Solubility of Total Reduced Sulfurs (Hydrogen Sulfide, Methyl Mercaptan, Dimethyl Sulfide, and Dimethyl Disulfide) in Liquids
Bibliographic record
Abstract
The solubility of the total reduced sulfurs, which include hydrogen sulfide (H 2 S), methyl mercaptan (methanethiol, CH 3 SH), dimethyl sulfide (CH 3 SCH 3 ), and dimethyl disulfide (CH 3 S 2 CH 3 ), is of great interest in various applications in the chemical, oil, and gas industries and in environmental protection as well. They can occur naturally in the environment and can also be present in numerous industrial gaseous streams (petroleum, natural gas, some chemical industries like the pulp and paper industry). The aim of this review is to update different aspects concerning the solubility data of these compounds in various liquids, which are essential for the design and operation of absorption scrubbing equipment and/or of interest in many technical areas (e.g., the petroleum and natural gas industry). The review deals with the compound's characterization in direct connection with their source and a survey of relevant existing experimental data including (i) all data concerning the solubility of methyl mercaptan, dimethyl sulfide, and dimethyl disulfide published generally before January 2006 as well as (ii) data concerning the solubility of hydrogen sulfide published generally after January 1987. The liquids include water, aqueous electrolyte solutions, nonaqueous solvents, and alkanolamines. Both pure compounds and mixtures are considered.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".