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Record W1508682127 · doi:10.1002/pc.23564

Effect of agave fiber surface treatment on the properties of polyethylene composites produced by dry‐blending and compression molding

2015· article· en· W1508682127 on OpenAlex
Erick Omar Cisneros‐López, J. Anzaldo, Francisco Javier Fuentes-Talavera, Rubén González‐Núñez, Jorge Ramón Robledo‐Ortíz, Denis Rodrigue

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.

Bibliographic record

VenuePolymer Composites · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceComposite materialCompression moldingPolyethyleneFiberUltimate tensile strengthComposite numberNatural fiberMolding (decorative)High-density polyethylenePolymerModulusCompression (physics)Mold

Abstract

fetched live from OpenAlex

In this work, the effect of natural fiber surface treatment with maleated polyethylene (MAPE) is presented to improve the mechanical properties of natural fiber composites (NFC). In particular, a simple dry blending technique was used to disperse natural fibers (agave) in a polymer matrix (linear low density polyethylene) and produce samples via compression molding. The effect of fiber content was also studied (0, 10, 20, 30, and 40 wt%) and the samples were characterized in terms of morphology, density, hardness, as well as mechanical (tensile, flexion, and impact) and thermal (DSC and TGA) properties. The results show that the simple dry‐blending method is efficient to produce homogeneous NFC and that surface treatment can substantially improve composite modulus (164%) and strength (121%). POLYM. COMPOS., 38:96–104, 2017. © 2015 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 score0.015
Threshold uncertainty score0.926

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 studies0.0000.000
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 head0.022
GPT teacher head0.249
Teacher spread0.228 · 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