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Characterization of Wastes and Coproducts from the Coffee Industry for Composite Material Production

2016· article· en· W2501303822 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioResources · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsThermogravimetric analysisPolypropyleneMaterials scienceThermal stabilityComposite materialFiller (materials)Scanning electron microscopeBiomass (ecology)Coffee groundsComposite numberPolymerChemical engineeringChemistryAgronomyFood science

Abstract

fetched live from OpenAlex

This study characterizes and compares coffee chaff (CC) and spent coffee grounds (SCG), the two most useful coffee waste products, and evaluates their performance as fillers and/or reinforcing agents in polymer composites. The morphologies of the CC and the SCG were studied using a scanning electron microscope (SEM). Detailed compositional and elemental analyses of the samples were carried out using several techniques. The thermal stabilities of the two types of biomass were evaluated using thermogravimetric analysis (TGA). Infrared spectroscopy was performed to investigate the functional groups available on the surface of the biomass. It was found that the CC had higher thermal stability, lower fat content, and a denser fibrous structure than the SCG, making it potentially a more suitable material than the SCG for use as a reinforcing filler in polymer composites. To verify this potential, CC and SCG filled polypropylene composites were produced and evaluated.

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.

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 score0.021
Threshold uncertainty score0.219

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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