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Record W4389820399 · doi:10.3390/polym15244714

Recyclability of Post-Consumer Polystyrene at Pilot Scale: Comparison of Mechanical and Solvent-Based Recycling Approaches

2023· article· en· W4389820399 on OpenAlexafffund
Jean‐Mathieu Pin, Iman Soltani, Keny Negrier, Patrick Lee

Bibliographic record

VenuePolymers · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRaw materialPolystyreneSolventMaterials scienceHuman decontaminationWaste managementPulp and paper industryProcess engineeringChemical engineeringOrganic chemistryChemistryPolymerComposite material

Abstract

fetched live from OpenAlex

Solvent-based and mechanical recycling technology approaches were compared with respect to each process's decontamination efficiency. Herein, post-consumer polystyrene (PS) feedstock was recycled by both technologies, yielding recycled PS resins (rPS). The process feedstock was subjected to four recycling cycles in succession to assess the technology perennity. The physico-chemical and mechanical properties of the rPS were then evaluated to discern the advantages and drawbacks of each recycling approach. The molecular weight of the mechanically recycled resin was found to decrease by 30% over the reprocessing cycles. In contrast, the solvent-base recycling technology yielded a similar molecular weight regarding the feedstock. This consistency in the rPS product is critical for consumer applications. Further qualitative and quantitative analyses on residual organic compounds and inorganic and particulate contaminants were investigated. It was found that the solvent-based technology is very efficient for purifying deeply contaminated feedstock in comparison to mechanical recycling, which is limited to well-cleaned and niche feedstocks.

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 score0.087
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.046
GPT teacher head0.254
Teacher spread0.209 · 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

Citations22
Published2023
Admission routes2
Has abstractyes

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