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Polyurethane Foams Reinforced with Biobased Materials: Properties and Applications

2018· article· en· W2899213852 on OpenAlexaff
G. Sharifishourabi, Nguyen Tien-Binh, Denis Rodrigue

Bibliographic record

VenueCurrent Applied Polymer Science · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPolyurethaneMaterials scienceBlowing agentPolyolComposite materialFiller (materials)Composite numberAutomotive industryPolymerPolymer scienceEngineering

Abstract

fetched live from OpenAlex

Background: Today, polyurethane foams can be found in various commercial products such as bedding, home furniture, automotive interiors and even construction materials. From a chemical point of view, polyurethane foams are made from a chemical reaction between a polyol (molecules with more than one hydroxyl group) and a diisocyanate in the presence of a blowing agent. Objective: Because of their highly stable bonds, polyurethane foams are considered as nondegradable leading to some environmental impact. To address this concern different bio-based fillers have been used to create "greener" polyurethane materials. This review presents an overview of different bio-based fillers and containing natural polyols for polyurethane foams formulation with respect to their natural properties, sizes, geometries and contents. Method: A wide range of bio-based fillers derived from wood and non-wood sources are summarized based on their physico-mechanical properties. Then, possible applications are presented and future trends are discussed for the research and development of these complex (multiphase systems) materials (polymer composite foams). Conclusion: Beside traditional polyurethane foams applications including automotive, building, home furniture and package, bio-based filler addition could bring new feature and widen their applications such as shape memory and medication, as well as oil absorbent.

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 score0.016
Threshold uncertainty score0.885

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.253
Teacher spread0.226 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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