The Variable Strength of the Sulfur–Sulfur Bond: 78 to 41 kcal – G3, CBS‐Q, and DFT Bond Energies of Sulfur (S<sub>8</sub>) and Disulfanes XSSX (X = H, F, Cl, CH<sub>3</sub>, CN, NH<sub>2</sub>, OH, SH)
Bibliographic record
Abstract
Abstract Experimental values for the strength of the sulfur–sulfur bond scatter over a wide range and are frequently in disagreement with each other. For S8, reliable experimental data seem to lack entirely. To check experimental data and establish the strength of the sulfur–sulfur bond in S8, high‐precision thermochemical calculations (G3, CBS‐Q) and DFT methods were employed. The calculations confirm a stunning range for the strength of the sulfur–sulfur bond with energies between 77.7 kcal for FSSF and only 41.8 kcal for tetrasulfane, HSS–SSH. For the pivotal bond energy of elemental sulfur, S8, the bond energy is 40.5 kcal but can be narrowed to 38.0–39.2 kcal mol–1 by including CBS‐QB3 data. While older DFT methods are not well suited to accurately reproduce the S–S bond energies, excellent data can be obtained with the recently introduced Boese–Martin (BMK) hybrid DFT method. Atoms in molecules (AIM) calculations reveal significant multiple bonding and spin delocalization in the sulfur radicals XS·. (© Wiley‐VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2009)
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".