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Record W2317116120 · doi:10.1021/nl403943g

Engineered Si Electrode Nanoarchitecture: A Scalable Postfabrication Treatment for the Production of Next-Generation Li-Ion Batteries

2013· article· en· W2317116120 on OpenAlexaff
Fathy M. Hassan, Victor Chabot, Abdel Rahman Elsayed, Xingcheng Xiao, Zhongwei Chen

Bibliographic record

VenueNano Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectrodeMaterials scienceNanoarchitectures for lithium-ion batteriesIonNanotechnologyAnodeChemistry

Abstract

fetched live from OpenAlex

A novel, economical flash heat treatment of the fabricated silicon based electrodes is introduced to boost the performance and cycle capability of Li-ion batteries. The treatment reveals a high mass fraction of Si, improved interfacial contact, synergistic SiO2/C coating, and a conductive cellular network for improved conductivity, as well as flexibility for stress compensation. The enhanced electrodes achieve a first cycle efficiency of ∼84% and a maximum charge capacity of 3525 mA h g(-1), almost 84% of silicon's theoretical maximum. Further, a stable reversible charge capacity of 1150 mA h g(-1) at 1.2 A g(-1) can be achieved over 500 cycles. Thus, the flash heat treatment method introduces a promising avenue for the production of industrially viable, next-generation Li-ion 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)0.000
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 score0.050
Threshold uncertainty score0.510

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 head0.019
GPT teacher head0.221
Teacher spread0.202 · 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

Citations129
Published2013
Admission routes1
Has abstractyes

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