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Record W2223550206 · doi:10.2495/dne-v9-n3-237-244

Product development using vegetable fibers

2014· article· en· W2223550206 on OpenAlexvenueno aff
Claudio Costa, Andrea Ratti, Barbara Del Curto

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsEngineeringNew product developmentProduct designBiochemical engineeringProduct (mathematics)BusinessMathematics

Abstract

fetched live from OpenAlex

This article is a comprehensive review on the mechanical and tribological behavior of four plant fi bers (hemp, kenaf, coconut and broom) and the products design made based on these plant fi bers. The treatments and the chemical and physical characteristics of these types of plant fi bers are investigated to understand the applications in design fi eld. The application of plant fi bers are subject to many scientifi c and research projects, as well as many commercial projects. Data around these fi bers are being collected and analyzed to arrange them and add new value for future applications. In most studies, natural fi bers are used as replacement of traditional fi bers in fi ber-reinforced composites, or in automotive sector, geo textiles and other engineering fi elds. The research carried on is been organized so that mechanical and chemical-physical characteristics of these plant fi bers can be used in conjunction with previous studies, to give a new scenario for design applications. In general, natural fi bers have the advantages of biodegradability, low density, abundance and renewability, non-toxic nature, useful mechanical properties, and low cost. However, the main disadvantages of natural fi bers are (i) the poor compatibility between fi ber and matrix in composites and (ii) the relative high moisture sorption.

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)0.001
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 score0.090
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and EcodynamicsSame topicNatural Fiber Reinforced CompositesFrench-language works237,207