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Record W3082140590 · doi:10.1039/d0nr05050d

The lightest solid meets the lightest gas: an overview of carbon aerogels and their composites for hydrogen related applications

2020· article· en· W3082140590 on OpenAlexaff
Sheng Yü, Shaolei Song, Rong Li, Baizeng Fang

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsVancouver Biotech (Canada)University of British Columbia
FundersNational Natural Science Foundation of China
KeywordsCarbon fibersHydrogenMaterials scienceComposite materialChemistryComposite numberOrganic chemistry

Abstract

fetched live from OpenAlex

century. Hydrogen production, hydrogen storage and hydrogen sensing are three most important aspects for hydrogen economy. Interestingly, the lightest solid, carbon aerogels (CAs), has found wide applications in these aspects due to its unique characteristics including large specific surface area, hierarchical porous structure, high electrical conductivity, superb chemical stability, and low fabrication cost. Herein, various fabrication strategies of CAs are presented, and their applications in the three most important aspects are comprehensively reviewed. In addition, the challenges and prospects are also discussed. In the light of the recent progress in CAs for hydrogen-related applications, this review provides a comprehensive assessment on materials selection, synthesis, hydrogen adsorption characteristics of CAs and catalytic activity of CA-supported nanocatalysts, offering a strategic guide to build a close connection between CAs and hydrogen economy.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.281
Teacher spread0.247 · 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
GenreReview

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

Citations64
Published2020
Admission routes1
Has abstractyes

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