Proceedings of the 10th annual TRU Undergraduate and Innovation Conference
Bibliographic record
Abstract
Antibacterial agents are extremely common in everyday personal care products, such as toothpastes, facial cleansers, hand soaps, body washes, cosmetics, and numerous other products.Due to their widespread use, the active antibacterial agents in these products have been detected in public water samples, such as from pools and rivers.One antibacterial agent under scrutiny at this time is triclosan.Although its effects on human health are controversial and largely unknown, it has been reported to have an effect on the endocrine system.It should also be noted that industries are now avoiding the use of triclosan since very minute quantities can pose a severe risk to marine life in aquatic ecosystems.The purpose of this research was to develop a sensitive, rapid liquid chromatography-mass spectrometry (LC/MS) method to detect triclosan in personal care products and public water samples.The experimental conditions, such as column temperature, solvents, flow rate, analyte extraction methods, and experimental procedure, were all optimized to find the best experimental conditions for detecting triclosan in the samples.The ability to detect triclosan in personal care products, as well as in pool and river water samples, will hopefully encourage consumers to reduce or avoid the use of triclosan containing products.Using the optimized method developed, the average concentration of triclosan in the personal care product samples ranged from 10 ppm to 4741 ppm.The average concentrations in the pool water and river water samples were 49 ppb and 72 ppb, respectively. AcknowledgementsI would like to thank my supervisor, Dr. Kingsley Donkor, for giving me the opportunity to conduct research under his guidance, as well as for proposing research that is in an
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.008 | 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 teacher head, 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".