Enhanced Dark-Field\nHyperspectral Imaging and Spectral\nAngle Mapping for Nanomaterial Detection in Consumer Care Products\nand in Skin Following Dermal Exposure
Bibliographic record
Abstract
Consumer\npersonal care products, and cosmetics containing nanomaterials\n(NM), are increasingly available in the Canadian market. Current Canadian\nregulations do not require product labeling for ingredients that are\npresent in the nanoscale. As a result, unless voluntarily disclosed,\nit is unclear which products contain NM. The enhanced dark-field hyperspectral\nimaging (EDF-HSI) coupled with spectral angle mapping (SAM) is a recent\ntechnique that has shown much promise for detection of NM in complex\nmatrices. In the present study, EDF-HSI was used to screen cosmetic\ninventories for the presence of nano silver (nAg), nano gold (nAu),\nand nano titanium dioxide (nTiO<sub>2</sub>). In addition, we also\nassessed the potential of EDF-HSI as a tool to detect NM in skin layers\nfollowing application of NM products <i>in vitro</i> on\ncommercially available artificial skin constructs (ASCs) and <i>in vivo</i> on albino hairless SKH-1 mouse skin. Spectroscopic\nanalysis positively detected nAu (4/9 products) and nTiO<sub>2</sub> (7/13 products), but no nAg (0/6 products) in a subset of the cosmetics.\nThe exposure of ASCs for 24 h in a Franz diffusion cell system to\na diluted cosmetic containing nTiO<sub>2</sub> revealed penetrance\nof nTiO<sub>2</sub> through the epidermal layers and was detectable\nin the receptor fluid. Moreover, both single and multiple applications\nof nTiO<sub>2</sub> containing cosmetics on the dorsal surface of\nSKH-1 mice resulted in detectable levels of trace nTiO<sub>2</sub> in the layers of the skin indicating that penetrance of NM was occurring\nafter each application of the product. The current study demonstrates\nthe sensitivity of EDF-HSI with SAM mapping for qualitative detection\nof NM present in cosmetic products <i>per se</i> and very\nlow levels in complex biological matrices on which these products\nare applied.
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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 | 0.001 |
| 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 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".