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Record W4285398563 · doi:10.1149/ma2022-01552263mtgabs

A Low-Cost and Green Si-Based Anode Material for Lithium-Ion Batteries

2022· article· en· W4285398563 on OpenAlexaff
Alexandre Heitz, Victor Vanpeene, Natalie Herkendaal, Patrick Soucy, Thierry Douillard, Lionel Roué

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMaterials scienceAnodeElectrodeNanocrystalline materialAmorphous solidMicrostructureLithium-ion batteryIonChemical engineeringComposite materialNanotechnologyBattery (electricity)MetallurgyChemistry

Abstract

fetched live from OpenAlex

The conception of cheaper and greener electrode materials is critical for Li-ion battery manufacturers. In this study, it is shown that a by-product of the carbothermic reduction of SiO2 to Si, containing Si, SiC and C materials, can be valorized as a low-cost and high-capacity anode material for Li-ion batteries after an appropriate high-energy ball milling treatment. The latter results in the production of a micrometric powder (D50 ~1 mm) in which submicrometric SiC inclusions are embedded in a nanocrystalline/amorphous Si matrix. Such a microstructure prevents the deleterious formation of c-Li15Si4 phase, which is well known to accentuate particle cracking. As a result, the electrode is able to maintain a capacity >1000 mAh g-1 (>3 mAh cm-2) over 100 cycles. Moreover, calendering has no negative impact on the electrode performance. However, a significant and irreversible increase of the electrode mass and thickness was observed over cycling, which is mainly attributed to the accumulation of SEI products. In order to have deeper insights into the microstructural evolution of the electrode during cycling, a focused ion beam (FIB) milled microcavity (45×20×50 µm3) was created in the center of the pristine electrode. This cavity was observed by SEM at different cycling periods of a single electrode (Fig. 1a). This investigation method allows following the same electrode along different steps of its cycling, nearly as for an in-situ method. Additionally, backscattered-electron (BSE) imaging was performed on broad ion beam (BIB) polished cross-section of the electrode after different periods of cycling (Fig. 1b). The morphological change is characterized at the electrode and particle scales by monitoring the thickness, mass, porosity and macrocracking of the electrode, SEI layer thickness and particle morphology. On the basis of these investigations, a more comprehensive view of the degradation phenomena of the electrode is established. Figure 1

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 science0.0000.000
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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