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Record W3106881006 · doi:10.1002/adfm.202004239

A High Capacity All Solid‐State Li‐Sulfur Battery Enabled by Conversion‐Intercalation Hybrid Cathode Architecture

2020· article· en· W3106881006 on OpenAlexafffund
Shiqi Xu, Chun Yuen Kwok, Laidong Zhou, Zhizhen Zhang, Ivan Kochetkov, Linda F. Nazar

Bibliographic record

VenueAdvanced Functional Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsFaraday efficiencyMaterials scienceIntercalation (chemistry)CathodeBattery (electricity)Gravimetric analysisElectrochemistryNanomaterialsSulfurChemical engineeringEnergy storageRedoxLithium (medication)NanotechnologyInorganic chemistryElectrodeChemistryOrganic chemistryPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract As demands for electrochemical energy storage continue to rise, alternative electrochemistries to conventional Li‐ion batteries become more appealing. Here, an intercalation‐conversion hybrid cathode that combines intercalation‐type VS2 with conversion‐type sulfur chemistry to construct high performance solid‐state lithium‐sulfur batteries is reported. The layered VS2 nanomaterial features Li‐ion transport channels, metallic conductivity, and active capacity contribution, all of which provide an ideal platform for the solid state S/Li2S redox couple to unlock its high gravimetric capacity. The S/VS2/Li3PS4 hybrid cathode composite is prepared by a facile, low‐cost, and low‐energy mechanical blending process. The S/VS2/Li3PS4|Li3PS4|Li/In (or Li) all‐solid‐state cell exhibits sulfur utilization of ≈85%, with a Coulombic efficiency of close to 100%. High areal capacity up to 7.8 mA h cm−2 with an active material loading (S/VS2) as high as 15.5 mg cm−2 is achieved.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.194
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), 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

Citations91
Published2020
Admission routes2
Has abstractyes

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