Adjacent Axial Ligand Enables Secondary Coordination Effects in Metal-Nitrogen-Carbon Towards Improved Scaling Relation of Oxygen Electrocatalysis
Bibliographic record
Abstract
Heterogenous single-atom catalysts (SACs) are reminescent of homogenous catalysts because of similarity of structural motif of active sites, opening a window for applying merits of homogenous catalysts to address issues in hetereogenous catalysis. In heterogeneous oxygen electrocatalysis, the homogeneity of adsorption patterns of reaction intermediates leads to scaling relationships that limit their activities. In contrast, homogeneous catalysts can circumvent such limits by selectively altering the adsorption of intermediates through secondary coordination effects (SCE). This inspired us to explored SCE in metal-nitrogen-carbon (M-N-C), a promising type of oxygen electrocatalysts. First-principles calculations on M-N-C containing two adjacent four-nitrogen-coordinated metal centers show that axial ligand in proximate active sites induces SCE that selectively stabilize OOH intermediate, disrupt scaling relation between oxygen-species, and eventually increase the catalytic activity in oxygen evolution reactions. Additionally, the activity of oxygen reduction reaction of selected M-N-C is also enhanced by such SCE. Our computational work underscored the critical role of SCE in shaping activities of SACs, particularly in breaking stubborn scaling relationships, and demonstrated its potential of leveraging it to tackling challenges in heterogeneous catalysis.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".