Direct measurement of part‐per‐billion levels of dimethyl sulfoxide in water by gas chromatography with stacked injection and chemiluminescence detection
Bibliographic record
Abstract
Dimethyl sulfoxide (DMSO) is a chemical of industrial significance with many important applications. DMSO is used as an industrial solvent, in drug delivery and healthcare applications, among others. Analysis of DMSO in water typically involves extensive sample preparation, enrichment, and derivatization to improve solute detectability. A novel gas chromatographic procedure has been developed for the direct measurement of trace levels of DMSO in an aqueous matrix, such as potable water. The technology utilizes stacked injection techniques for in-column solute enrichment, a precolumn to enhance solute focusing effects, and sulfur chemiluminescence detection for matrix suppression and sensitivity. A detection limit of 2 parts per billion (ppb) (v/v) of DMSO in water was attained. Relative precision of less than 7% at the concentration of 10 ppb (v/v) of DMSO was demonstrated. A correlation coefficient of 0.9988 was obtained over a range of 2 ppb (v/v) to 100 ppb (v/v). No detectable carry-over was found at the 5 ppb (v/v) level whereas less than 4% carry-over was observed at the 100 ppb (v/v) level. Various sample storage media including glass, polyethylene, and polycarbonate were also studied to minimize solute loss. Recoveries greater than 84% were achieved with all storage media tested. The method was found to be reliable and simple to implement.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".