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Record W2591051206 · doi:10.1149/ma2017-01/5/383

A Comprehensive Study of a Film Maturation Process for Improving the Cycle Life of Silicon-Based Anodes

2017· article· en· W2591051206 on OpenAlexaff
Cuauhtémoc Reale Hernandez, Zouina Karkar, Alix Tranchot, Aurélien Etiemble, Éric Maire, Dominique Guyomard, Bernard Lestriez, Lionel Roué

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSiliconAnodeGraphiteMaterials scienceInterphaseElectrolyteNanotechnologyVolume expansionEngineering physicsElectrodeComposite materialOptoelectronicsEngineeringChemistry

Abstract

fetched live from OpenAlex

Increasing the energy density of Li-ion batteries (LiB) is a key issue for transport applications. A promising approach is to replace the currently used graphite in LIB anodes with silicon since its specific capacity is ten times higher than that of graphite. The main challenge is to deal with the large silicon volume expansion induced by its lithiation, which damages the mechanical integrity (electronic network) of the electrode, and produces an unstable solid electrolyte interphase (SEI). In last years, our group has successfully improved the performance of silicon based anodes by working on various aspects1. First, our ball-milled silicon offers the right nanostructure to limit Si particle cracking in addition to be produced using an industrially viable process. Second, the use of an acid buffer solution to prepare the electrode ink favors the formation of resilient covalent bonds between the carboxymethylcellulose (CMC) binder and the Si particles. Third, adding fluoroethylene carbonate (FEC) to the electrolyte limits the SEI growth. Lastly, the use of carbon nanoplatelets instead of carbon black as conductive additive insures more durable electronic network, especially for Si-based electrodes with high areal capacities2. Recently, we have developed a film maturation process that further improves the performance of our silicon-based anodes3. This simple process consists in storing the electrode in a humid atmosphere for a few days before drying and cell assembling. As seen in Fig.1, this results in an impressive improvement of the electrode cycle life. Such a cycling performance is remarkable considering the high Si areal mass loading of the electrode (2 mg Si cm-2). In this work, we present a comprehensive study that aims at explaining how the maturation process works. On one hand, we have studied the effect of the maturation process on the mechanical properties of our Si-based electrodes using indentation measurement, peeling test, scratch test, in-operando dilatometry and in-operando optical microscopy. Those various experiments confirm that the film adhesion and cohesion strengths are reinforced by the maturation step. On the other hand, we have studied the effect of maturation on the electrochemical performance of various electrode formulations. These results show that maturation works with different types of silicon, binder and conductive agent. They also suggest that the nature of the substrate and the acidic environment play a crucial role in the maturation process. Furthermore, reflectance Fourier transform infrared spectroscopy (ATR - FTIR) and Nuclear Magnetic Resonance (NMR) analyses have been performed in order to evaluate the impact of the maturation step on the chemical bonds between the Si particles and the CMC binder and with the Cu substrate. ). Lastly, focused ion beam scanning electron microscopy (FIB-SEM) tomography shows a better preservation with cycling of the pore and Si particle connectivities for the matured electrode. From the conjunction of these different experiments, a film maturation mechanism is proposed. References M. Gauthier, D. Mazouzi, D. Reyter, B. Lestriez, P. Moreau, B. Lestriez, D. Guyomard, L. Roué. A low-cost and high-performance Si-based electrode for Li-ion batteries. Energy Environ. Sci. 6 (2013) 2145–2155. Z. Karkar, D. Mazouzi, C. Reale Hernandez, D. Guyomard, L. Roué, B. Lestriez. Threshold-like dependence of silicon-based electrode performance on active mass loading and nature of carbon conductive additive. Electrochim. Acta 215 (2016) 276-288. C. Real Hernandez, Z. Karkar, D. Guyomard, B. Lestriez, L. Roué. A film maturation process for improving the cycle life of Si-based anodes for Li-ion batteries. Electrochem. Comm. 61 (2015) 102-105. Figure 1

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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 score0.002

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 communication0.0000.001
Open science0.0000.000
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 head0.028
GPT teacher head0.290
Teacher spread0.262 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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Same venueECS Meeting Abstracts→Same topicAdvancements in Battery Materials→French-language works237,207→