Instrumental Analysis Experiment: Direct Surface Analysis of Personal Protective Equipment
Bibliographic record
Abstract
An advanced instrumental analysis experiment was designed for senior undergraduate and first-year graduate students for the evaluation of surface compositions of personal protective equipment (PPE), with attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) and direct analysis in real time high-resolution mass spectrometry (DART-HRMS). Both techniques are capable of analyzing surfaces in seconds without sampling or sample pretreatments. Students performed direct tests on common PPE products such as facial masks, gloves, and protective clothing fabrics. ATR-FTIR obtained structural characteristics of the polymeric materials, and DART-MS detected additives and residual chemicals including toxic and hazards substances. Different PPE samples showed distinctive spectroscopic profiles in both ATR-FTIR and DART-HRMS. Multivariate data analysis was implemented to demonstrate the differences among the PPE groups. Through this experiment, students can better understand the unique features of different advanced level instruments, improve their capability for in-depth data analysis, and cultivate their ability to critically evaluate the different aspects of chemicals used in real life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".