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Record W2972854972 · doi:10.1039/c9sm01028a

Mechanical properties of cellulose aerogels and cryogels

2019· article· en· W2972854972 on OpenAlexaff
Nela Buchtová, Christophe Pradille, Jean‐Luc Bouvard, Tatiana Budtova

Bibliographic record

VenueSoft Matter · 2019
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsCanadian Nautical Research Society
FundersMinistère de l'Agriculture et de l'Alimentation
KeywordsCelluloseMaterials scienceChemical engineeringComposite materialChemistryEngineering

Abstract

fetched live from OpenAlex

Highly porous and lightweight cellulose materials were prepared via dissolution-coagulation and different drying routes. Cellulose of three different molecular weights was dissolved in an ionic liquid/dimethyl sulfoxide mixture. Drying was performed either with supercritical CO2 resulting in "aerogels", or via freeze-drying resulting in "cryogels". The influence of cellulose molecular weight, concentration and drying method on the morphology, density, porosity and specific surface area was determined. The mechanical properties of cellulose cryogels and aerogels under uniaxial compression were studied in detail and analyzed in the view of existing models developed for porous materials. It was demonstrated that the Poisson's ratio of cellulose aerogels is not equal to zero, contrary to what is usually reported in the literature, but decreases with an increase in density. Compressive modulus and yield stress of cryogels turned out to be higher than those of aerogels taken at the same density. This was interpreted by the different morphology of the porous materials studied.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), 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

Citations137
Published2019
Admission routes1
Has abstractyes

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