Strategies to achieve effective nitrogen activation
Bibliographic record
Abstract
Abstract Ammonia serves as a crucial chemical raw material and hydrogen energy carrier. Aqueous electrocatalytic nitrogen reduction reaction (NRR), powered by renewable energy, has attracted tremendous interest during the past few years. Although some achievements have been revealed in aqueous NRR, significant challenges have also been identified. The activity and selectivity are fundamentally limited by nitrogen activation and competitive hydrogen evolution. This review focuses on the hurdles of nitrogen activation and delves into complementary strategies, including materials design and system optimization (reactor, electrolyte, and mediator). Then, it introduces advanced interdisciplinary technologies that have recently emerged for nitrogen activation using high‐energy physics such as plasma and triboelectrification. With a better understanding of the corresponding reaction mechanisms in the coming years, these technologies have the potential to be extended in further applications. This review provides further insight into the reaction mechanisms of selectivity and stability of different reaction systems. We then recommend a rigorous and detailed protocol for investigating NRR performance and also highlight several potential research directions in this exciting field, coupling with advanced interdisciplinary applications, in situ/operando characterizations, and theoretical calculations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".