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Record W4296783445 · doi:10.1177/08927057221128186

Effect of microwave heat treatment on physical and mechanical properties of high-Density polyethylene/wood flour/Nano-SiO <sub>2</sub> composites

2022· article· en· W4296783445 on OpenAlexaff
Amir Amini, Mohammad Farsi, Masoud Ebadi, Fatemeh Maashi Sani, Majid Shahbabaei

Bibliographic record

VenueJournal of Thermoplastic Composite Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceComposite materialFlexural strengthWood flourAbsorption of waterFlexural modulusMolding (decorative)Izod impact strength testPolyethyleneHigh-density polyethyleneSwellingCompression moldingMicrowaveHeat deflection temperatureUltimate tensile strengthMold

Abstract

fetched live from OpenAlex

The study aimed at investigating the impact of Microwave Heat Treatment (MHT) on the physical and mechanical properties of wood flour (WF)/Nano-SiO 2 /High-Density polyethylene composites by varying Nano-SiO 2 loading from 0, 1, 2, and 3% wt with a constant WF loading at 50% wt. By milling the materials in which were firstly mixed in a twin-screw extruder (Brabender® Plasti-Corder®), wood-plastic composites ASTM Standard samples were produced by an injection molding machine. Microwave irradiation with a power of 900 W and a temperature of 85°C for 7 min was applied for post-treatment of WPCs. After applying the microwave treatment, samples were subjected to physical and mechanical tests. The results indicated that increased Nano-SiO 2 loading up to 3 wt% led to increasing the flexural strength and modulus while decreasing the impact resistance, water absorption, and thickness swelling of samples. As such, morphological analyses revealed that MHT increased the flexural modulus and strength, as well as, decreased the impact strength, water adsorption, and thickness swelling of the samples.

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 categoriesMeta-epidemiology (narrow)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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