Influence of Pnictogen and Ligand Framework on the Lewis Acidity and Steric Environment in Pnictogen Pincer Complexes
Bibliographic record
Abstract
Pnictogen pincer complexes are a fascinating class of compounds due to their dynamic molecular and electronic structures, and valuable stoichiometric or catalytic reactivity. As recognition of their unique chemistry has grown, so too has the library of pincer ligands employed and pnictogen centres engaged to prepare them. Here we computationally study how the choice of pincer ligand framework and pnictogen influence the electronic and steric outcomes of the complexes obtained. The most relevant electronic parameter is the pnictogen-centred electrophilicity, which has been quantified by fluoride ion affinities and LUMO energies, while the most relevant steric parameter is the crowding around the central pnictogen, which has been quantified by the %Vbur values and visualized using steric maps. The resulting trends are analyzed with reference to binding pocket size, acceptor orbital type, electronic delocalization, π-donor strengths, and heteroatom incorporation. Thus, considering 16 ligand frameworks and 4 heavy pnictogen centres, this study provides a broad-spectrum view of stereo-electronic variation in pnictogen pincer complexes, which, together with a recent study on geometric variation in the same family, provides a substantial dataset to guide future molecular design and reactivity studies.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".