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Record W2437736150 · doi:10.1021/acsnano.5b05068

A Foldable Lithium–Sulfur Battery

2015· article· en· W2437736150 on OpenAlexafffund
Lu Li, Zi Ping Wu, Hao Sun, Deming Chen, Jian Gao, Shravan Suresh, Philippe K. Chow, Chandra Veer Singh, Nikhil Koratkar

Bibliographic record

VenueACS Nano · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Toronto
FundersDivision of Civil, Mechanical and Manufacturing InnovationMinistry of Trade, Industry and EnergyNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaRensselaer Polytechnic Institute
KeywordsConformable matrixBattery (electricity)Carbon nanotubeMaterials scienceCurrent collectorEnergy storageElectronicsNanotechnologyLithium (medication)Lithium–sulfur batteryCarbon fibersElectrical engineeringPower (physics)Composite materialEngineering

Abstract

fetched live from OpenAlex

The next generation of deformable and shape-conformable electronics devices will need to be powered by batteries that are not only flexible but also foldable. Here we report a foldable lithium-sulfur (Li-S) rechargeable battery, with the highest areal capacity (∼3 mAh cm(-2)) reported to date among all types of foldable energy-storage devices. The key to this result lies in the use of fully foldable and superelastic carbon nanotube current-collector films and impregnation of the active materials (S and Li) into the current-collectors in a checkerboard pattern, enabling the battery to be folded along two mutually orthogonal directions. The carbon nanotube films also serve as the sulfur entrapment layer in the Li-S battery. The foldable battery showed <12% loss in specific capacity over 100 continuous folding and unfolding cycles. Such shape-conformable Li-S batteries with significantly greater energy density than traditional lithium-ion batteries could power the flexible and foldable devices of the future including laptops, cell phones, tablet computers, surgical tools, and implantable biomedical devices.

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 score0.079
Threshold uncertainty score0.371

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 head0.020
GPT teacher head0.215
Teacher spread0.195 · 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

Citations138
Published2015
Admission routes2
Has abstractyes

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