Recent Progress Toward Electrocatalytic Conversion of Nitrobenzene
Bibliographic record
Abstract
Despite that extensive efforts have been dedicated to the search for advanced catalysts to boost the electrocatalytic nitrobenzene reduction reaction (eNBRR), its progress is severely hampered by the limited understanding of the relationship between catalyst structure and its catalytic performance. Herein, this review aims to bridge such a gap by first analyzing the eNBRR pathway to present the main influential factors, such as electrolyte feature, applied potential, and catalyst structure. Then, the recent advancements in catalyst design for eNBRR are comprehensively summarized, particularly about the impacts of chemical composition, morphology, and crystal facets on regulating the local microenvironment, electron and mass transport for boosting catalytic performance. Finally, the future research of eNBRR is also proposed from the perspectives of performance enhancement, expansion of product scope, in-depth understanding of the reaction mechanism, and acceleration of the industrialization process through the integration of upstream and downstream technologies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".