High-throughput screening of single-atom catalysts confined in monolayer black phosphorus for efficient nitrogen reduction reaction
Bibliographic record
Abstract
The discovery of metal-nitrogen centers as the active sites for electrolysis has aroused significant interest in utilizing single-atom catalysts for nitrogen reduction reaction (NRR). Properly designed nanostructured catalysts that strongly interact with nitrogen molecules (N 2 ) can promote adsorption and activation, thereby resulting in efficient catalysts with high stability, activity, and selectivity. In this study, using density functional theory calculations, we selected monolayer black phosphorus (BP) as the substrate and screened a series of single-atom transition metals confined in tri-coordinated and tetra-coordinated active centers (without and with N dopants) to electro-catalyze NRR. As a result, we have identified two promising candidates (Hf 1 -N 1 P 2 -1 and Tc 1 -N 4 ), which exhibit not only low overpotentials of 0.56 and 0.49 V but also high thermodynamic and electrochemical stability, as well as good selectivity towards NRR over the competing hydrogen evolution reaction. We also demonstrate the ability of Hf 1 -N 1 P 2 -1 and Tc 1 -N 4 to activate and hydrogenate N 2 by donating electrons and regulating charge transfer. This study not only predicts new BP-based promising catalysts but also provides guidance for the rational design of high-performance NRR electrocatalysts under ambient conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".