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Record W4308526982 · doi:10.3390/ma15227877

Selected Properties of Two Alternative Plant Fibers: Canola and Sweet Clover Fibers

2022· article· en· W4308526982 on OpenAlexafffund
Vahid Sadrmanesh, Ying Chen

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceUltimate tensile strengthCrystallinityFourier transform infrared spectroscopyThermogravimetric analysisComposite materialFiberThermal stabilityNatural fiberTensile testingChemical engineering

Abstract

fetched live from OpenAlex

Identifying sustainable resources of natural fibers is essential due to their high demand in industrial applications such as automotive and biomedical materials. Two alternative fibers obtained from canola and sweet clover stalks were characterized for their properties using energy dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), contact angle, and tensile test. Hemp and flax fibers, both in use as industrial fibers, were also characterized as conventional fibers. Results showed that all the fibers had the same chemical elements (carbon, oxygen, magnesium, and potassium) and chemical bonds. The crystallinity index for the alternative fibers ranged from 62 to 71%, which was close but lower than the conventional fibers (82% for hemp and 80% for flax). The thermal stability of the alternative fibers was around 220 °C, close to the conventional fibers (230 °C). The alternative fibers had contact angles of less than 90°, showing high surface energy. Since the alternative fibers had a low Young's modulus and tensile strength (5.57-8.52 GPa and 57.45-71.26 MPa, respectively), they are suitable for some specific applications in the biomedical industry. In contrast, conventional fibers are suitable where a higher stiffness and strength is required.

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 score0.002
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.227
Teacher spread0.211 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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