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Record W2890755234 · doi:10.1177/147776061302900402

Effect of SBR Rubber Particle Size Distribution on Its Thermo-Mechano-Chemical Regeneration

2013· article· en· W2890755234 on OpenAlexafffund
Adrian Macsiniuc, Annie Rochette, Denis Rodrigue

Bibliographic record

VenueProgress in Rubber Plastics and Recycling Technology · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsCegep de ThetfordUniversité Laval
FundersCentre québécois sur les matériaux fonctionnels
KeywordsNatural rubberStyrene-butadieneParticle sizeRegeneration (biology)Materials scienceParticle-size distributionComposite materialParticle (ecology)Chemical engineeringStyrenePolymerCopolymerEngineering

Abstract

fetched live from OpenAlex

Recycled Styrene-Butadiene Rubber (SBR) from ground tire rubber (GTR) was sieved into four granulometric fractions between 125 and 500 microns to determine the effect of particle size on its thermo-mechano-chemical regeneration in an internal batch mixer. Different temperatures (140-180°C) and rotor speeds (40-120 rpm) were imposed with and without the addition of a regeneration aid (aromatic oil). The results show that partial regeneration (lower crosslinking degree) is possible under specific thermo-mechanical conditions, but the addition of the aromatic oil has limited efficiency towards this regeneration.

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 score0.039
Threshold uncertainty score0.429

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

Explore more

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