Perovskite oxides for electrocatalytic nitrogen/carbon fixation
Bibliographic record
Abstract
The electrochemical conversion of carbon and nitrogen species provides a sustainable way to reduce carbon dioxide emissions and address reactive nitrogen pollution. Perovskite oxides have shown broad application prospects in the field of electrocatalytic carbon/nitrogen fixation attributable to their tunable electronic structure, abundant oxygen vacancies and low cost. Their inherent ability to regulate electronic structure, defect states, and surface coordination environment enables them to selectively activate and convert inert molecules under mild conditions. This paper systematically reviews the progress of perovskite oxides in the field of electrocatalytic carbon/nitrogen fixation in recent years, with special emphasis on effective design strategies, including doping engineering, defect engineering, heterostructures and crystal face engineering. In addition, this work deeply analyzes the main challenges currently faced and proposes prospects for future development directions, including the precise design of high-performance catalysts, in-depth analysis of reaction mechanisms, stability improvement strategies, and the development of large-scale application technologies. By multidisciplinary cross-integration, perovskite oxide electrocatalysis technology holds great potential to contribute to carbon neutrality and green chemical synthesis, providing innovative solutions for sustainable development.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".