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Record W2309673495 · doi:10.1149/ma2014-02/5/352

Very High Surface Capacity with Si Negative Electrodes Embedded in Copper Foam as 3D Current Collector

2014· article· en· W2309673495 on OpenAlexaffabout
Driss Mazouzi, David Reyter, Magali Gauthier, Dominique Guyomard, Lionel Roué, Bernard Lestriez

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsGraphiteElectrodeMaterials scienceGravimetric analysisCurrent collectorLithium (medication)SiliconComposite materialNanotechnologyLithium-ion batteryCopperChemical engineeringBattery (electricity)OptoelectronicsMetallurgyChemistryElectrolyteOrganic chemistry

Abstract

fetched live from OpenAlex

For the last 10 years, a tremendous amount of work has been published to solve the problem of capacity fade of silicon-based electrodes which prevents their utilization in commercial lithium-ion batteries. The use of Si nanoparticles/nanowires to better accommodate large strain without cracking has developed and is very popular in the academic community. By playing on the nano-architecturing effect or tailoring the composite electrode formulation, several groups have reached up to 1000 cycles in half-cells versus lithium metal [1,2]. However, a careful look at the papers shows that in all studies the active mass loading is very low, typically less than 1 mg per cm², and thus the practical surface capacity of the corresponding electrodes is low, typically less than 1 mAh per cm². This is much lower than that of the state of the art graphite-based negative electrode, which reaches up to 5 mAh per cm² in cellular phones for example. As a consequence, although silicon is much more attractive than graphite due to its very high gravimetric capacity (3572 mAh g-1 versus 372 mAh g-1 for graphite) and volumetric capacity (2249 versus 779 mAh cm-3 for graphite), Si-based composite electrodes show lower practical surface capacity, as a matter of fact. The point is that the cycle life of Si-based electrodes dramatically decreases as the active mass loading increases, e.g. 1000 cycles at 0.5 mg per cm² vs. 50 cycles at 4 mg per cm² (Figure 1). We demonstrated that using copper foam instead of copper foil as current collector shows a great advantage in the cycle life and power performance. More than 400 cycles at an impressive Si loading of 10 mg cm-² could be reached, i.e. with a surface capacity of 10 mAh cm-2 [3]. The thinness of the composite coating on the foam walls favors a better preservation of the electronic wiring upon cycling and fast lithium ion diffusion. A higher coulombic efficiency in half cells with lithium metal as the counter electrode is achieved by using carbon nanofibers (CNF) rather than carbon black (CB). The possibility to reach in practice higher surface could allow a significant increase of both the volumetric and gravimetric energy densities by 23% and 19%, respectively, for the Cu foam-Silicon//LiFePO4 stack compared to the Graphite/LiFePO4 stack of traditional design. Acknowledgements Financial funding from the Agence Nationale de la Recherche (ANR) of France (BASILIC project) and the Natural Science and Engineering Research Council (NSERC) of Canada is acknowledged. The authors thank D. Pilon (Metafoam Inc.) for supplying the Cu foams. References [1] L. Hu, F. La Mantia, H. Wu, X. Xie, J. McDonough, M. Pasta, Y. Cui, Adv. Energy Mater., 1, 1012 (2011). [2] I. Kovalenko, B. Zdyrko, A. Magasinski, B. Hertzberg, Z. Milicev, R. Burtovyy, I. Luzinov and G. Yushin, Science, 334, 75 (2011). [3] D. Mazouzi, , D. Reyter, M. Gauthier, P. Moreau, D. Guyomard, L. Roué, B. Lestriez, Adv. Energy Mater., DOI: 10.1002/aenm.201301718. Figure 1. (a) Surface SEM images of a Cu foam filled with 5 mg of Si/CNF/CMC/Buffer composite electrode (4.6 mg Si per cm2). (b) Cycle life as a function of the active mass loading for Foil-Si/CB/CMC/Buffer and Foam-Si/CNF/CMC/Buffer electrodes (Si//Li half-cell with LP30+2%VC+10%FEC, capacity limitation of 1200mAh per g of Si).

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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
Published2014
Admission routes2
Has abstractyes

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