Atomically dispersed metal site materials for hydrogen energy utilization: Theoretical and experimental study in fuel cells and water electrolysis
Bibliographic record
Abstract
Atomically dispersed metal site (ADMS) materials have emerged as a promising class of materials for electrocatalysis reactions in the field of energy conversion. Characterized by individual metal atoms dispersed on suitable supports, ADMS materials provide unique catalytic sites with highly tunable electronic structures. This review summarizes recent advancements in the field, with a focus on the critical roles of support materials, coordination environments, and the mechanisms underlying catalytic activity at the atomic level. First, commonly used density functional theory (DFT) simulations are reviewed, emphasizing their pivotal role in elucidating reaction mechanisms and predicting the behavior of ADMS in electrochemical reactions for hydrogen energy utilization. Then, advancements in ADMS for half-cell electrochemical reactions, including oxygen evolution reaction, hydrogen evolution reaction, and oxygen reduction reaction, as well as their applications in fuel cells and water splitting, are summarized. Finally, the challenges and future prospects of ADMS are discussed. This review underscores the transformative potential of ADMS in electrocatalysis, paving the way for innovative and sustainable energy conversion 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".