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Conveying Advanced Li‐ion Battery Materials into Practice The Impact of Electrode Slurry Preparation Skills
Abstract
Li‐ion batteries (LIB's) are of the greatest practical utility for portable electronics and electric vehicles (EV's). LIB energy, power and cycle life performances depend on cathode and anode compositions and morphology, electrolyte composition and the overall cell design. Electrode morphology is influenced by the shape and size of the active material (AM), conductive additive (CA) particles, the polymeric binder properties, and also on the AM/CA/binder mass ratio. At the same time, it also substantially depends on the electrode preparation process. This process is usually comprised of mixing a solvent, a binder, AM and CA powders, and casting the resulting slurry onto a current collector foil followed by a drying process. Whereas the problems of electrode morphology and their influence on the LIB‐electrode performance always receive a proper attention, the influence of slurry properties and slurry preparation techniques on the electrode morphology is often overlooked or at least underrated. The present work summarizes the current state‐of‐the‐art in the field of LIB‐electrode precursor slurries preparation, characterized by multicomponent compounds and large variations in sizes and shapes of the solid components. Approaches to LIB‐electrode slurry preparation are outlined and discussed in the context of the ultimate LIB‐electrode morphology and performance.
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Metadata
- Authors
- Kraytsberg, A., & Ein‐Eli, Y.
- Venue
- Advanced Energy Materials
- Publisher
- Wiley
- Published
- 2016-07-29
- Volume
- 6
- Issue
- 21
- ISSN
- 1614-6832 (ISSN-L)
- 1614-6840
- OpenAlex
- W2491409348
- Cited by
- 299
- References
- 146
- FWCI
- 7.62
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
