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We report a large spin-polarized current injection from a ferromagnetic metal into a nonferromagnetic semiconductor, at a temperature of 100 Kelvin. The modification of the spin-injection process by a nanoscale step edge was observed. On flat gallium arsenide [GaAs(110)] terraces, the injection efficiency was 92%, whereas in a 10-nanometer-wide region around a [111]-oriented step the injection efficiency is reduced by a factor of 6. Alternatively, the spin-relaxation lifetime was reduced by a factor of 12. This reduction is associated with the metallic nature of the step edge. This study advances the realization of using both the charge and spin of the electron in future semiconductor devices.

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It is vital to search for highly efficient bifunctional oxygen evolution/reduction reaction (OER/ORR) electrocatalysts for sustainable and renewable clean energy. Herein, we propose a single transition-metal (TM)-based defective AlP system to validate bifunctional oxygen electrocatalysis by using the density functional theory (DFT) method. We found that the catalytic activity is enhanced by substituting two P atoms with two N atoms in the Al vacancy of the TM-anchored AlP monolayer. Specifically, the overpotential of OER(ORR) in Co-and Ni-based defective AlP systems is found to be 0.38 (0.25 V) and 0.23 V (0.39 V), respectively, showing excellent bifunctional catalytic performance. The results are further presented by establishing the volcano plots and contour maps according to the scaling relation of the Gibbs free-energy change of *OH, *O, and *OOH intermediates. The d-band center and the product of the number of d-orbital electrons and electronegativity of the TM atom are the ideal descriptors for this system. To investigate the activity origin of the OER/ORR process, we performed the machine learning (ML) algorithm. The result indicates that the number of TM-d electrons (N e ), the radius of TM atoms (r d ), and the charge transfer of TM atoms (Q e ) are the three primary descriptors characterizing the adsorption behavior. Our results can provide a theoretical guidance for designing highly efficient bifunctional electrocatalysts and pave a way for the DFT−ML hybrid method in catalysis research.