Determination of the Zinc Concentration in Human Fingernails Using Laser-Induced Breakdown Spectroscopy
Bibliographic record
Abstract
The absolute concentration of Zn in human fingernail clippings was determined ex vivo using 1064 nm laser-induced breakdown spectroscopy and confirmed by speciated isotope dilution mass spectrometry. A nail testing protocol that sampled across the nail (perpendicular to the direction of growth) was developed and validated by scanning electron microscopy energy-dispersive X-ray spectrometry. Using this protocol, a partial least squares (PLS) regression model predicted the Zn concentration in the fingernails of five people to within an average of 7 ppm. The variation in the Zn concentration with depth into the nail determined by laser-induced breakdown spectroscopy was studied and showed no systematic variation for up to 15 subsequent laser pulses in one location. The effects of nail hydration (dehydrated and over-hydrated) and nail surface roughness were investigated to explain an anomalously large scatter observed in the measurements. This scatter was attributed to the layered nature and fibrous structure of the fingernails, which resulted in non-uniform ablation as determined by scanning electron microscopy. This work demonstrates that a protocol consisting of low pulse energy (<10 mJ) 1064 nm laser pulses incident on human fingernail clippings in an Ar environment can produce quantifiable Zn emission in the laser-induced plasma and that the measured Zn intensity can be used to accurately predict the Zn concentration in human fingernails.
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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".