{Fe(NO)<sub>2</sub>}<sup>9</sup> Dinitrosyl Iron Complex Acting as a Vehicle for the NO Radical
Bibliographic record
Abstract
To carry and deliver nitric oxide with a controlled redox state and rate is crucial for its pharmaceutical/medicinal applications. In this study, the capability of cationic {Fe(NO) 2 } 9 dinitrosyl iron complexes (DNICs) [( R DDB)Fe(NO) 2 ] + (R = Me, Et, Iso; R DDB = N,N ′-bis(2,6-dialkylphenyl)-1,4-diaza-2,3-dimethyl-1,3-butadiene) carrying nearly unperturbed nitric oxide radical to form [( R DDB)Fe(NO) 2 ( • NO)] + was demonstrated and characterized by IR, UV–vis, EPR, NMR, and single-crystal X-ray diffractions. The unique triplet ground state of [( R DDB)Fe(NO) 2 ( • NO)] + results from the ferromagnetic coupling between two strictly orthogonal orbitals, one from Fe d z 2 and the other a π* op orbital of a unique bent axial NO ligand, which is responsible for the growth of a half-field transition (Δ M S = 2) from 70 to 4 K in variable-temperature EPR measurements. Consistent with the NO radical character of coordinated axial NO ligand in complex [( Me DDB)Fe(NO) 2 ( • NO)] +, the simple addition of MeCN/H 2 O into CH 2 Cl 2 solution of complexes [( R DDB)Fe(NO) 2 ( • NO)] + at 25 °C released NO as a neutral radical, as demonstrated by the formation of [S 5 Fe(NO) 2 ] − from [S 5 Fe(μ-S) 2 FeS 5 ] 2– .
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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) | 0.001 | 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".