MétaCan
Menu
Back to cohort
Record W4393254149 · doi:10.3390/polym16070926

Characterization of Low- and High-Velocity Responses of Basalt–Epoxy and Basalt–Elium Composites

2024· article· en· W4393254149 on OpenAlexafffund
Jesse R. J. G. Llanos, Ke Wang, Farid Taheri‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Bibliographic record

VenuePolymers · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaArkema
KeywordsEpoxyMaterials scienceComposite materialBasalt fiberComposite numberBasaltFinite element methodComposite epoxy materialAramidEnvironmentally friendlyCharacterization (materials science)FiberStructural engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Currently, fiber-reinforced polymer composites (FRPs) used for demanding structural applications predominantly utilize carbon, glass, and aramid fibers embedded in epoxy resin, albeit occasionally polyester and vinyl ester resins are also used. This study investigates the feasibility of employing recyclable and sustainable materials to formulate a composite suitable for load-bearing structural applications, particularly in scenarios involving low-velocity and high-velocity impacts (LVIs and HVIs, respectively). The paper presents a comparative analysis of the performance of basalt-Elium, a fully recyclable, sustainable, and environmentally friendly composite, with an epoxy-based counterpart. Moreover, an accurate and reliable numerical model has been developed and introduced through which the response of these composites can be examined efficiently and accurately under various loading states. The results of this investigation demonstrate the viability of the basalt-elium composite as a fully recyclable and sustainable material for crafting efficient and lightweight composites. Additionally, the accurately developed finite element model presented here can be used to assess the influence of several parameters on the composite, thereby optimizing it for a given situation.

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 score0.019
Threshold uncertainty score0.472

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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

Citations13
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuePolymersSame topicNatural Fiber Reinforced CompositesFrench-language works237,207