DFT studies of the σ-donor/π-acceptor properties of [SnCB<sub>10</sub>H<sub>11</sub>]<sup>–</sup> and its relationship to [SnCl<sub>3</sub>]<sup>–</sup>, CO, PF<sub>3</sub>, [SnB<sub>11</sub>H<sub>11</sub>]<sup>2–</sup>, SnC<sub>2</sub>B<sub>9</sub>H<sub>11</sub>, and related SnC<sub>2</sub>B<sub>n</sub>H<sub>n+2</sub> compounds
Bibliographic record
Abstract
The 1,2-carbastanna-closo-dodecaborate, [SnCB10H11]–, was found via DFT calculations to have intermediate σ-donor/π-acceptor properties between those of [SnB11H11]2– and 3,1,2-SnC2B9H11, and quite similar HOMO and LUMO energies and shapes to those of [SnCl3]–, which is a stronger σ-donor and weaker π-acceptor than CO or PF3. The non-carbon containing cluster [SnB11H11]2– is shown to be a very strong donor, whereas the dicarbon cluster 3,1,2-SnC2B9H11 is a poor σ-donor but good Lewis acid, consistent with experimental results. Thus, these systems can be tuned across a very wide range via isolobal replacement of [BH]– for CH vertices. The higher negative charge in the non-carbon-containing systems, as well as the fact that boron is more electropositive than carbon, contribute to the increased energies of both HOMO and LUMO in the stannaboranes compared to the stannacarboranes, explaining their relative donor and acceptor properties.Key words: DFT calculations, tin, borane cluster.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".