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Record W3025169301 · doi:10.1149/ma2021-0112605mtgabs

(Invited) Graphene Aerogels: From Self-Assembly to Applications

2021· article· en· W3025169301 on OpenAlexaff
Marta Cerruti

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsMcGill University
Fundersnot available
KeywordsGrapheneAerogelMaterials scienceNanotechnologyPhotocatalysisOxidePorositySubstrate (aquarium)Absorption (acoustics)Composite materialCatalysisChemistry

Abstract

fetched live from OpenAlex

Graphene aerogels are highly porous and light structures that maintain most of the exceptional mechanical and electrical properties of graphene in a substrate that can be handled and used in practical applications. This is possible because the graphene flakes do not excessively restack during aerogel formation. In this talk we will explore how we can direct the self-assembly of graphene oxide flakes to generate graphene aerogels with different structures and mechanical properties, ranging from plastic to elastic structures and structures with dual porosity and highly controlled architectures. We will briefly highlight applications for these structures in fields as varied as sensing, pollutant absorption, photocatalysis, and cell culture.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.244
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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