A novel tetragonal T-C2N supported transition metal atoms as superior bifunctional catalysts for OER/ORR: From coordination environment to rational design

Navigating the evolution of two-dimensional carbon nitride research: integrating machine learning into conventional approaches

Carbon nitride research has reached a promising point in today's research endeavours with diverse applications including photocatalysis, energy storage, and sensing due to their unique electronic and structural properties. Recent advances in machine learning (ML) have opened new avenues for exploring and optimizing the potential of these materials. This study presents a comprehensive review of the integration of ML techniques in carbon nitride research with an introduction to CN classifications and recent advancements. We discuss the methodologies employed, such as supervised learning, unsupervised learning, and reinforcement learning, in predicting material properties, optimizing synthesis conditions, and enhancing performance metrics. Key findings indicate that ML algorithms can significantly reduce experimental trial-and-error, accelerate discovery processes, and provide deeper insights into the structure-property relationships of carbon nitride. The synergistic effect of combining ML with traditional experimental approaches is highlighted, showcasing studies where ML driven models have successfully predicted novel carbon nitride compositions with enhanced functional properties. Future directions in this field are also proposed, emphasizing the need for high-quality datasets, advanced ML models, and interdisciplinary collaborations to fully realize the potential of carbon nitride materials in next-generation technologies.

Semimetallic state transition in Two-Dimensional carbon nitride covalent networks and enhanced electrocatalytic activity for nitrate to ammonia conversion

Single-atom catalysts (SACs), due to their maximum atomic utilization rate, show tremendous potential for application in the electrocatalytic synthesis of ammonia from nitrate. Yet, the development of superior supports that preserve the high selectivity, activity, and stability of SACs remains an imperative challenge. In this work, based on first-principles calculations and tight-binding (TB) model analysis, a new two-dimensional (2D) carbon nitride monolayer, C7N6, is proposed. The C7N6 structure exhibits a strong covalent network, with dynamical, thermal, and mechanical stability. Surprisingly, the structural transition from C9N4 to C7N6 corresponds to a semimetallic state transition. Further symmetry analysis unveils that the Dirac states in C7N6 are protected by space-time inversion symmetry, and the physical origin of the Dirac cone was confirmed using the TB model. Additionally, a non-zero Z2 invariant and significant topological edge states demonstrate its topologically nontrivial nature. Considering the excellent structural and topological properties of C7N6, a three-step screening strategy is designed to identify eligible SACs for electrochemical nitrate reduction reaction (NO3RR), and Ti@C7N6 is identified as possessing the best activity, with the last proton-electron coupling step *NH2→*NH3 being the potential-determining step (PDS), for which the limiting potential is 0.48 V. Moreover, a free energy diagram shows that the *NOH reaction pathway is energetically preferred on Ti@C7N6, and ab initio molecular dynamics (AIMD) calculations at 500 K confirm its good thermal stability. Our study not only provides excellent CN-based support material but also offers theoretical guidance for constructing highly active and selective SACs for nitrate reduction.

Designing asymmetrical TMN4 sites via phosphorus or sulfur dual coordination as high-performance electrocatalysts for oxygen evolution reaction

The development ofhighly efficient oxygen evolution reaction (OER) catalysts based on more cost-effective and earth-abundant elements is of great significance and still faces a huge challenge. In this work, a series of transition metal (TM)embedding a newly-defined monolayer carbon nitride phase is theoretically profiled and constructed as a catalytic platform for OER studies. Typically, a four-step screening strategy was proposed to rapidly identified high performance candidates and the coordination structure and catalytic performance relationship was thoroughly analyzed. Moreover, the eliminating criterion was established to condenses valid range based on the Gibbs free energy of OH*. Our results reveal that the as-constructed 2FeCN/P exhibits superior activity toward OER with an ultralow overpotential of 0.25 V, at the same time, the established 3FeCN/S configuration performed well as abifunctional OER/ORR electrocatalysis with extremely low overpotential ηOER/ηORR of 0.26/0.48 V. Overall, this work provides an effective framework for screening advanced OER catalysts, which can also be extended to other complex multistep catalytic reactions.

Unraveling the activity trends of T-C2N based Single-Atom catalysts for electrocatalytic nitrate reduction via high-throughput screening

Electrochemical nitrate reduction reaction (NO3RR) offers a cost-effective and environmentally friendly method to simultaneously yield valuable NH3and alleviate NO3-pollution under mild operating conditions.However, this complicated eight-electron reaction suffers from low selectivity and Faradaic efficiency, which highlight the importance of developing efficient catalysts, but still a critical challenge. Here, a theoretical screening is performed on transition metal-tetragonal carbon nitride (TM@T-C2N) as active and selective electrocatalysts for NO3RR, where detailed reaction mechanisms and activity origins are explored. In addition, five-step screening criteria and volcano plots enable fast prescreening among numerous candidates.We identify that V@T-C2N and Cr@T-C2N are promising candidates with low overpotentials and high selectivity and stability. In particular, a significant negative correlation between the adsorption strength ofnitrate and the Gibbs free energy for the last proton-electron coupling step (*NH2→*NH3) was existed, which is considerably advantaged to track the activity trend and reveal the origin of activity. This work provides theoretical insights into the rational design of TM-N4/C catalysts for NO3RR andpaves a valuable electrochemical screening framework for other multi-step reactions.