Homolytic bond-dissociation enthalpies of tin bonds and tin–ligand bond strengths — A computational study
Bibliographic record
Abstract
Density functional theory and second-order Møller–Plesset perturbation theory with effective core potentials have been used to calculate homolytic bond-dissociation enthalpies, D(Sn–X), of organotin compounds, and their performance has been assessed by comparison with available experimental bond enthalpies. The SDB-aug-cc-pVTZ basis set with its effective core potential was used to calculate the D(Sn–X) of a series of trimethyltin(IV) species, Me3Sn–X, where X = H, CH3, CH2CH3, NH2, OH, Cl, and F. This is the most comprehensive report to date of homolytic Sn–X bond-dissociation enthalpies (BDEs). Effective core potentials are then used to calculate thermodynamic parameters including donor–acceptor bond enthalpies, [Formula: see text], for a series of tin-ligand complexes, L2SnX4 (X = Br or Cl, L = py, dmf, or dmtf), which are compared with previous experimental and nonrelativistic computational results. Based on computational efficiency and accuracy, it is concluded that effective core potentials are appropriate computational methods to examine bonding in organotin 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".