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Record W4396866580 · doi:10.1177/07316844241253905

Mechanical recycling of poly(lactic acid)/agave fiber biocomposites

2024· article· en· W4396866580 on OpenAlexaff
Tomás Ramos‐Hernández, Jorge Ramón Robledo‐Ortíz, Alan Salvador Martín del Campo, Denis Rodrigue, A. Cano, Aida Alejandra Pérez‐Fonseca

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2024
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversité Laval
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsMaterials scienceAgaveComposite materialFiberLactic acidPolymer science

Abstract

fetched live from OpenAlex

This paper focuses on evaluating the mechanical recycling potential of poly(lactic acid) (PLA) biocomposites reinforced with agave fibers (AF). The biocomposites were prepared by extrusion using 5, 15, and 30 wt.% of agave fibers and reprocessed up to eight times. The results show that the fiber dimensions substantially decrease during reprocessing, especially after the first extrusion cycle, followed by a more gradual decrease in each subsequent cycle. The melt flow index (MFI) and the mechanical properties (except impact strength) tend to decrease as the fiber concentration increases. On the other hand, the glass transition temperature ( T g ) and the crystallinity ( X c ) of the biocomposites increased with increasing fiber concentration. It is important to highlight that closed-loop reprocessing does not significantly affect the overall behavior of the biocomposites under the conditions investigated. Therefore, PLA reinforced with AF is suitable for primary recycling since the final properties are mainly influenced by the fiber concentration and less by the number of reprocessing cycles.

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 score0.221
Threshold uncertainty score0.432

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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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