Biomass‐derived nonprecious metal catalysts for oxygen reduction reaction: The demand‐oriented engineering of active sites and structures
Bibliographic record
Abstract
Abstract Oxygen reduction reaction (ORR) is an important electrochemical process for renewable energy conversion and storage applications such as fuel cells and metal‐air batteries. ORR is sluggish in kinetics and requires a large amount of platinum group metal (PGM)‐based catalysts to facilitate its slow reaction rate. Application of precious metals raises the cost and decreases the competitivity of these devices in the market. To address this challenge, PGM‐free ORR catalysts have been intensively investigated as an alternative to replace the PGM‐based catalysts and to promote the deployment of ORR‐related applications. In particular, the biomass holds promising potential to be used as the precursor material for PGM‐free ORR catalysts. This pathway has gained more and more attention in recent years. In this review, recent advances regarding biomass‐derived ORR catalysts are summarized with a focus on the rational design of both active sites and porous structures which are the two key factors in determining ORR performance of catalysts. At the end, the perspectives of development of biomass‐derived catalysts is discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".