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Record W2379741391

Research on Ammonium Polyphosphate/Aluminum Hydroxide/Methyl Vinyl Silicone Rubber (APP/ATH/MVQ) Flame Retardant Composites

2012· article· en· W2379741391 on OpenAlexaff
Qian Huang-hai

Bibliographic record

VenueSilicone Material · 2012
Typearticle
Languageen
FieldMaterials Science
TopicFlame retardant materials and properties
Canadian institutionsScience North
Fundersnot available
KeywordsAmmonium polyphosphateComposite materialMaterials scienceFire retardantUltimate tensile strengthShore durometerDissipation factorSilicone rubberElongationNatural rubberDielectricSilicone
DOInot available

Abstract

fetched live from OpenAlex

The ammonium polyphosphate/aluminum hydroxide/methyl vinyl silicone rubber(APP/ATH/MVQ) flame retardant composites were prepared by single APP,ATH,and APP/ATH hybrid fillers,respectively.The effect of the content and the combination-pattern of different fillers on the flame retardancy,mechanical and dielectric properties of the APP/ATH/MVQ composites was discussed.The results showed that the flame retardancy was improved with increasing the content of the flame retardant fillers.And the flame retardancy of the composites filled with APP/ATH hybrid fillers had better flame retardancy than that of the composites filled with single APP or ATH.The Shore A hardness,dielectric constant,and dielectric dissipation factor were all improved with increasing the content of the APP/ATH hybrid fillers.However,the tensile strength and elongation at break were decreased.With the total amount of APP/ATH(mass fraction was 3∶ 2) of 80,the oxygen index(OI) of the rubber was 44%,and the tensile strength,elongation at break,Shore A hardness,dielectric constant and dielectric dissipation factor were 6.8 MPa,438%,62,3.92,249%,respectively.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.046
GPT teacher head0.305
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2012
Admission routes1
Has abstractyes

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