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Record W4286435971 · doi:10.1016/j.jcomc.2022.100297

A review of the recent developments in flame-retardant nylon composites

2022· review· en· W4286435971 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.

Bibliographic record

VenueComposites Part C Open Access · 2022
Typereview
Languageen
FieldMaterials Science
TopicFlame retardant materials and properties
Canadian institutionsUniversity of Windsor
FundersNational Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and InformatizationWenzhou Municipal Science and Technology BureauNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsFire retardantMaterials scienceComposite materialNylon 6Polymer

Abstract

fetched live from OpenAlex

An overview of recent developments of flame-retardant nylon 6 and nylon 66 is presented in this paper. The research of flame-retardant composites is mainly through adding flame-retardant elements to nylon molecules, which can be divided into physical addition and chemical addition. Each of these two methods has its advantages and disadvantages. In this paper, according to the different addition methods and flame-retardant mechanism of various flame-retardants, the flame-retardant properties of phosphorus flame-retardants, nitrogen flame-retardants, bio based flame-retardants and synergistic systems, as well as their effects on the mechanical properties of flame-retardant composites have been critically reviewed and summarized. This review will objectively explore and give new direction for the development of the flame-retardant nylon materials, which would be more accessible to the emerging field of materials science.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0100.011
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.204
GPT teacher head0.416
Teacher spread0.212 · 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