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Record W4400688281 · doi:10.1002/smll.202400952

Interfacial Membranization of Regenerated Cellulose Nanoparticles and a Protein Renders Stable Water‐in‐Water Emulsion

2024· article· en· W4400688281 on OpenAlexafffund
Ya Zhu, Marco Beaumont, Katariina Solin, Panagiotis Spiliopoulos, Bin Zhao, Han Tao, Eero Kontturi, Long Bai, Orlando J. Rojas

Bibliographic record

VenueSmall · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of British Columbia
FundersAustrian Science FundEuropean CommissionHORIZON EUROPE Reforming and enhancing the European Research and Innovation systemChina Scholarship CouncilCanada Excellence Research Chairs, Government of CanadaCanada Foundation for Innovation
KeywordsBiopolymerBovine serum albuminPickering emulsionChemical engineeringCelluloseEmulsionNanoparticleMaterials scienceAqueous solutionColloidAdsorptionRegenerated cellulosePolymer chemistryPolymerChemistryNanotechnologyOrganic chemistryChromatographyComposite material

Abstract

fetched live from OpenAlex

Abstract Pickering water‐in‐water (W/W) emulsions stabilized by biobased colloids are pertinent to engineering biomaterials with hierarchical and confined architectures. In this study, stable W/W emulsions are developed through membranization utilizing biopolymer structures formed by the adsorption of cellulose II nanospheres and a globular protein, bovine serum albumin (BSA), at droplet surfaces. The produced cellulose II nanospheres (NPcat, 63 nm diameter) bearing a soft and highly accessible shell, endow rapid and significant binding (16 mg cm − 2 ) with BSA. NPcat and BSA formed complexes that spontaneously stabilized liquid droplets, resulting in stable W/W emulsions. It is proposed that such a system is a versatile all‐aqueous platform for encapsulation, (bio)catalysis, delivery, and synthetic cell mimetics.

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 score0.010
Threshold uncertainty score0.226

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 head0.018
GPT teacher head0.225
Teacher spread0.207 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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