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Record W2981940764 · doi:10.1002/batt.201900121

Co−Ni Binary‐Metal Oxide Coated with Porous Carbon Derived from Metal‐Organic Framework as Host of Nano‐Sulfur for Lithium‐Sulfur Batteries

2019· article· en· W2981940764 on OpenAlexaff
Xuan Zhang, Yu Fan, Muhammad Arif Khan, Hongbin Zhao, Daixin Ye, Jiulin Wang, Baohua Yue, Jianhui Fang, Jiaqiang Xu, Lei Zhang, Jiujun Zhang

Bibliographic record

VenueBatteries & Supercaps · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsBC Innovation CouncilNational Research Council Canada
FundersNational Key Research and Development Program of China
KeywordsSulfurChemical engineeringMaterials scienceLithium–sulfur batteryCathodeCarbon fibersBimetallic stripBattery (electricity)DissolutionComposite numberInorganic chemistryMetalChemistryElectrolyteElectrodeComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract Lithium‐sulfur battery is considered as a promising energy storage system because of its high energy density. The specific capacity and cycling stability of sulfur cathode, however, are impeded by intrinsic poor electrical conductivity of sulfur and dissolution of polysulfides intermediates. Herein, we demonstrate a novel strategy to overcome the two obstacles by designing a bimetallic‐organic‐framework‐derived nano‐sulfur host consisted of porous graphitic carbon and bimetallic cobalt‐nickel oxides (C/NiCo 2 O 4 ), in which porous carbon and NiCo 2 O 4 not only entrapping the polysulfides effectively through physical and chemical entrapment capability, but also serving as a highly conductive matrix for sulfur. With a sulfur content of 68.9 % in the composite, the composite cathode delivered a specific capacity of 977 mAh g −1 and maintained 673 mAh g −1 at 0.5 C over 500 cycles. Besides, the binding mechanism between NiCo 2 O 4 and polysulfides has been explored by ex situ XRD and density functional theory(DFT)simulation. This work may provide a feasible strategy to improve the performance of lithium‐sulfur battery.

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)
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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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

Citations51
Published2019
Admission routes1
Has abstractyes

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