Recent Advances in MOF‐Derived Single Atom Catalysts for Electrochemical Applications
Bibliographic record
Abstract
Abstract Electrocatalysis plays a critical role in clean energy conversion, enabling great improvement for future sustainable technologies. Single atom catalysts (SACs) derived from metal–organic framework (MOF) are emerging extraordinary materials in electrochemical catalytic applications. Covering the merits of unique electronic structure, low‐coordination environment, quantum size effect, and metal–support interaction, SACs promise enhanced electrocatalytic activity, stability, and selectivity in the field of clean energy conversion. In this article, MOF synthesis routes to afford well‐dispersed SACs along with the respective synthesis mechanism are systematically reviewed first, and typical examples of each strategy are carefully discussed. Then the characterization techniques in understanding the isolated and spatial distribution, local electronic structure, coordination environment for SACs, and insights into stable mechanisms provided by density functional theory (DFT) calculations are summarized. In addition, several important electrocatalytic applications and electrocatalytic mechanisms of the MOF‐derived SACs, including for the oxygen reduction reaction, CO 2 reduction reaction, nitrogen reduction reaction, hydrogen evolution reaction, oxygen evolution reaction, etc., are highlighted. To facilitate the future development of high‐performing SACs, several technical challenges and corresponding research directions are proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".