Iron L<sub>2,3</sub>-Edge X-ray Absorption and X-ray Magnetic Circular Dichroism Studies of Molecular Iron Complexes with Relevance to the FeMoco and FeVco Active Sites of Nitrogenase
Bibliographic record
Abstract
Abstract Herein, a systematic study of a series of molecular iron model complexes has been carried out using Fe L2,3-edge X-ray absorption (XAS) and X-ray magnetic circular dichroism (XMCD) spectroscopies. This series spans iron complexes of increasing complexity, starting from ferric and ferrous tetrachlorides ([FeCl4]−/2–), to ferric and ferrous tetrathiolates ([Fe(SR)4]−/2–), to diferric and mixed-valent iron–sulfur complexes [Fe2S2R4]2–/3–. This test set of compounds is used to evaluate the sensitivity of both Fe L2,3-edge XAS and XMCD spectroscopy to oxidation state and ligation changes. It is demonstrated that the energy shift and intensity of the L2,3-edge XAS spectra depends on both the oxidation state and covalency of the system; however, the quantitative information that can be extracted from these data is limited. On the other hand, analysis of the Fe XMCD shows distinct changes in the intensity at both L3 and L2 edges, depending on the oxidation state of the system. It is also demonstrated that the XMCD intensity is modulated by the covalency of the system. For mononuclear systems, the experimental data are correlated with atomic multiplet calculations in order to provide insights into the experimental observations. Finally, XMCD is applied to the tetranuclear heterometal–iron–sulfur clusters [MFe3S4]3+/2+ (M = Mo, V), which serve as structural analogues of the FeMoco and FeVco active sites of nitrogenase. It is demonstrated that the XMCD data can be utilized to obtain information on the oxidation state distribution in complex clusters that is not readily accessible for the Fe L2,3-edge XAS data alone. The advantages of XMCD relative to standard K-edge and L2,3-edge XAS are highlighted. This study provides an important foundation for future XMCD studies on complex (bio)inorganic systems.
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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) | 0.004 | 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".