Learning and Taguchi DOE Combined Approach for Modeling Dynamic Ultrasound-Assisted Fresh-Cut Leafy Green Sanitation
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 (âSurface
Microbeâ, R2 = 0.88), and E. coli O157:H7 concentration in sanitation water (âWater
Microbeâ, 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