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Record W2905068512 · doi:10.1021/acssuschemeng.8b05085

Elasticity-Enhanced and Aligned Structure Nanocellulose Foam-like Aerogel Assembled with Cooperation of Chemical Art and Gradient Freezing

2018· article· en· W2905068512 on OpenAlexaff
Yuan Chen, Dongbin Fan, Shaoyi Lyu, Gaiyun Li, Feng Jiang, Siqun Wang

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersChinese Academy of ForestryNational Natural Science Foundation of China
KeywordsAerogelMaterials scienceLamellar structureNanocellulosePorosityComposite materialBacterial celluloseCelluloseChemical engineeringBiopolymerElasticity (physics)Polymer

Abstract

fetched live from OpenAlex

Cellulose nanofibril (CNF) aerogels are renewable and biocompatible materials with high porosity and tunable surface chemistry. However, ultralight and ultraporous aerogels remain a great challenge to obtain high elasticity. This work focused on a scalable strategy to create large-scale lamellar-aligned CNF foam-like aerogels and the relationship between structure and mechanical properties. The morphology and mechanical properties of aerogels assembled by original TEMPO-mediated oxidation CNF cross-linking with 1,2,3,4-butanetetracarboxylic acid were investigated for homogeneous freezer freezing and unidirectional gradient freeze-casting. This study successfully fabricated ultralight foam-like aerogels with centimeter-sized and aligned lamellar/porous structure via cooperation of tunable chemical reaction and unidirectional gradient freezing. The resulting aerogels exhibited flyweight densities of 3–4 mg/cm3, enhanced recovery from 70% strain, water adsorption 82.5 times over self-weight at 20 °C and 52.0 times over self-weight at high temperature (100 °C) for 20 cycles. Moreover, the aligned aerogel followed by carbonization showed a differential and anisotropic electrical resistivity.

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 score0.006
Threshold uncertainty score0.919

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 head0.004
GPT teacher head0.206
Teacher spread0.203 · 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

Citations78
Published2018
Admission routes1
Has abstractyes

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