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Record W2757484711 · doi:10.1002/smtd.201700237

Green Solid Electrolyte with Cofunctionalized Nanocellulose/Graphene Oxide Interpenetrating Network for Electrochemical Gas Sensors

2017· article· en· W2757484711 on OpenAlexafffund
Jing Zhang, Gaopeng Jiang, M. Golędzinowski, Felix J. E. Comeau, Kecheng Li, Timothy Cumberland, Jared Lenos, Pan Xu, Matthew Li, Aiping Yu, Zhongwei Chen

Bibliographic record

VenueSmall Methods · 2017
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsAlcohol Countermeasure Systems (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsGrapheneElectrolyteMaterials scienceCelluloseNanocelluloseOxideVinyl alcoholChemical engineeringMembraneElectrochemistryElectrochemical gas sensorNanofiberConductivityProton exchange membrane fuel cellNanotechnologyElectrodeChemistryComposite materialPolymer

Abstract

fetched live from OpenAlex

Abstract A cofunctionalized cellulose/graphene oxide (GO) proton‐conducting solid electrolyte with a 3D interpenetrating network structure is developed in an efficient and green strategy, and successfully applied in an electrochemical gas sensor for the detection of alcohol, namely an alcohol fuel‐cell sensor. With grafted sulfonic acid groups onto the surface of cellulose nanofibers and GO nanosheets, the membrane is endowed with proton conductivity along both the through‐plane and the in‐plane ion‐transport channels. The alcohol fuel‐cell sensor equipped with cofunctionalized cellulose/GO membrane demonstrates great responses to ethanol vapor at different concentrations, showing excellent linearity and sensitivity, as well as low ethanol‐detection limits approaching 25 ppm. This novel concept of developing a cofunctionalized cellulose/GO membrane opens a promising route for the application of ion‐conducting solid electrolyte in electrochemical devices, particularly in electrochemical gas sensors.

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 head0.043
GPT teacher head0.348
Teacher spread0.305 · 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

Citations72
Published2017
Admission routes2
Has abstractyes

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