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Record W4385986294 · doi:10.29169/1927-5129.2023.19.12

Influence of Microstructure on the Dynamic Behaviour of Polyurethane Foam with Various Densities

2023· article· en· W4385986294 on OpenAlexvenueno aff
Noureddine Boumdouha, Mohamed Abderaouf Louar

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsnot available
FundersDirection Générale de la Recherche Scientifique et du Développement Technologique
KeywordsPolyurethaneMaterials scienceComposite materialMicrostructureScanning electron microscopeDeformation (meteorology)Compression (physics)

Abstract

fetched live from OpenAlex

Polyurethane foam is reinforced with varying proportions of metal loads and other components to increase shock absorption and mechanical impact. The main objective is to develop high-performance polymeric materials based on polyurethane foam developed with different compositions and specific densities. We monitor the growth distances and temperatures of the polyurethane foam in time to reach the optimum formulations. We conduct static compression tests and investigate the effect of drop weight on the deformation of polyurethane foam structures by dropping a weight from a specified height. Dynamic collisions cause deformations of the polyurethane foam structure. After investigating the low weight, we found that polyurethane foams have a good absorption coefficient at certain frequencies. Dynamic stress-strain response curves are used to characterize different stress rates. High-stress levels and similar strains indicate a high resistance to shock. We follow the evolution of microstructure structures by scanning electron microscopy (SEM) to observe deformation and fracture behavior with reversibility and recovery.

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)0.001
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 score0.047
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.237
Teacher spread0.227 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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