MétaCan
Menu
Back to cohort
Record W3212735519 · doi:10.1021/acsnano.1c07084

Superstable Wet Foams and Lightweight Solid Composites from Nanocellulose and Hydrophobic Particles

2021· article· en· W3212735519 on OpenAlexafffund
Roozbeh Abidnejad, Marco Beaumont, Blaise L. Tardy, Bruno D. Mattos, Orlando J. Rojas

Bibliographic record

VenueACS Nano · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of British Columbia
FundersAustrian Science FundH2020 European Research CouncilCanada Excellence Research Chairs, Government of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceCoalescence (physics)NanocellulosePorosityComposite materialCelluloseColloidChemical engineering

Abstract

fetched live from OpenAlex

Colloids are suitable options to replace surfactants in the formation of multiphase systems while simultaneously achieving performance benefits. We introduce synergetic combination of colloids for the interfacial stabilization of complex fluids that can be converted into lightweight materials. The strong interactions between high aspect ratio and hydrophilic fibrillated cellulose (CNF) with low aspect ratio hydrophobic particles afford superstable Pickering foams. The foams were used as a scaffolding precursor of porous, solid materials. Compared to foams stabilized by the hydrophobic particles alone, the introduction of CNF significantly increased the foamability (by up to 350%) and foam lifetime. These effects are ascribed to the fibrillar network formed by CNF. The CNF solid fraction regulated the interparticle interactions in the wet foam, delaying or preventing drainage, coarsening, and bubble coalescence. Upon drying, such a complex fluid was transformed into lightweight and strong architectures, which displayed properties that depended on the surface energy of the CNF precursor. We show that CNF combined with hydrophobic particles universally forms superstable complex fluids that can be used as a processing route to synthesize strong composites and lightweight structures.

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 score0.374

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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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

Citations50
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueACS NanoSame topicPickering emulsions and particle stabilizationFrench-language works237,207