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Record W2898689949 · doi:10.21967/jbb.v3i4.92

Pickering emulsion polymerization of styrene stabilized by nanocrystalline cellulose

2018· article· en· W2898689949 on OpenAlexvenueno aff
Mingqin Lin, Changsheng Song, Zipei Shi, Heng Zhang

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2018
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsCreamingPickering emulsionStyreneEmulsionMaterials scienceChemical engineeringEmulsion polymerizationPolystyreneMicrocrystalline celluloseCellulosePolymer chemistryHydrolysisPolymerizationNanocelluloseOrganic chemistryChemistryComposite materialCopolymerPolymer

Abstract

fetched live from OpenAlex

In this work, polystyrene-based oil-in-water Pickering emulsion stabilized by nanocrystalline cellulose (NCC) was formulated. The NCC was prepared by sulfuric-acid-catalyzed hydrolysis of microcrystalline cellulose, with a yield of 60% and an average particle size of about 152.9 nm. When the content of NCC was 5 g/L, the surface tension was 54.58 mN/m, and stable styrene-based Pickering emulsion was prepared using NCC as the stabilizer. The presence of NCC particles in the emulsion system resulted in high resistance against creaming. Due to improved stability, the conversion efficiency of styrene was higher in the polymerization process of the styrene-based Pickering emulsion.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.219
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 teacher head, 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
Published2018
Admission routes1
Has abstractyes

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