Retention and release of odorants in cotton and polyester fabrics following multiple soil/wash procedures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Odorous clothing can be an annoying and unpleasant problem, particularly when odorants are not effectively removed via laundering. Cotton and polyester knit fabrics were soiled with three selected odorants, representing different polarities and lipophilicities (i.e. octanoic acid, 2-nonenal, dodecane). Fabrics were subjected to 1, 5 and 10 soil/wash cycles using a regular liquid detergent (Tide® Free and Gentle) or a sport liquid detergent (Tide® Plus Febreze Sport). Odorants released into the headspace were collected using solid phase micro-extraction, and odorants retained within the fabric were collected using solvent extraction. Analysis of odorant peaks was carried out using gas chromatography-flame ionization detection. Prior to laundering, higher amounts of all odorants were released into the headspace above polyester fabrics than above cotton fabrics. Cotton fabrics retained more octanoic acid within the fabric and lower amounts of 2-nonenal than polyester. Laundering was more effective at removing odorants from cotton than from polyester, and the polar octanoic acid was more readily removed than the two non-polar odorants from both fabrics. Accumulation of odorants occurred as soil/wash cycles increased from 1 to 5 cycles. However, between 5 and 10 soil/wash cycles the amounts of compounds did not significantly increase, with significantly lower amounts of octanoic acid extracted from cotton at 10 cycles compared to 5 cycles. The results from this study indicate that incomplete removal of odorants during washing, especially from oleophilic polyester fabrics, is a cause for odor build-up in clothing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it