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Record W4407548125 · doi:10.1021/acs.jchemed.4c01429

Instrumental Analysis Experiment: Direct Surface Analysis of Personal Protective Equipment

2025· article· en· W4407548125 on OpenAlexaff
Liping Xu, Zhiru Bai, Hongli Li, David D. Y. Chen

Bibliographic record

VenueJournal of Chemical Education · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacillus and Francisella bacterial research
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of ChinaNanjing Normal University
KeywordsSurface (topology)ChemistryNanotechnologyEngineering physicsMaterials scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.310
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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