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Photocatalytic water splitting for hydrogen production provides a viable approach to address the energy crisis. However, the sluggish four-electron water oxidation severely restricts water splitting efficiency. Herein, with the Mo-doped ZnIn2S4(Mo-ZIS) photocatalyst, the hydrogen production reaction is significantly facilitated through synergistic integration with the kinetically favourable oxidation of benzyl alcohol (BA). The high-valence and unsaturated Mo species promote not only the separation of photo carriers but also the activation of BA, thereby accelerating the oxidation of BA. Moreover, Mo doping adjusts the band structure of ZIS, enhancing the oxidation capability of the photocatalyst without inducing the excessive oxidation of BA. Consequently, Mo-ZIS efficiently produces both high-value-added H2and benzaldehyde (BAD) products, exhibiting a remarkable hydrogen evolution rate (16,353 µmol g−1h−1) and BAD yield (13,942 µmolg−1h−1) without the need for additional noble-metal cocatalysts, significantly surpassing the performance of pristine ZIS photocatalysts. Strikingly, by modulating the desorption process of intermediates on the Mo-ZIS surface through adjusting the hydrogen bonding network strength between BA and the solvent, the BAD production rate of Mo-ZIS is 23,068 µmol g−1h−1, resulting in a 65% increase compared to acetonitrile solution. This work introduces a valuable strategy for combining photocatalytic hydrogen production with selective organic product transformation, providing new insights for the development of novel photocatalysts to achieve efficient solar energy conversion.

Electromagnetic (EM) inversion refers to reconstructing the EM properties of unknown targets from measured scattered fields. In practical applications, various algorithms have been developed, especially those that rely on greedy pursuit (GP) within the compressive sensing framework. However, the GP-based algorithms still suffer from several intractable issues, such as poor priors of the pregiven sparsity index, insufficient robustness of generated single support, and possible failure when reconstructing nonsparse targets. A novel algorithm based on depth-first multipath matching pursuit (MMP-DF) assisted by a convolutional neural network (CNN) is proposed to overcome these issues. Specifically, the CNN is designed to predict the sparsity index, and MMP-DF is implemented to identify multiple promising supports for improving the likelihood of finding favorable support, and the Fourier basis is applied to transform the case of nonsparse targets into sparse ones. Theoretical analysis and numerical simulations show that, compared with the algorithm based on orthogonal matching pursuit, the proposed algorithm achieves improved and robust results in the case of nonsparse targets.

Developing efficient electrocatalysts for industrial alkaline hydrogen evolution reaction (HER) is important for alleviating energy problems and achieving decarbonization. Here, a multimetal alloyed NiMoCo coating was in situelectrodeposited on Ni mesh as an HER electrocatalyst. The obtained cathode exhibits ultrahigh HER activity in alkaline environments, requiring only low overpotentials of 13.7 and 146.0 mV in 1 M KOH to achieve current densities of 10 and 200 mA cm–2, along with a robust stability of over 120 h at an operating current density of 400 mA cm–2. The introduction of Co elements into NiMo-based alloys could significantly change the surface morphology from a planar to a three-dimensional structure for exposing abundant active sites; moreover, strong electronic interaction between multimetal alloyed sites is beneficial to modulate alkaline HER activity. The full alkaline water splitting electrolyzer, which consisted of electrodeposited multimetal alloyed NiMoCo as the cathode and NiMoFe as the anode, was constructed, showing a cell voltage as low as 1.437 V to obtain the current density of 10 mA cm–2and 1.703 V for 200 mA cm–2. This work would pave a new avenue to design and fabricate multimetal alloyed electrocatalysts directly grown on an industrial porous conductive substrate.