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Record W4414564322 · doi:10.1021/acsami.5c12948

Synergistic Effect of VS<sub><b>2</b></sub>/MoS<sub><b>2</b></sub> as an Electrocatalyst for Accelerating Polysulfide Conversion in Lithium–Sulfur Batteries

2025· article· en· W4414564322 on OpenAlexafffund
Thilini Boteju, Abinaya Sivakumaran, Sathish Ponnurangam, Venkataraman Thangadurai

Bibliographic record

VenueACS Applied Materials & Interfaces · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du Canada
KeywordsPolysulfideElectrocatalystElectrochemistryLamellar structureCatalysisAdsorptionBattery (electricity)Hydrothermal circulationConductivity

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The shuttle effect of soluble lithium polysulfides (LiPSs) poses a formidable challenge that severely compromises the electrochemical performance of lithium–sulfur (Li–S) batteries. This study introduces a unique lamellar stacked VS 2 /MoS 2 nanoflower structure, prepared using a simple one-step hydrothermal synthesis method, to reduce the polysulfide shuttle effect in Li–S batteries. VS 2 /MoS 2 synergistically boosts LiPS conversion, combining VS 2 ’s high conductivity with the catalytic activity of MoS 2, as confirmed by density functional theory (DFT) calculations. Electrochemical testing demonstrated excellent performance for VS 2 /MoS 2 @S cathodes. It delivers an initial discharge-specific capacity of 1353 mAh g –1 at 0.1 C, and at 1 C, the capacity remains as high as 925 mAh g –1 . At 0.2 C, the initial discharge-specific capacity is 1299 mAh g –1, and the capacity retention rate reaches 55% after 500 cycles. This study provides valuable insights into designing and engineering high-performance heterostructures to enhance the adsorption of LiPSs and improve the reaction kinetics in Li–S batteries.

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)0.001
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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