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Record W2801548606 · doi:10.1002/cssc.201800512

Synthesis of Grain‐like MoS<sub>2</sub> for High‐Performance Sodium‐Ion Batteries

2018· article· en· W2801548606 on OpenAlexaff
Kai Yao, Zhanwei Xu, Zhi Li, Xinyue Liu, Xuetao Shen, Liyun Cao, Jianfeng Huang

Bibliographic record

VenueChemSusChem · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFaraday efficiencyStackingAnodeMaterials scienceChemical engineeringSulfurElectrodeNanotechnologyChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract MoS 2 is a promising anode material for sodium‐ion batteries (SIBs) due to its attractive theoretical capacity and low cost. MoS 2 generally presents a sheet‐like structure based on its (002) lattice plane; however, such a structure tends to result in agglomeration and stacking of the sheets that cannot accommodate volume expansion, resulting in poor cyclability. Herein, grain‐like MoS 2 particulates (G‐MoS 2 ) are synthesized by sulfiding MoO 3 in highly concentrated sulfur vapor, which results in epitaxial growth of MoS 2 in (002), (100), and (110) lattice planes, with the product consisting of MoS 2 particulates of about 300 nm coated with few‐layered MoS 2 nanosheets. The unique G‐MoS 2 architecture ensures good dispersion and sufficient distance to accommodate volume expansion during sodiation/desodiation, which effectively prevents stacking of MoS 2 , maintaining structural stability. When employed as the working electrode for SIB, G‐MoS 2 delivers a high reversible capacity of 324 mAh g −1 at 0.5 A g −1 , retaining 312 mAh g −1 over 300 cycles with an average coulombic efficiency of 99.8 %. Even when G‐MoS 2 is cycled at a high current density (2.0 A g −1 ), the retained capacity is 175 mAh g −1 after 400 cycles. Comparison with literature reveals that these capacities are among the more promising reversible values reported for pure MoS 2 .

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 score0.007
Threshold uncertainty score0.997

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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations49
Published2018
Admission routes1
Has abstractyes

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