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A Simple and Efficient Approach to Cellulose/Silica Composite Aerogel with High Silica Utilization Efficiency

2015· article· en· W2006704315 on OpenAlexvenueno aff
Chong Lin, Ang Li, Yang Cao, Lingbin Lu

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2015
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsnot available
FundersHainan University
KeywordsAerogelMaterials scienceCelluloseComposite numberThermal stabilityPorosityComposite materialThermal conductivityChemical engineering

Abstract

fetched live from OpenAlex

Cellulose aerogel is a fascinating material with high porosity, low density and biocompatibility. However, cellulose aerogel lacks sufficient thermal stability. Recombination between cellulose aerogel with silica is efficacious for enhance the cellulose aerogel’s thermal stability. This work described a simple and efficient approach to the cellulose/silica composite aerogel via a dropwise manner, using tetraethoxysilane as silicon source and NaOH solution as cellulose solvent. The result showed that the thermal stability of cellulose aerogel was enhanced by introducing silica. And by this manner, the utilization efficiency of silica was up to 95%. The composite aerogel had a low density and a high porosity, which promised the material a good heat insulation performance, and the thermal conductivity of the composite aerogel was low to 0.0161W/(m·K). Moreover, by adjusting cellulose concentration and tetraethoxysilane amount, the density, porosity and thermal conductivity of the composite aerogel could be controlled. This work contributed to improving the utilization efficiency of silica for the composite aerogel with better performances.

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)0.003
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 score0.054
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.065
GPT teacher head0.346
Teacher spread0.281 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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