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Record W2550906466 · doi:10.1002/aenm.201601630

A Comprehensive Approach toward Stable Lithium–Sulfur Batteries with High Volumetric Energy Density

2016· article· en· W2550906466 on OpenAlexafffund
Quanquan Pang, Xiao Liang, Chun Yuen Kwok, Joern Kulisch, Linda F. Nazar

Bibliographic record

VenueAdvanced Energy Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceSulfurPolysulfideElectrolyteCathodeAnodeElectrodeGrapheneLithium (medication)Chemical engineeringLithium–sulfur batteryBattery (electricity)Composite numberComposite materialNanotechnologyMetallurgyThermodynamics

Abstract

fetched live from OpenAlex

A comprehensive approach is reported to construct stable and high volumetric energy density lithium–sulfur batteries, by coupling a multifunctional and hierarchically structured sulfur composite with an in‐situ cross‐linked binder. Through a combination of first‐principles calculations and experimental studies, it is demonstrated that a hybrid sulfur host composed by alternately stacking graphene and layered graphitic carbon nitride embraces high electronic conductivity as well as high polysulfide adsorptivity. It is further shown that the cross‐linked elastomeric binder empowers the hierarchical sulfur composites—multi‐microns in size—with the ability to form crack‐free and compact high‐loading electrodes using traditional slurry processing. Using this approach, electrodes with up to 14.9 mg cm −2 sulfur loading and an extremely low electrolyte/sulfur ratio as low as 3.5: 1 µL mg −1 are obtained. This study sheds light on the essential role of multifaceted cathode design and further on the challenges facing lithium metal anodes in building high volumetric energy density lithium–sulfur 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 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 score0.003

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 communication0.0000.000
Open science0.0000.001
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 head0.009
GPT teacher head0.183
Teacher spread0.173 · 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

Citations317
Published2016
Admission routes2
Has abstractyes

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