A Single‐Atom Iridium Heterogeneous Catalyst in Oxygen Reduction Reaction
Bibliographic record
Abstract
Abstract Combining the advantages of homogeneous and heterogeneous catalysts, single‐atom catalysts (SACs) are bringing new opportunities to revolutionize ORR catalysis in terms of cost, activity and durability. However, the lack of high‐performance SACs as well as the fundamental understanding of their unique catalytic mechanisms call for serious advances in this field. Herein, for the first time, we develop an Ir‐N‐C single‐atom catalyst (Ir‐SAC) which mimics homogeneous iridium porphyrins for high‐efficiency ORR catalysis. In accordance with theoretical predictions, the as‐developed Ir‐SAC exhibits orders of magnitude higher ORR activity than iridium nanoparticles with a record‐high turnover frequency (TOF) of 24.3 e− site−1 s−1 at 0.85 V vs. RHE) and an impressive mass activity of 12.2 A mg−1Ir, which far outperforms the previously reported SACs and commercial Pt/C. Atomic structural characterizations and density functional theory calculations reveal that the high activity of Ir‐SAC is attributed to the moderate adsorption energy of reaction intermediates on the mononuclear iridium ion coordinated with four nitrogen atom sites.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".