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Thermoplastic Vulcanizates/Recycled Polypropylene Blend for Automotive OEM

2017· article· en· W2767041913 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKey engineering materials · 2017
Typearticle
Languageen
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials sciencePolypropylenePlastics extrusionComposite materialThermoplasticInjection mouldingScrapUltimate tensile strengthFlexural strengthAutomotive industryPolymer blendRaw materialMelt flow indexPolymerCopolymerMetallurgy

Abstract

fetched live from OpenAlex

An original equipment market (OEM) in Thailand mainly imports thermoplastic vulcanizates (TPV) from abroad that leads to a high manufacturing cost. To reduce the cost and to create value-added products from a plastic scrap, therefore, this research aim is to observe a possibility of using TPV and recycled polypropylene (rPP) blends as a raw material for OEM. The blends with various rPP loadings were successfully prepared through a traditional twin-screw extruder. Proportions between TPV and rPP were adjusted to determine the optimal flow and mechanical properties for productions of different auto parts. The blends were tested for studying rheology and mechanical properties: tensile; hardness; flexural; and creep behavior. All tests resulted in discussions about the feasibility of using TPV/rPP blends with respect to auto part specifications in real applications. Test results suggested that the TPV/rPP blends meet the requirements of specific automotive applications. Thermal property and morphological analysis were also carried out to have more understanding about changes in mechanical properties.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.093
Threshold uncertainty score1.000

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)0.0000.000

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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