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Record W2788289539 · doi:10.1021/acs.chemmater.7b04800

Biotemplated Lightweight γ-Alumina Aerogels

2018· article· en· W2788289539 on OpenAlexafffund
Thanh‐Dinh Nguyen, Dorothy Tang, Francesco D’Acierno, Carl A. Michal, Mark J. MacLachlan

Bibliographic record

VenueChemistry of Materials · 2018
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAerogelChitosanMaterials scienceAqueous solutionNanofiberSelf-healing hydrogelsChemical engineeringDissolutionComposite materialPolymer chemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We present the biotemplating of γ-Al 2 O 3 aerogels with chitosan nanofibrils. Aluminum–chitosan interactions cause the swelling of iridescent chitosan structures into helicoidal hydrogels and the subsequent aqueous dissolution of swollen fibrils to form Al–chitosan solutions. Viscous aqueous solutions of Al 3+ –chitosan hybrid nanofibers were freeze-dried to give lightweight cotton-like aerogels. Homogeneous incorporation of Al 3+ ions into chitosan yields water-soluble nanofibrils that can serve as polymeric templates to support Al 3+ ions in the aerogel composites. We investigated thermal removal of chitosan in the composites to obtain lightweight γ-Al 2 O 3 nanocrystal aerogels that retain the weblike fiber networks of the chitosan template. These biotemplated alumina aerogel materials are promising candidates for catalyst supports and thermal insulation.

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations48
Published2018
Admission routes2
Has abstractyes

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