Key Takeaways & Executive Findings
- •• • The proposed selectivity descriptors ΔΔG and ΔU provide a quantitative benchmark: ΔΔG (ΔG_N2 − ΔG_H) identifies the potential range where N2 adsorption dominates over H adsorption, while ΔU (U_cross – U_eNRR) specifies the potential range to trigger direct eNRR, enabling rational catalyst design to overcome HER competition. • • Confined dual-atom configurations with optimized interatomic distances achieve simultaneous overwhelming N2 adsorption and sufficient activation, enabling ammonia synthesis with industrially relevant production rates and current density even at elevated potentials, directly addressing the premature decay in NH3 yield observed across reported catalysts. • • The study employs constant-potential and solvation models within DFT (e.g., RPBE functional, DFT-D3 dispersion correction, and implicit solvation) to accurately capture potential-dependent competition, providing mechanistic insights into the root causes of eNRR performance limitations. • • The work establishes a rational design framework for high-performance catalysts across a broad range of electrochemical transformations, moving beyond trial-and-error approaches by linking electronic structure descriptors to macroscopic performance metrics.
Abstract
The premature decay of electrochemical nitrogen reduction reaction (eNRR) performance at low electrode potentials remains a major obstacle to practical applications, primarily due to competition from the hydrogen evolution reaction (HER). A new paradigm capable of transcending current selectivity constraints is urgently required to advance eNRR toward industrial implementation. In this work, we propose two practical selectivity descriptors (ΔΔG and ΔU) based on a systematic investigation of the potential-dependent competition between eNRR and HER on confined dual-atom catalysts. The descriptor ΔΔG (ΔG_N2 − ΔG_H) identifies the potential range where N2 adsorption dominates over H adsorption, while ΔU (U_cross – U_eNRR) specifies the potential range to trigger direct eNRR, offering a quantitative benchmark for rational catalyst design. Ideal catalysts should maintain N2-preferential adsorption across a broad potential window to facilitate direct eNRR. Guided by this insight, we demonstrate that confined dual-atom configurations with optimized interatomic distances can simultaneously achieve both overwhelming N2 adsorption and sufficient activation, thereby overcoming conventional selectivity limitations. This strategy enables ammonia synthesis with industrially relevant production rates and current density even at elevated potentials. Our mechanistic insights not only elucidate the root causes of performance limitations in eNRR but also offer a rational design framework for developing high-performance catalysts across a broad range of electrochemical transformations.
1. Introduction
The electrochemical nitrogen reduction reaction (eNRR) offers a sustainable route to ammonia synthesis under ambient conditions, yet its industrial viability is undermined by two persistent challenges: insufficient activity due to the chemical inertness of N2, and severe selectivity limitations caused by the competing hydrogen evolution reaction (HER). HER, a kinetically favorable 2H+/2e− process, outcompetes the 6H+/6e− eNRR pathway, leading to a characteristic volcanic relationship between NH3 yield and applied potential. Across all reported catalysts, NH3 yield decays prematurely at negative potentials before reaching mass-transfer limits, a phenomenon primarily attributed to intrinsic catalyst properties. This premature decay hampers achieving reasonable production rates at industrially relevant potentials, necessitating innovative catalyst design strategies that can transcend conventional selectivity constraints.
This work introduces a paradigm shift by proposing two practical selectivity descriptors, ΔΔG and ΔU, derived from systematic investigation of potential-dependent eNRR vs. HER competition on confined dual-atom catalysts. The descriptor ΔΔG identifies the potential window where N2 adsorption dominates over H adsorption, while ΔU specifies the potential range to trigger direct eNRR. Guided by these descriptors, the authors demonstrate that confined dual-atom configurations with optimized interatomic distances can simultaneously achieve overwhelming N2 adsorption and sufficient activation, thereby overcoming the selectivity bottleneck. This strategy enables ammonia synthesis with industrially relevant production rates and current densities even at elevated potentials, offering a rational design framework for high-performance catalysts across a range of electrochemical transformations.
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Nana Hu, Xingshuai Lv, Guobo Chen, Thomas Frauenheim, Liangzhi Kou (2026). Redefining selectivity paradigms in electrochemical nitrogen reduction reaction on confined dual-atom catalysts. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3739-8
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Frequently Asked Questions
How do the proposed descriptors ΔΔG and ΔU quantitatively guide catalyst design to overcome HER competition?
ΔΔG (ΔG_N2 − ΔG_H) quantifies the thermodynamic preference for N2 adsorption over H adsorption; a positive value indicates N2-preferential adsorption. ΔU (U_cross – U_eNRR) defines the potential window where direct eNRR is thermodynamically feasible. Catalysts with a broad potential window where ΔΔG > 0 and ΔU > 0 are predicted to maintain N2 adsorption and trigger eNRR, thus overcoming HER. The study validates this by showing confined dual-atom configurations with optimized interatomic distances achieve both overwhelming N2 adsorption and sufficient activation, enabling industrially relevant NH3 production rates.
What computational methods were employed to accurately model the electrochemical interface and potential dependence?
The study utilized density functional theory (DFT) with the revised Perdew-Burke-Ernzerhof (RPBE) functional for improved adsorption energetics, DFT-D3 dispersion correction for van der Waals interactions, and an implicit solvation model (VASPsol) to account for electrolyte effects. Constant-potential simulations were performed to capture the effect of applied electrode potential on adsorption energies and reaction barriers, as opposed to fixed-charge methods. This approach allows for accurate prediction of potential-dependent competition between eNRR and HER.
What are the practical implications of achieving industrially relevant NH3 production rates at elevated potentials?
Elevated potentials typically suppress NH3 yield due to enhanced HER, but the proposed confined dual-atom catalysts maintain high selectivity and activity even at more negative potentials. This means that in practical electrolyzers, higher current densities can be applied without sacrificing Faradaic efficiency, leading to higher NH3 production rates per unit area. This is crucial for meeting industrial productivity targets and reducing capital costs.
How do the selectivity descriptors account for the dynamic nature of the electrochemical interface, such as solvation and electric double-layer effects?
The descriptors are derived from free energy calculations that include solvation corrections via implicit solvation models and account for potential-dependent adsorption energies through constant-potential DFT. This captures the shift in adsorption free energies with applied potential, which is essential for predicting the crossover potential (U_cross) where HER becomes dominant. The methodology thus integrates key interfacial effects into a simple descriptor framework.
What are the limitations of the proposed descriptors and computational approach for practical catalyst screening?
The descriptors are based on thermodynamic adsorption free energies and may not fully capture kinetic barriers or dynamic restructuring of catalysts under operating conditions. Additionally, the computational screening relies on idealized models of the catalyst surface and electrolyte, which may not fully represent the complexity of real electrochemical systems. However, the descriptors provide a first-order screening criterion that can be combined with microkinetic modeling and experimental validation to accelerate catalyst discovery.
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