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Record W2767595407 · doi:10.3390/en10111844

Novel Carbon Materials in the Cathode Formulation for High Rate Rechargeable Hybrid Aqueous Batteries

2017· article· en· W2767595407 on OpenAlexafffund
Xiao Zhu, Tuan K.A. Hoang, Pu Chen

Bibliographic record

VenueEnergies · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research Chairs
KeywordsCathodeGrapheneBattery (electricity)Materials scienceAqueous solutionCarbon nanotubeConductivityCarbon fibersChemical engineeringPorosityElectrical conductorNanotechnologyComposite materialComposite numberChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Novel carbon materials, carbon nanotubes (CNTs) and porous graphene (PG), were exploited and used as conductive additives to improve the rate performance of LiMn2O4 cathode for the rechargeable aqueous Zn/LiMn2O4 battery, namely the rechargeable hybrid aqueous battery (ReHAB). Thanks to the long-range conductivity and stable conductive network provided by CNTs, the rate and cycling performances of LiMn2O4 cathode in ReHAB are highly improved—up to about 100 mAh·g−1 capacity is observed at 10 C (1 C = 120 mAh·g−1). Except for CNTs, porous graphene (PG) with a high surface area, an abundant porous structure, and an excellent electrical conductivity facilitates the transportation of Li ions and electrons, which can also obviously enhance the rate capability of the ReHAB. This is important because the ReHAB could be charged/discharged in a few minutes, and this leads to potential application of the ReHAB in automobile industry.

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 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 score0.013
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.022
GPT teacher head0.239
Teacher spread0.217 · 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.

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

Citations10
Published2017
Admission routes2
Has abstractyes

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