Progress and perspective of single‐atom catalysts for membrane electrode assembly of fuel cells
Bibliographic record
Abstract
Abstract A fuel cell is an energy conversion device that can continuously input fuel and oxidant into the device through an electrochemical reaction to release electrical energy. Although noble metals show good activity in fuel cell‐related electrochemical reactions, their ever‐increasing price considerably hinders their industrial application. Improvement of atom utilization efficiency is considered one of the most effective strategies to improve the mass activity of catalysts, and this allows for the use of fewer catalysts, saving greatly on the cost. Thus, single‐atom catalysts (SACs) with an atom utilization efficiency of 100% have been widely developed, which show remarkable performance in fuel cells. In this review, we will describe recent progress on the development of SACs for membrane electrode assembly of fuel cell applications. First, we will introduce several effective routes for the synthesis of SACs. The reaction mechanism of the involved reactions will also be introduced as it is highly determinant of the final activity. Then, we will systematically summarize the application of Pt group metal (PGM) and nonprecious group metal (non‐PGM) catalysts in membrane electrode assembly of fuel cells. This review will offer numerous experiences for developing potential industrialized fuel cell catalysts in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".