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Energy Technology
Article . 2022 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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https://dx.doi.org/10.5445/ir/...
Article . 2022
License: CC BY NC ND
Data sources: Datacite
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(Near‐) Infrared Drying of Lithium‐Ion Battery Electrodes: Influence of Energy Input on Process Speed and Electrode Adhesion

Authors: Andreas Altvater; Thilo Heckmann; Jochen Christoph Eser; Sandro Spiegel; Philip Scharfer; Wilhelm Schabel;

(Near‐) Infrared Drying of Lithium‐Ion Battery Electrodes: Influence of Energy Input on Process Speed and Electrode Adhesion

Abstract

The drying of electrodes represents a critical process step in the production of lithium‐ion batteries. In this process step, unfavorably adjusted drying conditions can result in deteriorated electrode properties. Furthermore, the process speed is restricted by limited heat and mass transfer in purely convective drying. To counteract those effects, energy input by near‐infrared (NIR) radiation is a promising approach. Herein, analytical considerations are carried out to demonstrate the suitability of infrared radiation with regard to achievable electrode temperatures and drying rates. In an experimental approach, aqueous processed graphite anodes are dried with an NIR module, varying the power and the amount of convection for different experiments. The temperature profiles of the electrodes and the drying rates are measured and analyzed, and the electrodes are subsequently characterized using adhesion measurements. The results obtained show that energy input by NIR radiation during the drying of electrodes can lead to an increased drying speed, electrode temperature during drying, and adhesion force of the dry electrode. These findings indicate that the binder distribution during NIR drying is advantageous in terms of electrode adhesion, compared with convectively dried electrodes produced at comparable drying rates, positioning the process promisingly with regard to high throughput rates.

Countries
Germany, Germany
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Keywords

Technology, ddc:600, info:eu-repo/classification/ddc/600, 600

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    17
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    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Average
Top 10%