MétaCan
Menu
Back to cohort
Record W2972854972 · doi:10.1039/c9sm01028a

Mechanical properties of cellulose aerogels and cryogels

2019· article· en· W2972854972 on OpenAlex

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

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.013
Threshold uncertainty score0.997

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)0.0040.000

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