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Record W4200565313 · doi:10.1039/d1em00392e

Characterizing photochemical ageing processes of microplastic materials using multivariate analysis of infrared spectra

2021· article· en· W4200565313 on OpenAlexafffund
Misha Zvekic, Larissa C. Richards, Christine C. Tong, Erik T. Krogh

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsVancouver Island UniversityUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaVancouver Island University
KeywordsLow-density polyethyleneMicroplasticsHigh-density polyethyleneWeatheringEnvironmental chemistryPolyethyleneAttenuated total reflectionPolystyrenePolypropyleneSorptionChemistryEnvironmental scienceInfrared spectroscopyPolymerGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

>500 spectra) simultaneously discriminated plastic type and extent of photochemical weathering. The magnitude of PCA scores correlated with exposure time and the loadings were consistent with surface chemistry changes including photooxidation. Projecting sunlight and UV-C exposed samples onto this PCA model demonstrated that similar chemical changes occurred, albeit at different rates. The results were compared to the carbonyl index (CI) with similar weathering trends indicating PP weathered at a faster initial rate than LDPE and HDPE. We propose that a multivariate approach is more widely applicable than CI as illustrated by PS, which lacked a stable reference peak. Kinetic analysis of the time series indicated that outdoor weathering occurred 5-12 times slower than the artificial exposure used here, depending on the plastic and the light source employed. The results provide unique insights into weathering processes and the photochemical age of naturally weathered plastics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations57
Published2021
Admission routes2
Has abstractyes

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