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Record W2299674193 · doi:10.1002/pc.23960

Effects of glass fibers on mechanical and thermal properties of poly(3‐hydroxybutyrate‐<i>co</i>‐3‐hydroxyhexanoate)

2016· article· en· W2299674193 on OpenAlexaff
Willson Arifin, Takashi Kuboki

Bibliographic record

VenuePolymer Composites · 2016
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceComposite materialDifferential scanning calorimetryCrystallinityGlass fiberUltimate tensile strengthFiberCompoundingModulusMolding (decorative)Thermodynamics

Abstract

fetched live from OpenAlex

This study investigates the effects of glass fiber on the mechanical and thermal properties of bacterial polyester, poly(3‐hydroxybutyrate‐ co ‐3‐hydroxyhexanoate) (PHBH). PHBH composites were prepared by melt‐compounding and injection molding, using PHBH with 3‐hydroxyhexanoate (3HH) molar fractions of 5.6% and 11.1%, and short glass fiber content varying from 0 to 23 volume percent. Tensile test results suggested that the glass fiber addition significantly increased Young's modulus and strength of PHBH. The Halpin–Tsai and Tsai–Pagano equations were used to predict Young's modulus of PHBH composites while the modified Kelly–Tyson model with the Bowyer–Bader method were used for strength prediction. These predictions gave reasonable estimates for the mechanical properties of PHBH composites. Differential scanning calorimetry results suggested that glass fiber addition had little effect on the degree of crystallinity of PHBH, as well as on the crystallization half‐time of PHBH containing 5.6 mol% 3HH. POLYM. COMPOS., 39:491–503, 2018. © 2016 Society of Plastics Engineers

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 score0.035
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.202
Teacher spread0.190 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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