Density functional theory study of substituent effects on gas‐phase heterolytic Fe–O and Fe–S bond energies of <i>m</i>‐G‐C<sub>6</sub>H<sub>4</sub>OFe(CO)<sub>2</sub>(η<sup>5</sup>‐C<sub>5</sub>H<sub>5</sub>) and <i>m</i>‐G‐C<sub>6</sub>H<sub>4</sub>SFe(CO)<sub>2</sub>(η<sup>5</sup>‐C<sub>5</sub>H<sub>5</sub>)
Bibliographic record
Abstract
The knowledge of accurate bond strengths is a fundamental basis for a proper analysis of chemical reaction mechanisms. Quantum chemical calculations at different levels of theory have been used to investigate heterolytic Fe–O and Fe–S bond energies of (meta‐substituted phenoxy)dicarbonyl(η 5 ‐cyclopentadienyl) iron [ m ‐G‐C 6 H 4 OFp ( 1 )] and (meta‐substituted benzenethiolato)dicarbonyl(η 5 ‐cyclopentadienyl) iron [ m ‐G‐C 6 H 4 SFp ( 2 )] complexes. In this study, Fp is (η 5 ‐C 5 H 5 )Fe(CO) 2 , and G is NO 2 , CN, COMe, CO 2 Me, CF 3 , Br, Cl, F, H, Me, MeO, and NMe 2 . The results show that Tao–Perdew–Staroverov–Scuseria and Becke's power‐series ansatz from 1997 with dispersion corrections functionals can provide the best price/performance ratio and accurate predictions of Δ H het (Fe–O)'s and Δ H het (Fe–S)'s. The excellent linear free energy relations [ r = 1.00 (g, 1e), 1.00 (g, 2b)] among the ΔΔ H het (Fe–O)'s and δΔ G 0 of OH bonds of m ‐G‐C 6 H 4 OH or ΔΔ H het (Fe–S)'s and Δp K a 's of SH bonds of m ‐G‐C 6 H 4 SH imply that the governing structural factors for these bond scissions are similar. And, the linear correlations [ r = −0.97 (g, 1 g), −0.97 (g, 2 h)] among the ΔΔ H het (Fe–O)'s or ΔΔ H het (Fe–S)'s and the substituent σ m constants show that these correlations are in accordance with Hammett linear free energy relationships. The inductive effects of these substituents and the basis set effects influence the accuracy of Δ H het (Fe–O)'s or Δ H het (Fe–S)'s. The ΔΔ H het (Fe–O)'s(g) (1) and ΔΔ H het (Fe–S)'s(g)(2) follow the capto‐dative Principle. The substituent effects on the Fe–O bonds are much stronger than those on the less polar Fe–S bonds. Insight from this work may help the design of more effective catalytic processes. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.011 | 0.011 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".