A theoretical study of the <sup>77</sup>Se NMR and vibrational spectroscopic properties of Se<i><sub>n</sub></i>S<sub>8</sub><i><sub>n</sub></i> ring molecules
Bibliographic record
Abstract
The structures and spectroscopic properties of SenS8n ring molecules have been studied by the use of ab initio molecular orbital techniques and density functional techniques involving Stuttgart relativistic large core effective core potential approximation with double zeta basis sets for valence orbitals augmented by two polarization functions for both sulfur and selenium. Full geometry optimizations have been carried out for all 30 isomers at the Hartree-Fock level of theory. The optimized geometries and the calculated fundamental vibrations and Raman intensities of the SenS8n molecules agree closely with experimental information where available. The nuclear magnetic shielding tensor calculations have been carried out by the Gauge-independent atomic orbital method at the DFT level using Becke's three-parameter hybrid functional with Perdew/Wang 91 correlation. The isotropic shielding tensors correlate well with the observed chemical shift data. The calculated chemical shifts provide a definite assignment of the observed 77Se NMR spectroscopic data and can be used in the prediction of the chemical shifts of unknown SenS8n ring molecules.Key words: selenium sulfides, ab initio, DFT, effective core potentials, geometry optimization, energetics, fundamental vibrations, 77Se chemical shifts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".