Textile sorption and release of odorous volatile organic compounds from a synthetic sweat solution
Bibliographic record
Abstract
Body odorants typically transfer to clothing fabrics by way of liquid sweat, yet investigations of odor retention in textiles often neglect this route of exposure in their test procedures. This paper describes a novel method for transferring selected odorous volatile organic compounds to six types of textile fibers in yarn bundle form by an aqueous sweat solution. Headspace volatile organic compounds varying by chemical class (ketones, aldehydes, carboxylic acids) were monitored at discrete time intervals (30 min, 3 h, 24 h) using proton transfer reaction mass spectrometry. Lower intensities of ketones and aldehydes were detected in the headspace above cellulosic fibers (cotton, mercerized cotton, viscose) than above wool, nylon, and polyester fibers at 30 min. A rapid decrease in ketones occurred for all fibers, but lower intensities of ketones were released after 3 h for cellulosic and wool fibers. Nylon fibers typically released the highest amounts of ketones and aldehydes at 30 min, but by 24 h higher intensities of these compounds were released from polyester. Carboxylic acids exhibited minimal differences in intensities between 30 min and 3 h, with few differences evident among fiber types. Understanding the preferential sorption of odorants when clothing is exposed to volatile organic compounds in aqueous solutions such as sweat is enhanced from the results of this investigation.
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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 | 0.000 | 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) | 0.001 | 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".