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Record W2053543218 · doi:10.5539/eer.v4n3p105

Production of Pyrolysis Oil with Low Bromine and Antimony Contents from Plastic Material Containing Brominated Flame Retardants and Antimony Trioxide

2014· article· en· W2053543218 on OpenAlexvenueno aff
Hu Wu, Yafei Shen, Noboru Harada, Qi An, Kunio Yoshikawa

Bibliographic record

VenueEnergy and Environment Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
FundersNational Institute of Advanced Industrial Science and Technology
KeywordsAntimonyBromineAntimony trioxidePyrolysisFire retardantAntimony oxideChemistryYield (engineering)Materials scienceNuclear chemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Thermal degradation of high impact polystyrene (HIPS) containing brominated flame retardants and antimony trioxide (Sb2O3) was conducted at different temperatures with the presence of various additives (red mud, limestone and natural zeolite) in a fixed-bed reactor. The effect of the pyrolysis temperature on the product yield and the bromine content in the oil product was investigated. It was found that the maximum oil yield (84.38 wt.%) was obtained at the pyrolysis temperature of 500 ?. The pyrolysis temperature had no significant impact on the bromine reduction in the oil products. The bromine in the flame retardant was mainly transferred into the oil products, where the bromine content was in the range of 7.96-8.56 wt.%. With the aim of removing bromine and antimony from the oils, three additives (red mud, limestone and natural zeolite) was used to investigate the influence on the product yield and composition, especially on the bromine and antimony removal ability from the oil products. In this study, it was found that all of the additives could significantly lower the bromine and antimony content in the oils and the red mud was the most effective. The presence of red mud could reduce the bromine and antimony content from 8.21 and 1.84 wt.% when no additive was employed to 0.84 and 0.35 wt.%, respectively. In addition, the distribution and fate of bromine and antimony in the residues were also studied by the SEM-EDX and XRD analysis in detail.

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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

Citations32
Published2014
Admission routes1
Has abstractyes

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