Very High Surface Capacity with Si Negative Electrodes Embedded in Copper Foam as 3D Current Collector
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".