Rational engineering of metal-oxide interfaces presents a powerful avenue for tailoring the catalytic properties of metal nanoparticles. However, the strategy of focusing on a single interface offers limited scope for tuning the performance. Herein, we transcend this limitation by constructing a dual-oxide/alloy interface to concurrently boost the activity and stability of oxygen reduction reaction (ORR) catalysts. Using a PtNi alloy as a model system, we demonstrate that the synergistic interplay between two distinct oxides (PtO and NiO) and the PtNi alloy creates a uniquely tailored interfacial microenvironment (NiO-PtO/PtNi) which can improve the kinetics of the ORR. The stability of the NiO-PtO/PtNi dual-oxide/alloy interface structure during the actual ORR operation conditions is confirmed by in situ Raman spectra. The resulting catalyst exhibits exceptional performance, achieving a half-wave potential of 0.97 V, with mass and specific activities of 3.2 A/mg_Pt and 5.3 mA/cm², 9.2 and 4.4 times greater than commercial Pt/C (40 wt%), respectively, alongside outstanding durability (negligible decay after 50,000 cycles). A combination of in situ infrared spectroscopy and first-principles calculations reveals that this multi-component interface optimizes the Pt d-band center, thereby regulating the adsorption of oxygen-containing intermediates and altering the rate-determining step of the ORR. This study underscores the profound potential of multi-component interface engineering for advancing the design of high-performance Pt-based electrocatalysts.

Herein we proposed a data-driven high-throughput principle to screen high-performance single-atom materials for hydrogen evolution reaction (HER) and hydrogen sensing by combing the theoretical computations and a topology-based multi-scale convolution kernel machine learning algorithm. After the rational training by 25 groups of data and prediction of all 168 groups of single-atom materials for HER and sensing, respectively, a high prediction accuracy (> 0.931 R² score) was achieved by our model. Results show that the promising HER catalysts include Pt atoms in C₄ and Sc atoms in C₁N₃ coordination environment. Moreover, Y atoms in C₄ coordination environment and Cd atoms in C₂N₂-ortho coordination environment were predicted with great potential as hydrogen sensing materials. This method provides a way to accelerate the discovery of innovative materials by avoiding the time-consuming empirical principles in experiments.