Screening Strategies for Diatom Metal-Doped β-Borophene Nanosheet Catalysts for Electrochemical Synthesis of Ammonia Using Density Functional Theory
Bibliographic record
Abstract
Electrocatalysis is a promising approach for ammonia production under ambient conditions. However, practically, owing to the competing hydrogen evolution reaction (HER), the Faradaic efficiency is low, and it is difficult to activate the inert N≡N. The theoretical screening method based on density functional theory calculations is a novel technique for developing and designing nitrogen reduction reaction (NRR) diatom catalysts (DACs). We proposed an NRR new type of DAC doped on β-borophene (B-β) nanosheets exhibiting enhanced catalytic activities for the NRR through the pull–pull effect of dual-metal sites and donor–acceptor coupling. By systematically evaluating the stabilities, selectivities, and activities of 24 diatom-doped β-borophene (M 2 @B-β) candidates, Mo 2 @B-β was found to be a promising NRR electrocatalyst with a low limiting potential of −0.23 V for the NRR and a high overpotential of −0.16 V for the HER. The limiting potentials for Cr 2 @B-β, Mn 2 @B-β, and Re 2 @B-β were −0.52, −0.51, and −0.35 V, respectively, making them suitable electrocatalysts for the NRR. Based on the Sabatier principle, we proposed two descriptors, Δ G (*N) and d-band center, as the criteria for screening and designing catalysts with high catalytic activities to reveal the intrinsic correlation between adsorption properties and structural properties. Based on the Δ G (*N) function, a volcano plot was built to predict trends in the activities. The difference, Δ G (*N 2 ) – Δ G (*H), was used as the selectivity descriptor, where the negative values of Δ G mean that the adsorption of N 2 dominates over the adsorption of H on the surface of M 2 @B-β. This study thus provides a feasible strategy for designing NRR electrocatalysts and is helpful for the fast screening of efficient DACs for other electroreduction reactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".