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Record W4412819436 · doi:10.3390/polym17152103

Assessment of the Environmental Feasibility of Utilizing Hemp Fibers in Composite Production

2025· article· en· W4412819436 on OpenAlexafffund
Denis da Silva Miranda, Douglas Alexandre Casetta, Leonardo C. Simon, Luiz Kulay

Bibliographic record

VenuePolymers · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsPelletsGlobal-warming potentialGreenhouse gasComposite numberBiomass (ecology)Materials scienceEnvironmental scienceGreenhousePulp and paper industryComposite materialWaste managementEngineeringAgronomy

Abstract

fetched live from OpenAlex

This study investigated the impact of incorporating hemp fibers into composites for manufacturing industrial parts. The Global Warming Potential (GWP) of producing a traditional polymer matrix composite containing glass fibers was compared to that of producing a counterpart from natural hemp fibers. The investigation concluded that the partial replacement of synthetic fibers with biomass reduced the GWP of the product by up to 25% without compromising its mechanical properties. This study also quantified and discussed the GWP of intermediate products obtained from alternative routes, such as the manufacture of hemp stalks and pellets. In these cases, the findings showed that the amount of CO2 absorbed during plant growth exceeded the emissions related to soil preparation, farming, and processing of hemp stalks by up to 15 times, and the processing of row hemp bales into pellets could result in an even “greener” product. This study highlights the importance of using bio-based inputs in reducing greenhouse gas emissions in the materials manufacturing industry and concludes that even partial substitutions of synthetic inputs with natural fibers can show significant reductions in this type of environmental impact.

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 score0.118
Threshold uncertainty score0.246

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 head0.014
GPT teacher head0.284
Teacher spread0.270 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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