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Record W2050250853 · doi:10.1039/c3ta13560h

Biocomposites from hydrolyzed waste proteinaceous biomass: mechanical, thermal and moisture absorption performances

2013· article· en· W2050250853 on OpenAlexafffund
Tizazu H. Mekonnen, Paolo Mussone, Kirill Alemaskin, Lisa Sopkow, John Wolodko, Phillip Choi, David C. Bressler

Bibliographic record

VenueJournal of Materials Chemistry A · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsAlberta InnovatesUniversity of Alberta
FundersAlberta Livestock and Meat Agency
KeywordsEpoxyMaterials scienceBiocompositeUltimate tensile strengthFlexural strengthAbsorption of waterComposite materialCuring (chemistry)HydrolysisMoistureLigninCelluloseComposite numberChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The main goal of this research is maximizing the utilization of renewable materials in both the matrix and reinforcement, more importantly exploitation of waste material for biocomposite development and evaluating the mechanical, thermal and water resistance performances. Woven roving and chopped strand mat fiber glass, and hemp fiber mats are incorporated to an epoxy resin based matrix cured with novel hydrolyzed specified risk material (SRM) extracts. Aminophenyl sulphone (APS) is used as a control crosslinking agent for the epoxy resin. Results show that the biocomposites developed in this research exhibit promising flexural strength, tensile strength and tensile modulus; despite relatively poor moisture resistance. The use of waste protein hydrolyzate extracts, hydrolyzed proteins, as crosslinking agent of epoxy resins in making biocomposites is novel and promising and results can be extended to other proteinaceous biomasses as curing agent of epoxy resins.

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 categoriesInsufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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

Citations43
Published2013
Admission routes2
Has abstractyes

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