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Synthesis of Nanoplatelet Zinc Borate and its Combination with Expandable Graphite and Red Phosphorus as Flame Retardants for Polypropylene

2022· article· en· W4286218358 on OpenAlexfundno aff
Truong Cong Doanh, Nguyen Thi My Hanh, Nguyen Thi Thu Hien, Doan Tien Dat, Vu Minh Tan, Mai Ha Hoang

Bibliographic record

VenueVNU Journal of Science Natural Sciences and Technology · 2022
Typearticle
Languageen
FieldMaterials Science
TopicFlame retardant materials and properties
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le CancerVietnam Academy of Science and Technology
KeywordsZinc borateMaterials scienceFire retardantZincPolypropyleneNanocompositeContact angleChemical engineeringScanning electron microscopeFourier transform infrared spectroscopyNuclear chemistryNanoparticleBoronComposite materialMetallurgyChemistryOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

The zinc borate nanoparticles (2ZnO.3B2O3.3H2O) were successfully prepared by precipitation reaction in aqueous solutions of borax and zinc sulfate using oleic acid as a surfactant. The structural, morphological characteristics and the wettability of the particle surface were studied through Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and the contact angle. The hydrophobic zinc borate (n-ZB) had nanoplatelet morphology with a diameter of 1.0 - 1.5 µm and thickness of about 90 nm. Nano zinc borate showed a synergistic influence with expandable graphite (EG) and red phosphorus (RP) on the flame retardant properties and thermo-oxidative stability of polypropylene (PP). The nanocomposite 7n-ZB/7RP/7EG/PP achieved the V-1 UL94 verticle burning test with a limited oxygen index of 23.7% and the char yield of 14.67 wt.% at 900 oC. Furthermore, the fire retardant performance and the mechanical properties of the nanocomposites loading ZB nanoparticles were improved compared with the composites employing commercial ZB microparticles.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
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 head0.225
Teacher spread0.217 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueVNU Journal of Science Natural Sciences and TechnologySame topicFlame retardant materials and propertiesFrench-language works237,207