A Review of Analytical Methods for the Determination of Sulfolane and Alkanolamines in Environmental Studies
Bibliographic record
Abstract
Sulfolane and alkanolamines are used extensively in the processing of sour natural gases. Over many years of operation, there have been inadvertent leaks of these chemicals to groundwater and wetlands surrounding gas processing facilities, leading to uptake by vegetation. Because sulfolane and alkanolamines are extremely water-soluble, their analysis has presented challenges, particularly requirements for suitable extraction from biological matrixes and soil, along with sensitive detection using commonly available instrumentation. Analytical methods usually use gas chromatography or liquid chromatography with a variety of detector systems. Sample preparation techniques may include extraction with organic solvents, water, or a combination of these. In some cases, direct aqueous injections have been used. Derivatization of alkanolamines has been used to improve the chromatographic separations and detection. More recent procedures, using positive-ion electrospray ionization mass spectrometry (MS), have been useful for the confirmation of uptake of the alkanolamines and transformation products by wetland vegetation. Future developments will likely center on further MS analyses for identification of metabolites and transformation products in aquatic environments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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".