Sulfur 1<i>s</i> near edge x-ray absorption fine structure spectroscopy of thiophenic and aromatic thioether compounds
Bibliographic record
Abstract
Thiophenic compounds are major constituents of fossil fuels and pose problems for fuel refinement. The quantification and speciation of these compounds is of great interest in different areas such as biology, fossil fuels studies, geology, and archaeology. Sulfur 1s Near-Edge X-ray Absorption Fine Structure (NEXAFS) spectroscopy has emerged as a qualitative and quantitative method for sulfur speciation. A firm understanding of the sulfur 1s NEXAFS spectra of organosulfur species is required for these analytical studies. To support this development, the sulfur 1s NEXAFS spectra of simple thiols and thioethers were previously examined, and are now extended to studies of thiophenic and aromatic thioether compounds, in the gas and condensed phases. High-resolution spectra have been further analyzed with the aid of Improved Virtual Orbital (IVO) and Δ(self-consistent field) ab initio calculations. Experimental sulfur 1s NEXAFS spectra show fine features predicted by calculation, and the combination of experiment and calculation has been used to improve the assignment of spectroscopic features important for the speciation and quantification of sulfur compounds. Systematic differences between gas and condensed phases are also explored; these differences suggest a significant role for conformational effects in the NEXAFS spectra of condensed species.
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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 | 0.001 | 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".