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Record W4413128505 · doi:10.1002/pen.70081

Flexible Polyurethane Foams Reinforced With Recycled Waste Tire Material

2025· article· en· W4413128505 on OpenAlexfundno aff
Y. Nezili, I. El Aboudi, Delong He, A. Mdarhri, Christian Brosseau, Mustapha Zaghrioui, Karine Mougin, Cyril Vaulot, Jinbo Bai

Bibliographic record

VenuePolymer Engineering and Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsnot available
FundersUniversité de ParisAgence Universitaire de la Francophonie
KeywordsMaterials sciencePolyurethaneComposite materialWaste managementEngineering

Abstract

fetched live from OpenAlex

ABSTRACT We explore the use of ground tire rubber (GTR) powder obtained through water jet pulverization (WJP), referred to as WJP‐GTR, and reveal that its structural parameters play a significant role in controlling the reinforcing properties of flexible polyurethane (PU) foams. Specifically, to improve interfacial adhesion with the PU matrix, we consider chemically treated WJP‐GTR particles either with NaOH (WJP‐GTR NaOH ), KMnO 4 (), or H 2 O 2 (). A series of PU/WJP‐GTR composite foams with varying filler contents (5–20 wt.%) are fabricated via the free‐rising foam method. Here, we explore the influence of the content and chemical treatment of the WJP‐GTR on the mechanical, thermal, and acoustic properties of PU/WJP‐GTR composite foams. Compression tests indicate that significantly enhances the overall stiffness and ultimate compressive strength of the sample containing 15 wt.% WJP‐GTR. Analysis of the acoustic characterization reveals that WJP‐GTR NaOH foams exhibit the highest sound absorption, with a peak acoustic activity of 0.46 at 15 wt.% filler content and enhanced absorption in the low‐frequency range. These findings demonstrate the potential of chemically treated WJP‐GTR as a sustainable reinforcement for flexible PU foams, offering tunable mechanical and acoustic properties suitable for automotive, construction, and aerospace applications.

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 score0.053
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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
Published2025
Admission routes1
Has abstractyes

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