MétaCan
Menu
Back to cohort
Record W2303684518 · doi:10.1149/ma2016-03/2/118

Study of the Expansion/Contraction Behavior of Si-Based Electrodes By Electrochemical Dilatometry

2016· article· en· W2303684518 on OpenAlexaff
Alix Tranchot, Pierre‐Xavier Thivel, Hassane Idrissi, Lionel Roué

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor materials and interfaces
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMaterials scienceElectrodeElectrolyteElectrochemistryFaraday efficiencyGraphiteComposite materialSiliconThermal expansionAnalytical Chemistry (journal)Chemical engineeringOptoelectronicsChemistryChromatography

Abstract

fetched live from OpenAlex

The replacement of graphite by silicon as the active material in negative electrodes of Li-ion batteries is very attractive since the specific capacity of Si is 10 times higher than that of graphite. However, Si suffers from huge volume variation (up to ~300% vs 10% for C) during its lithiation. This leads to the electrode/particle cracking which induces electrical disconnections in addition to cause an instability of the solid electrolyte interface (SEI), resulting in poor cycle life and low coulombic efficiency. A precise evaluation of the expansion/contraction behavior of the Si-based electrodes upon cycling is thus highly relevant to develop more efficient electrode formulations. For this purpose, a simple and efficient method consists of integrating a contact displacement transducer to the electrochemical cell, which permits a measurement of any thickness change of the working electrode upon its charge and discharge as shown hereafter. In the present study, electrochemical dilatometry experiments are performed on silicon/carbon/carboxymethylcellulose (Si/C/CMC) composite electrodes. It is shown that the pH of the slurry (pH 7 vs. buffered pH 3) and the size of the Si particles (1-5 µm vs. 85 nm) have a major impact on their electrochemical performance ( Fig. 1 ) and their expansion/contraction behavior ( Fig. 2a-c ). During the first discharge (lithiation), a maximum electrode thickness expansion of ~130% is observed for the pH3-micro Si electrode ( Fig. 2b ). compared to ~330% for the pH7-micro Si electrode. ( Fig. 2a ). A lower irreversible expansion is also observed at the end of the 1 st cycle (~50% compared to ~180% for the pH7 electrode). It can be explained by the the formation of more cohesive (covalent) bonds between the Si particles and the CMC chains when the electrode is prepared with a slurry buffered at pH 3 , increasing the mechanical strength of the composite electrode and its ability to reversibly sustain the volume variations of the silicon particles. 1,2 However,when the 1-5µm Si is replaced by 85 nm Si, a very large and abrupt expansion/contraction of the electrode is observed (up to 500%, see Fig. 2c ), resulting in a significant decrease of the electrode cycle life ( Fig. 1 ). It may be related to the higher specific surface area and/or lower surface oxidation state of the nanosized Si powder, inducing a less efficient grafting of the CMC binder at the surface of the Si particles. 3 Additionaldilatometric experiments with oxidized Si nanoparticles and with different CMC amounts will be presented to address this issue. 1. D. Mazouzi, B. Lestriez, L. Roué, D. Guyomard. Silicon composite electrode with high capacity and long cycle life . Electrochem. Solid State Let. 12 (2009) A215-A2182. 2. A. Tranchot, P-X. Thivel, H. Idrissi, L. Roué. Impact of the slurry pH on the expansion/contraction behavior of silicon/carbon/carboxymethylcellulose electrodes for Li-ion batteries . J. Electrochem. Soc., submitted. 3. N. Delpuech, D. Mazouzi, N. Dupré, P. Moreau, M. Cerbelaud, J. S. Bridel, J.-C. Badot, E. De Vito, D. Guyomard, B. Lestriez, and B. Humbert. Critical Role of Silicon Nanoparticles Surface on Lithium Cell Electrochemical Performance Analyzed by FTIR, Raman, EELS, XPS, NMR, and BDS Spectroscopies . J. Phys. Chem. C 118 (2014) 17318−17331. 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 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 score0.037
Threshold uncertainty score0.303

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicSemiconductor materials and interfacesFrench-language works237,207