Modulating the oxygen affinity of porphyrins with metals, ligands, and functional groups: A <scp>DFT</scp> study
Bibliographic record
Abstract
Abstract The interaction between different metals (M), axial ligands (L), and ring substituents (R) in porphyrins was investigated using density functional theory. Different combinations of iron and cobalt as metal centers; imidazole, chlorine, and an n‐heterocyclic carbene (NHC) as axial ligands, and unsubstituted, octaethyl‐, and tetraphenyl‐porphyrins were explored in their low, intermediate, and high‐spin states, alongside oxygen affinity. Remarkably, the n‐heterocyclic carbene enhanced the affinity of cobalt porphyrins to oxygen, with binding energies on average 4.4 kcal mol −1 higher than FeP with the same ligand, and 0.78 kcal mol −1 higher than FeP with imidazole. The planarity of the iron tetraphenyl porphyrin with imidazole compared to its ruffled cobalt counterpart is noteworthy in both oxy‐ and deoxy‐forms, highlighting imidazole's stabilizing influence on the porphyrin structure, particularly iron porphyrins, alongside imidazole's stabilizing effect on the affinity to O 2 . Despite the significant non‐planarity induced by NHC as an axial ligand ‐regardless of the metal or ring substituent used‐, it did not hinder the affinity of CoP to O 2 (14.26 kcal mol −1 , on average) as it did with the FeP with NHC (9.88 kcal mol −1 , on average). Cobalt porphyrins with n‐heterocyclic carbene ligands show promising potential for O 2 activation or oxygen transport applications. The results show the complex interactions between the different parts of metalloporphyrins and highlight the capability of tailoring their affinity to O 2 . It also exemplifies the stabilizing effect of imidazole on the porphyrins, providing a very narrow range of binding energies and smaller differences in their geometries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".