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Record W7042560450

Plasticizer Identification and Characterization Across Multiple Poly(Vinyl) Chloride (PVC) Blends

2024· dissertation· en· W7042560450 on OpenAlexafffund

Bibliographic record

VenueMacSphere (McMaster University) · 2024
Typedissertation
Languageen
FieldMaterials Science
TopicPolymer Science and PVC
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsPlasticizerFusible alloyPolyvinyl chlorideHomogenizerLiquation
DOInot available

Abstract

fetched live from OpenAlex

Poly(vinyl) Chloride (PVC) is extensively utilized as a thermoplastic due to its exceptional properties and diverse applications. However, PVC presents various recycling challenges compared to other polymer plastics. The characteristic halogen group (chlorine) is released as HCl during vigorous recycling processes, which deters researchers and recyclers. Formulations of PVC consist of multiple additives, such as plasticizers, which provide the material with flexibility but complicate recycling. Industrial plasticizers are typically phthalate-based, which are toxic and migrate from the material over time. In our research, we developed a methodology for the removal, identification, and quantification of a known phthalate di-isononyl phthalate (DINP)) and PVC blend. The extraction process, comprised of a solvent/anti-solvent system (THF: MeOH), coupled with multiple analytical methods, effectively removes multiple plasticizers from PVC blends. The process produces two products: the plasticizers in solution and the recycled PVC (rPVC) resin. Our findings indicated that the plasticizers were successfully removed and were below the limit of detection (LOD) with Fourier Transform Infrared - Attenuated Total Reflectance (FTIR-ATR) and Nuclear Magnetic Resonance (NMR) analysis. Through NMR analysis, we identified multiple plasticizers utilized in the sample rPVC blends, highlighting the importance of understanding the composition of our blend. All rPVC blends contained the toxic phthalate plasticizer, di(2-ethylhexyl) phthalate (DEHP), with one blend relying on it as the sole plasticizer. DEHP is known for its strong plasticizing properties, which explains the flexibility of this blend. Another blend used in extrusion applications contained di-(2-ethylhexyl) terephthalate (DEHT) (primary plasticizer), tri-octyl trimellitate (TOTM), and DEHP. A custom blend produced by the recycler, made from 50% industrial and 50% post-consumer reprocessed PVC, contained a dual-plasticizer system of DEHT and DEHP. These results emphasize the need to identify and quantify phthalates in applications in PVC to control quality and mitigate the environmental and health impacts of toxic plasticizers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.225
Teacher spread0.214 · 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 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
Published2024
Admission routes2
Has abstractyes

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