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Record W4239939832 · doi:10.1002/ange.201808888

Adaptive Structured Pickering Emulsions and Porous Materials Based on Cellulose Nanocrystal Surfactants

2018· article· en· W4239939832 on OpenAlexaff
Yanan Li, Xubo Liu, Zhen Zhang, Shaojie Zhao, Guifang Tian, Jinkai Zheng, Dong Wang, Shaowei Shi, Thomas P. Russell

Bibliographic record

VenueAngewandte Chemie · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPickering emulsionChemical engineeringNanocrystalPolymerEmulsionMaterials sciencePorosityCellulosePorous mediumColloidNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Abstract Taking advantage of the formation and assembly of cellulose nanocrystal surfactants (CNCSs) at the water–oil interface, where polar cellulose nanocrystals (CNCs) and end‐functionalized polymer chains interact, the preparation and stability of emulsions prepared with CNCSs were investigated. The packing density of CNCSs at the interface can be adjusted by tuning parameters such as pH, ionic strength, and concentration/molecular weight of the end‐functionalized polymer ligands. Stable non‐spherical emulsions are obtained during homogenization, as a result of the interfacial jamming of CNCSs, with pH‐triggered reconfigurability. Porous materials are prepared by freeze‐drying creamed, CNCS‐stabilized emulsions. The cells of the porous materials have a controlled pore size and shape that are commensurate with the droplets in the emulsion and are responsive to pH. The behavior of the adaptive, reconfigurable supracolloidal system is coupled to its internal and surrounding environment.

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 score0.002
Threshold uncertainty score0.983

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)0.0010.000

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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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