Dispersion Stabilizes Metal–Metal Bonds in the 1,8-Bis(silylamido)naphthalene Ligand Environment
Bibliographic record
Abstract
There is emerging consensus that stabilization of weak bonds using bulky substituents operates not only by steric shielding but also by boosting the dispersive attraction across the bond. While many studies have explored this concept for hydrocarbon, arene, carbene, and phosphine ligands, it remains minimally explored for amide ligands. Bulky 1,8-bis(silylamido) naphthalenes were recently used to isolate the first example of Sb–Bi σ-bonds, which was tentatively ascribed to an unexpectedly high degree of interfragment dispersive stabilization. To understand this finding and study how the interplay between steric repulsion and dispersive attraction alters metal–metal bond strengths more generally, we have computationally examined Sb–Sb, Sb–Bi, and Bi–Bi σ-bond enthalpies and energies in 21 compounds within the 1,8-bis(silylamido) naphthalenes ligand framework. The energies have been dissected into base electronic, London dispersion, and ligand deformation contributions. The London dispersion component has been further deconvoluted to identify the most significant pairwise functional group interactions driving stabilization from noncovalent interactions. Steric clashes have been considered by examining the extent of ligand deformation. The resulting insights will enable the rational evolution of these accessible and tunable ligands in the context of stabilizing weak bonds and may also be transferable to other amide ligands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".