Employable Electrocatalysts: a DFT Investigation of NORR using Porphyrin-Based Transition Metal SACs
Bibliographic record
Abstract
Nitric Oxide (NO) is an airborne pollutant released into the atmosphere by the combus- tion of fossil fuels. Its toxicity has short- and long term effects, including respiratory toxicity, DNA damage, and interference with the oxygen-delivery capacity of blood heme. Here, the reduction of Nitric Oxide to ammonia (NORR) by transition metal single atom cat- alysts within an unreactive porphyrin structure is examined theoretically. Density Func- tional Theory calculations are used to investigate the energetic relationships between reac- tion steps and to assess the overall catalytic ability of each catalyst. The adsorption and reaction pathways for NO reduction on a series of metalloporphyrin catalysts are iden- tified. Among several catalysts examined here, Cr-porphyrin emerges as the most likely NORR candidate, demonstrating exergonic adsorption of NO and minimum overpotential. The adsorption of NO is identified as the critical step, and the associated change in free energy is found to heavily influence the energy landscape of the overall reduction reaction. These insights establish Cr metalloporphyrins as potential NORR catalysts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".