Surfactant Concentration and Type Affects the Removal of Escherichia coli from Pig Skin During a Simulated Handwash
Bibliographic record
Abstract
The effect of surfactant type and concentration on a bland soap formulations ability to remove bacteria from hands remains largely unstudied. Several combinations of surfactants and water were combined to test bacterial removal efficacy using a handwashing device (two pieces of pig skin and a mechanical motor) to simulate a handwash. A nalidixic acid resistant, non-pathogenic strain of Escherichia coli (ATCC 11229) was used. Two anionic surfactants, sodium lauryl sulfate and sodium stearoyl lactylate, and two nonionic surfactants, poloxamer 407 and sorbitan monostearate, each in concentrations of 2%, 5%, and 10% were studied. A slight positive (r2=0.17) but significant (p=0.03) correlation was observed between hydrophile-lipophile balance value and mean log reduction. No correlation was observed between pH of the treatment solution and the mean log reduction (r2=0.05, p=0.25). A 10% sodium lauryl sulfate mixture showed the highest log reduction (x̄= 1.1 log cfu reduction, SD=0.54), and was the only treatment significantly different from washing with water (p=0.0005). There was a correlation between increasing surfactant concentrations above the critical micelle concentration, and mean microbial reduction (r2=0.62, p=0.001).
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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 | 0.001 |
| 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 | 0.000 |
| 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".