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Record W3022425743 · doi:10.1126/sciadv.aaz7328

Nanofibrillar networks enable universal assembly of superstructured particle constructs

2020· article· en· W3022425743 on OpenAlex
Bruno D. Mattos, Blaise L. Tardy, Luiz G. Greca, Tero Kämäräinen, Wenchao Xiang, Oriol Cusola, Washington Luiz Esteves Magalhães, Orlando J. Rojas

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of British Columbia
FundersEuropean Research Council
KeywordsVariety (cybernetics)Particle (ecology)NanotechnologyComputer scienceMaterials scienceArtificial intelligenceBiologyEcology

Abstract

fetched live from OpenAlex

Superstructured colloidal materials exploit the synergies between components to develop new or enhanced functions. Cohesion is a primary requirement for scaling up these assemblies into bulk materials, and it has only been fulfilled in case-specific bases. Here, we demonstrate that the topology of nanonetworks formed from cellulose nanofibrils (CNFs) enables robust superstructuring with virtually any particle. An intermixed network of fibrils with particles increases the toughness of the assemblies by up to three orders of magnitude compared, for instance, to sintering. Supramolecular cohesion is transferred from the fibrils to the constructs following a power law, with a constant decay factor for particle sizes from 230 nm to 40 μm. Our findings are applicable to other nanofiber dimensions via a rationalization of the morphological aspects of both particles and nanofibers. CNF-based cohesion will move developments of functional colloids from laboratory-scale toward their implementation in large-scale nanomanufacturing of bulk materials.

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.

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 score0.007
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.248
Teacher spread0.233 · 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