MétaCan
Menu
Back to cohort
Record W2605055306 · doi:10.1002/celc.201700295

Au Nanochains Anchored on 3D Polyaniline/Reduced Graphene Oxide Nanocomposites as a High‐Performance Catalyst for Ethanol Electrooxidation

2017· article· en· W2605055306 on OpenAlexaff
Ke Zhang, Yuting Shi, Shumin Li, Caiqin Wang, Bo Yan, Hui Xu, Jin Wang, Jun Guo, Yukou Du

Bibliographic record

VenueChemElectroChem · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsGraphenePolyanilineMaterials scienceOxideCatalysisNanocompositeChemical engineeringElectrolyteDispersion (optics)ElectrochemistryDispersion stabilityNanoparticleNanotechnologyChemistryElectrodeComposite materialPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A nanocomposite containing Au, polyaniline (PANI) and reduced graphene oxide (RGO) has been synthesized by a two‐step method. The PANI can intermix with graphene to build a three‐dimensional (3D) structure, which is beneficial for uniform dispersion of Au networks with a mean diameter of 5.6 nm. In addition to its role as the support of Au, the presence of PANI is also favorable for avoiding the heavy agglomeration of graphene when the reduction occurs. Additionally, the introduction of graphene can not only boost the connections with PANI, but also accelerate the electron transfer between the electrolyte solution and catalysts. Electrochemical tests indicate that the Au/PANI/RGO hybrid exhibits high catalytic activity and stability for ethanol electrooxidation in alkaline conditions. The superior performance can be ascribed to the uniform dispersion of the Au nanocatalyst on the PANI/RGO support with a particular 3D structure, resulting in an increase of the electrochemically active surface area together with a synergic effect between the PANI, graphene and Au.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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.

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

Citations16
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueChemElectroChemSame topicElectrocatalysts for Energy ConversionFrench-language works237,207