Analysis of Deposition Methods for Lithium-Ion Battery Anodes Using Reduced Graphene Oxide Slurries on Copper FoilSource: Journal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 009::page 94501DOI: 10.1115/1.4040265Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Graphene oxide (GO) slurries were deposited onto copper foil for use in lithium-ion battery anodes to determine the best deposition method(s) for research or high-volume manufacturing. Four deposition methods were tested: doctor blade, Mayer rod, slot die, and low volume low pressure (LVLP) spray. Analytical models that link tooling and process characteristics to mass flow rate of slurry and the resulting dry deposition height are developed and validated experimentally. While all methods successfully produced functioning batteries, a number of different qualitative and quantitative metrics from experimental results identified the best method for both situations. Observations were recorded on adhesion, deposition consistency, usability, and cleanability. Data on specific discharge capacity were recorded to show performance over the anode lifetime and at different charge/discharge rates. The data indicate that anodes produced using reduced graphene oxide (rGO) deliver a specific charge storage capacity of 50 to 400 mAh/g at charge–discharge rates of 1 C to 0.05 C. Doctor blading proved to be best for laboratory setups because of its adjustability, while the Mayer rod shows promise for high-volume manufacturing due to better performance and the use of nonadjustable, dedicated tooling. All methods, analysis, and metrics are discussed.
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| contributor author | Garofalo, James | |
| contributor author | Lawler, John | |
| contributor author | Walczyk, Daniel | |
| contributor author | Koratkar, Nikhil | |
| date accessioned | 2019-02-28T11:02:09Z | |
| date available | 2019-02-28T11:02:09Z | |
| date copyright | 6/28/2018 12:00:00 AM | |
| date issued | 2018 | |
| identifier issn | 1087-1357 | |
| identifier other | manu_140_09_094501.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4251954 | |
| description abstract | Graphene oxide (GO) slurries were deposited onto copper foil for use in lithium-ion battery anodes to determine the best deposition method(s) for research or high-volume manufacturing. Four deposition methods were tested: doctor blade, Mayer rod, slot die, and low volume low pressure (LVLP) spray. Analytical models that link tooling and process characteristics to mass flow rate of slurry and the resulting dry deposition height are developed and validated experimentally. While all methods successfully produced functioning batteries, a number of different qualitative and quantitative metrics from experimental results identified the best method for both situations. Observations were recorded on adhesion, deposition consistency, usability, and cleanability. Data on specific discharge capacity were recorded to show performance over the anode lifetime and at different charge/discharge rates. The data indicate that anodes produced using reduced graphene oxide (rGO) deliver a specific charge storage capacity of 50 to 400 mAh/g at charge–discharge rates of 1 C to 0.05 C. Doctor blading proved to be best for laboratory setups because of its adjustability, while the Mayer rod shows promise for high-volume manufacturing due to better performance and the use of nonadjustable, dedicated tooling. All methods, analysis, and metrics are discussed. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Analysis of Deposition Methods for Lithium-Ion Battery Anodes Using Reduced Graphene Oxide Slurries on Copper Foil | |
| type | Journal Paper | |
| journal volume | 140 | |
| journal issue | 9 | |
| journal title | Journal of Manufacturing Science and Engineering | |
| identifier doi | 10.1115/1.4040265 | |
| journal fristpage | 94501 | |
| journal lastpage | 094501-9 | |
| tree | Journal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 009 | |
| contenttype | Fulltext |