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Dehydrating of Flax Fiber with Microwave Heating for Biocomposite Production

2005· article· en· W2403725454 on OpenAlexafffund
Satyanarayan Panigrahi, A. Ghazanfari, Venkatesh Meda

Bibliographic record

VenueJournal of Microwave Power and Electromagnetic Energy · 2005
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsBiocompositeMaterials scienceFiberMicrowave heatingComposite materialMicrowavePulp and paper industryComposite numberComputer scienceEngineering

Abstract

fetched live from OpenAlex

The feasibility of microwave dehydrating flax fiber was evaluated using a commercial domestic microwave oven at four power settings representing 200, 300, 400 and 500 Watt (W) power level. Due to the possibility of local heating and consequent fiber degradation, the changes in color of the flax fiber at different levels of temperature were also investigated. The dehydration processes at various power levels were simulated by Page model. Based on visual inspection, color analysis and scanning electron microscopy (SEM) of the fiber, it was revealed that discoloration of the fiber occurred at about 170 degrees C. At 200 and 300 W power level, after 10 minutes of dehydrating, the moisture content of the fiber reached from initial 7.9% close to 2.0 and 1.0%, respectively. For 400 W power level, the moisture content of the fiber dropped to 0. 10% in about 9.5 minutes. Major discoloration of the fiber was noticed when dehydration was proceed beyond 4.5 minutes for 500 W treatment. The Page model very well fitted the experimental data. The coefficients of determination calculated from the model and the experimental data increased with increase in applied microwave power

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 score0.107
Threshold uncertainty score0.573

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 head0.004
GPT teacher head0.209
Teacher spread0.204 · 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

Citations11
Published2005
Admission routes2
Has abstractyes

Explore more

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