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Record W2055121120 · doi:10.1002/pc.23316

Effect of nanocrystalline cellulose on morphological, thermal, and mechanical properties of Nylon 6 composites

2014· article· en· W2055121120 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolymer Composites · 2014
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialUltimate tensile strengthFlexural strengthCompoundingIzod impact strength testNylon 6NanocompositeElongationNanocrystalline materialFlexural modulusCelluloseDispersion (optics)PolymerChemical engineering

Abstract

fetched live from OpenAlex

In this study, nanocomposites based on Nylon 6 and nanocrystalline cellulose (NCC) were prepared by melt compounding. Then, morphological, thermal, and mechanical properties were analyzed for NCC content between 0 and 7 wt%. Morphological analyses showed different roughness in fractured surface of neat Nylon and its nanocomposites caused by the presence of NCC. Mechanical results showed that the optimum properties were obtained at 3% NCC which could be related to relatively good NCC dispersion at low concentrations with good Nylon‐NCC bonding. Overall, flexural (41%) and tensile (23%) moduli, as well as tensile strength (11%) were increased up to 3% of NCC. However, elongation at break and impact strength decreased with NCC addition. Finally, density and hardness showed only a small increase of 5 and 3%, respectively. POLYM. COMPOS., 37:1473–1479, 2016. © 2014 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.

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 score0.005
Threshold uncertainty score0.651

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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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