Spectroscopic Characterization and Competitive Inhibition Studies of Azide Binding to a Functional NOR Model
Bibliographic record
Abstract
Abstract Azide binding to a functional nitric oxide reductase (NOR) model has been investigated in its mixed‐valence (LFeIIIFeII and LFeIIFeIII) and fully oxidized (LFeIIIFeIII) forms. FTIR and EPR spectroscopic methods indicate that azide binds in a bridging mode between the heme iron and the non‐heme iron sites. Terminal azide binding at both heme and non‐heme centers is also identified with the reactions of azide and the dinuclear compounds LFeIII/ZnII and LZnII/FeIII: the bound azide at the ferric heme center exhibits a vibrational frequency at 2007 cm–1 in the FTIR, while the bound azide at the ferric non‐heme site shows a band at 2055 cm–1. The reactivity of nitric oxide with the model compounds in the presence of excess azide was investigated. It is demonstrated that azide does not inhibit NO binding to the fully reduced catalyst but binds the FeB tightly in both the mixed‐valence and the fully oxidized states. These results further support the proposal that a bis‐ferrous instead of a mixed‐valence state is the active form of NOR.
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) | 0.001 | 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 | 0.000 | 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".