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Record W2163171090 · doi:10.1002/mame.201300473

Hybrid Bio‐Based Composites from UPE/EML Blends, Natural Fibers, and Nanoclay

2014· article· en· W2163171090 on OpenAlexaff
Mahmoodul Haq, Rigoberto Burgueño, Amar K. Mohanty, Manjusri Misra

Bibliographic record

VenueMacromolecular Materials and Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMaterials scienceComposite materialToughnessUltimate tensile strengthNatural fiberFiberFracture toughnessMoistureStiffness

Abstract

fetched live from OpenAlex

Hybrid bio‐based composites from blends of petroleum and vegetable oil resins reinforced with natural fibers and nanoclay platelets can lead to synergistic property enhancements from the multi‐scale constituents. Twelve material designs with average hemp fiber content of 30 wt%, varying nanoclay, up to 5 wt%, and epoxidized methyl linseedate (EML), up to 30%, in unsaturated polyester (UPE) were processed and their tensile, impact, and moisture properties were experimentally characterized. The resulting biocomposites displayed synergistic behavior with the effects of bio‐resin addition being complemented by nanoclay and vice versa. Optimal performance with balanced properties (stiffness, toughness, and resistance to moisture absorption) was observed in material systems with 2.5 wt% nanoclay and up to 20% EML. Overall, hybrid biocomposites show promise in applications that require excellent stiffness‐toughness balance.

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 categoriesMeta-epidemiology (narrow)
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 score0.011
Threshold uncertainty score1.000

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 head0.003
GPT teacher head0.176
Teacher spread0.173 · 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.

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

Citations9
Published2014
Admission routes1
Has abstractyes

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