Sulfolane Analysis in Environmental Samples: A Critical Review
Bibliographic record
Abstract
Sulfolane, a highly water-soluble industrial solvent, has raised environmental concerns due to its widespread usage and its prolonged persistence once released into the environment. To assess the extent of contamination effectively, reliable analytical methods are essential. In this review article, the existing methodologies for the sample preparation and determination of sulfolane are systematically examined and evaluated. In brief, existing guidelines for sampling from environmental matrices provide a solid foundation for sulfolane analysis. Notably, there is little variation in the choice of final determination methods, with GC-MS or GC-FID being favored across all studies. However, substantial variability emerges in sample preparation methods. Moreover, upon scrutiny of the analytical procedures utilized in sulfolane analysis, it becomes evident that a considerable number of them are characterized by inefficiency or environmental hazards, often relying on substantial quantities of chlorinated solvents. Through this review, valuable insights into the challenges at hand and potential solutions can be gained, offering a foundation for the development of novel sulfolane analysis methods applicable to a range of environmental matrices. Overall, this field calls for further research to devise efficient and environmentally sustainable analytical methods for sulfolane analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".