Electrochemical Activity of Iron Phosphide Nanoparticles in Hydrogen Evolution Reaction

Iron phosphide (FeP) has been recently demonstrated as a very attractive electrocatalyst for the hydrogen evolution reaction (HER). However, the understanding of its properties is far from satisfactory. Herein, we report the HER performance of FeP nanoparticles is enhanced after a stability test due to reduced surface-charge-transfer resistance in the HER process. The synthetic temperature and reactant ratio are important for surface-charge-transfer resistance, the electrochemically active surface area, and HER activity. Hydrogenation apparently improves the HER performance of FeP nanoparticles by reducing the surface-charge-transfer resistance, overpotential, and Tafel slope. Enhanced HER performance is observed after a stability test for both bare and hydrogenated FeP nanoparticles in the HER due to reduced surface-charge-transfer resistance. Thus, this study may enrich our knowledge and understanding to advance HER catalysis for electrochemical hydrogen generation

Three-Dimensional Crystalline/Amorphous Co/Co₃O₄ Core/Shell Nanosheets as Efficient Electrocatalysts for the Hydrogen Evolution Reaction

Earth-abundant, low-cost electrocatalysts with outstanding catalytic activity in the electrochemical hydrogen evolution reaction (HER) are critical in realizing the hydrogen economy to lift our future welfare and civilization. Here we report that excellent HER activity has been achieved with three-dimensional core/shell Co/Co₃O₄ nanosheets composed of a metallic cobalt core and an amorphous cobalt oxide shell. A benchmark HER current density of 10 mA cm^–2 has been achieved at an overpotential of ∼90 mV in 1 M KOH. The excellent activity is enabled with the unique metal/oxide core/shell structure, which allows high electrical conductivity in the core and high catalytic activity on the shell. This finding may open a door to the design and fabrication of earth-abundant, low-cost metal oxide electrocatalysts with satisfactory hydrogen evolution reaction activities

Converting CoMoO₄ into CoO/MoO_<i>x</i> for Overall Water Splitting by Hydrogenation

Special structures of materials often bring in unprecedented catalytic activities, which are critical in realizing large-scale hydrogen production by electrochemical water splitting. Herein, we report a CoO/MoO_<i>x</i> crystalline/amorphous structure as an effective bifunctional electrocatalyst for water splitting. Converted from CoMoO₄ by hydrogenation, the CoO/MoO_<i>x</i>, featured with crystalline CoO in amorphous MoO_<i>x</i> matrix, displays superior catalytic activities toward both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). It shows small onset overpotentials of 40 and 230 mV for the HER and OER in 1.0 M KOH, respectively, and overall water splitting starting at 1.53 V with a robust stability. The high catalytic activity of the CoO/MoO_<i>x</i> is benefited from the large defect-rich interface between CoO and MoO_<i>x</i>, along with the amorphous nature of MoO_<i>x</i>. Thus, this study demonstrates the effectiveness of structural manipulation in developing highly active electrocatalysts for overall electrochemical water splitting

Two Different Roles of Metallic Ag on Ag/AgX/BiOX (X = Cl, Br) Visible Light Photocatalysts: Surface Plasmon Resonance and Z-Scheme Bridge

Ag/AgX/BiOX (X = Cl, Br) three-component visible-light-driven (VLD) photocatalysts were synthesized by a low-temperature chemical bath method and characterized by X-ray diffraction patterns, X-ray photoelectron spectroscopy, field emission scanning electron microscopy, transmission electron microscopy, high-resolution transmission electron microscopy, and UV–vis diffuse reflectance spectra. The Ag/AgX/BiOX composites showed enhanced VLD photocatalytic activity for the degradation of rhodamine B, which was much higher than Ag/AgX and BiOX. The photocatalytic mechanisms were analyzed by active species trapping and superoxide radical quantification experiments. It revealed that metallic Ag played a different role for Ag/AgX/BiOX VLD photocatalysts, surface plasmon resonance for Ag/AgCl/BiOCl, and the Z-scheme bridge for Ag/AgBr/BiOBr

Phosphorus-Doped Graphitic Carbon Nitride Nanotubes with Amino-rich Surface for Efficient CO₂ Capture, Enhanced Photocatalytic Activity, and Product Selectivity

Phosphorus-doped graphitic carbon nitrides (P-g-C₃N₄) have recently emerged as promising visible-light photocatalysts for both hydrogen generation and clean environment applications because of fast charge carrier transfer and increased light absorption. However, their photocatalytic performances on CO₂ reduction have gained little attention. In this work, phosphorus-doped g-C₃N₄ nanotubes are synthesized through the one-step thermal reaction of melamine and sodium hypophosphite monohydrate (NaH₂PO₂·H₂O). The phosphine gas generated from the thermal decomposition of NaH₂PO₂·H₂O induces the formation of P-g-C₃N₄ nanotubes from g-C₃N₄ nanosheets, leads to an enlarged BET surface area and a unique mesoporous structure, and creates an amino-rich surface. The interstitial doping phosphorus also down shifts the conduction and valence band positions and narrows the band gap of g-C₃N₄. The photocatalytic activities are dramatically enhanced in the reduction both of CO₂ to produce CO and CH₄ and of water to produce H₂ because of the efficient suppression of the recombination of electrons and holes. The CO₂ adsorption capacity is improved to 3.14 times, and the production of CO and CH₄ from CO₂ increases to 3.10 and 13.92 times that on g-C₃N₄, respectively. The total evolution ratio of CO/CH₄ dramatically decreases to 1.30 from 6.02 for g-C₃N₄, indicating a higher selectivity of CH₄ product on P-g-C₃N₄, which is likely ascribed to the unique nanotubes structure and amino-rich surface

A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China

<div><p>Backgrounds/Objective</p><p>Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas.</p><p>Methods</p><p>A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model.</p><p>Results</p><p>The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10⁻⁴, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10⁻⁴, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend.</p><p>Conclusion</p><p>The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases.</p></div

Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of original prevalence series.

<p>A and B. ACF and PACF plots of original schistosomisis prevalence (1956–2008); C and D. ACF and PACF plots after one order of regular differencing (1956–2008); E and F. ACF and PACF plots of original schistosomisis prevalence (1956–2012); G and H. ACF and PACF plots after one order of regular differencing (1956–2012). Dotted lines indicate 95% confidence intervals. Most of the correlations fall around zero within their 95% confidence intervals except for the one at zero lag, which indicate the series achieved stationary.</p

Error autocorrelation plots of different target series.

<p>The error autocorrelation was one of the evaluation parameters in the modelling process. As shown in the figure, the correlations except for the one at zero lag, all fall within the 95% confidence limits around zero, which demonstrates that the model reliably corresponds to the data.</p

The change trend of prevalence of schistosomiasis from three models.

<p>The comparison of observation and predicted values between the hybrid ARIMA-NARNN model, and the single ARIMA or NARNN model are shown in Figure 6A. On the whole, the red line is closer to the observation curve that indicates the predicted values from the ARIMA-NARNN model are the best fit for the prevalence of schistosomiasis in humans. Figure 6B shows the predicted prevalence of schistosomiasis (1960–2016) from the reconstructed hybrid ARIMA-NARNN model.</p

The optimum networks configuration of different target series.

<p>Note: OS = original prevalence series, RS = residual series, NRS = new residual series</p><p>All MSE values should be multiplied by 10⁻⁴.</p