Bonding Interactions in Olefin (C<sub>2</sub>X<sub>4</sub>, X = H, F, Cl, Br, I, CN) Iron Tetracarbonyl Complexes: Role of the Deformation Energy in Bonding and Reactivity
Bibliographic record
Abstract
The iron−olefin bond energies for the monoolefin iron tetracarbonyl complexes Fe(CO) 4 (C 2 X 4 ) (X = H, F, Cl, Br, I, CN) have been determined using density functional theory (DFT), with the BP86 functional. An energy decomposition analysis of the bonding interactions demonstrate that, as predicted by current models of metal−olefin bonding, the attractive electronic interactions of the haloolefins and percyanoethylene with iron are stronger than those of ethylene. However, in addition to these electronic interactions the net bond energy depends on the energy needed to deform the Fe(CO) 4 and olefin moieties from their equilibrium geometries to the geometrical conformation they adopt in the complex. This energy is termed the deformation energy. As a result of the deformation energy, the bond energies for the substituted olefins are similar to or smaller than that of the Fe−C 2 H 4 bond. More than half of the total deformation energy involves deforming the olefin, principally as a result of a change in hybridization of the carbon atoms from sp 2 in the free olefin toward an sp 3 -like carbon in the bound olefin. The deformation of Fe(CO) 4 involves mainly the axial CO ligands, which bend away from the olefin as a result of a repulsive interaction with the olefin substituents. In addition, the increase in the C−X bond length, upon bonding of the olefin to Fe(CO) 4, correlates well with the exothermicity of the oxidative addition reaction, Fe(CO) 4 (C 2 X 4 ) → XFe(CO) 4 (C 2 X 3 ), indicating that the deformation of the bound olefin lowers the energy of the C−X bond.
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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) | 0.002 | 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".