Xiumei Wei

The performance decay of the electrochemical nitrogen reduction reaction (eNRR) at negative potentials is a primary obstacle to industrial implementation, which is predominantly attributed to the competition from the hydrogen evolution reaction (HER). In this work, a novel tiered screening strategy is proposed by integrating the computational hydrogen electrode (CHE) model and grand canonical ensemble constant-potential simulations to systematically investigate the potential-dependent competition of NRR and HER on M1M2@C₆N₆. Among 55 candidate catalysts, ScMn@C₆N₆ and VFe@C₆N₆ possessing high activity and selectivity are screened out, since they maintain a thermodynamic advantage for N₂ adsorption over a broad potential window. The equilibrium potential (U_eq) is defined as a critical benchmark to quantify the transition from N₂-dominant to H-dominant adsorption, which provides a rational explanation for the premature decline in NRR yield before reaching the mass transfer limit. Furthermore, a dual-pathway strategy is constructed to identify the decisive role of interatomic distance by interpretable Machine Learning (ML), and derive novel descriptors for understanding N₂ activation by symbolic regression. This research establishes a comprehensive theoretical framework and methodological guidance for rational design of highly selective electrocatalysts under realistic reaction conditions.

Precisely constructing transition metal (TM) active sites in two-dimensional materials can enhance water-splitting, whereas the role of geometric factors such as interfacial curvature remain poorly understood. Nanotube (NT) provides a well-defined curved architecture that can modulate electron interactions and reactivity. Here, we rationally design a series of TM@g-C6N6 NTs and use first-principles calculations assisted by machine learning to elucidate how dimensional reduction and curvature govern water-splitting activity. We find that reduced curvature radius systematically promotes water splitting. Notably, the Ir- and Rh-decorated g-C6N6 NTs show remarkable electrocatalytic performance with calculated overpotentials of 0.05 eV for the hydrogen evolution reaction and 0.20 eV for the oxygen evolution reaction. Curvature-induced confinement drives reconstruction and reoccupation of the d-orbitals states near the Fermi level upon transitioning from a flat plane to a tubular geometry. More importantly, for different reaction intermediates, we identify a curvature-dependent inversion of the dominant adsorption orbital transition from the dz2 to the dx2-y2 , which directly correlates with enhanced catalytic activity. Our results establish interfacial curvature as a key manipulator for tuning electronic structure and water-splitting reactivity in TM@g-C6N6 NT single-atom catalysts and provide a new perspective on modulating electronic and catalytic properties in TM@g-C6N6 NT-like structures.

Strategically engineering heterojunctions through the integration of two or more monolayer materials presents a promising avenue for augmenting the efficiency of solar‐driven overall water splitting, which holds the potential for mitigating the escalating environmental challenges. Herein, based on first‐principles calculations, the functional type‐II g‐C ₆ N ₆ /As heterojunction is first constructed by g‐C ₆ N ₆ and As, then, systematically investigated its structural stability, optoelectronic properties and photocatalytic mechanism and potential for catalyzing water splitting, respectively. Owing to the band‐bending effect and the built‐in electric field induced across the heterojunction interface, the photogenerated electrons and holes on the surface could effectively separate and extend their carrier lifetimes. The heterojunction as a type‐II system photocatalyst with the hydrogen and oxygen evolution reactions occurring, respectively, happen at g‐C ₆ N ₆ and As surfaces. The heterojunction requires only an additional voltage of 0.29 V to ensure the photoinduced holes provide sufficient energy to drive the OER process. The introduction of single‐layer As could effectively adjust the reaction energy barrier of the HER activity for single‐layer g‐C ₆ N ₆ , thus ultimately significantly enhancing HER performance of heterojunction. More significantly, the heterojunction breaks the optical‐capturing obstacle of the g‐C ₆ N ₆ and exhibits strong optical capture capability in the regions from the infrared to visible light. Meanwhile, the value of the STH efficiency for heterojunction is up to 28.18%, which exceeds the value of the economically feasible requirement (10%). The above results are beneficial for the quantified design and application of photocatalytic heterojunction for overall water splitting and offer valuable insights for potential commercial implementations.

Compared with the traditional electrocatalyst screening of the nitrogen reduction reaction (NRR), machine learning (ML) has achieved high-throughput screening with less computational cost.

A series of bifunctional OER/ORR SACs supported on g-t-C₄N₃ is proposed. It is found from DFT calculations that the d-orbital structure can regulate the catalytic activity of g-t-C₄N₃-based SACs.