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Record W2898442600 · doi:10.1002/adma.201804271

A Lithium–Sulfur Battery using a 2D Current Collector Architecture with a Large‐Sized Sulfur Host Operated under High Areal Loading and Low E/S Ratio

2018· article· en· W2898442600 on OpenAlexafffund
Matthew Li, Yining Zhang, Zhengyu Bai, Wen Wen Liu, Tongchao Liu, Jihyeon Gim, Gaopeng Jiang, Yifei Yuan, Dan Luo, Kun Feng, Reza S. Yassar, Xiaolei Wang, Zhongwei Chen, Jun Lü

Bibliographic record

VenueAdvanced Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsConcordia UniversityUniversity of Waterloo
FundersArgonne National LaboratoryWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaDivision of Materials ResearchOffice of ScienceUniversity of ChicagoU.S. Department of Energy
KeywordsMaterials scienceSulfurLithium–sulfur batteryBattery (electricity)Current collectorHost (biology)Lithium (medication)Current (fluid)NanotechnologyElectrical engineeringPower (physics)MetallurgyEngineering

Abstract

fetched live from OpenAlex

Abstract While backless freestanding 3D electrode architectures for batteries with high loading sulfur have flourished in the recent years, the more traditional and industrially turnkey 2D architecture has not received the same amount of attention. This work reports a spray‐dried sulfur composite with large intrinsic internal pores, ensuring adequate local electrolyte availability. This material offers good performance with a electrolyte content of 7 µL mg −1 at high areal loadings (5–8 mg cm −2 ), while also offering the first reported 2.8 µL mg −1 (8 mg cm −2 ) to enter into the second plateau of discharge and continue to operate for 20 cycles. Moreover, evidence is provided that the high‐frequency semicircle (i.e., interfacial resistance) is mainly responsible for the often observed bypassing of the second plateau in lean electrolyte discharges.

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 score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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

Citations92
Published2018
Admission routes2
Has abstractyes

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