Homogenous Meets Heterogenous and Electro‐Catalysis: Iron‐Nitrogen Molecular Complexes within Carbon Materials for Catalytic Applications
Bibliographic record
Abstract
Abstract High activity, selectivity and recyclability are crucial parameters in the design of performant catalysts. Furthermore, depletion of platinum‐group metals (PGM) drives further research towards highly available metal‐based catalysts. In this framework, iron‐based active sites supported on nitrogen‐doped carbon materials (Fe/N@C) have been explored to tackle important applications in organic chemistry, for both oxidation and reduction of C−O/C−N bonds, as well as in electrocatalysis for energy applications. This versatile reactivity makes them ideal substitutes to PGM‐based catalysts, being based on abundant elements. Despite important advances in material science and characterisation techniques allowing the analysis of heterogeneous/electro‐ catalysts at the atomic scale, the nature of the catalytically active sites in Fe/N@C remains elusive. Most recent theoretical studies point at individual FeNx single sites as the origin of the catalytic activity. Although their identification is still challenging with current technology, establishing their real nature will foster further research on these PGM‐free and redox‐polyvalent catalysts. In this review, we provide an overview of their applications in both thermal and electrochemical processes. Throughout the review, we highlight the different characterisation techniques employed to gain insight into the catalyst's active sites.
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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 | 0.001 |
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".