3D printing of an anode scaffold for lithium batteries guided by mixture design-based sequential learning

The application of safe, high energy-density solid-state lithium (Li) metal batteries is hindered by dendrite growth, poor interfacial contact, and side reactions between the Li metal and the solid-state ceramic electrolyte. The use of a three-dimensional (3D) porous copper (Cu) scaffold has shown t...

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Veröffentlicht in:Journal of materials processing technology 2021-09, Vol.295, p.117159, Article 117159
Hauptverfasser: Cipollone, Domenic, Yang, Hui, Yang, Feng, Bright, Joeseph, Liu, Botong, Winch, Nicholas, Wu, Nianqiang, Sierros, Konstantinos A.
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container_issue
container_start_page 117159
container_title Journal of materials processing technology
container_volume 295
creator Cipollone, Domenic
Yang, Hui
Yang, Feng
Bright, Joeseph
Liu, Botong
Winch, Nicholas
Wu, Nianqiang
Sierros, Konstantinos A.
description The application of safe, high energy-density solid-state lithium (Li) metal batteries is hindered by dendrite growth, poor interfacial contact, and side reactions between the Li metal and the solid-state ceramic electrolyte. The use of a three-dimensional (3D) porous copper (Cu) scaffold has shown to be an effective solution to enabling a Li metal anode. However, it is difficult to fabricate such 3D structures rapidly and controllably. Herein, a 3D printing approach has been developed to fabricate a 3D anode structure with controlled dimension, geometry, and chemical composition. In addition, mixture design-based sequential learning is used to guide design and optimization of the printing ink formula as well as the rheological and operating parameters of the 3D printing process. Inks are patterned directly onto the NASICON-type Li1+xAlx3+M2−x4+(PO4)3 (LATP) electrolyte, yielding scaffolds with a range of pore sizes. The printed scaffolds and the electrode-electrolyte interface are characterized using symmetric cell cycling, X-ray photoelectron spectroscopy, and scanning electron microscopy. The characterization results show that the 3D printed structure benefits both interfacial stability and the suppression of lithium dendrite growth. The Li|Cu@LATP@Cu|Li symmetrical cell with a 3D printed Cu scaffold exhibits a polarization voltage of 60 mV at a current density of 0.05 mA/cm2. This work shows that machine learning based on experimental design and statistical analysis leads to reduced experimental effort in optimizing the 3D printing process.
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The use of a three-dimensional (3D) porous copper (Cu) scaffold has shown to be an effective solution to enabling a Li metal anode. However, it is difficult to fabricate such 3D structures rapidly and controllably. Herein, a 3D printing approach has been developed to fabricate a 3D anode structure with controlled dimension, geometry, and chemical composition. In addition, mixture design-based sequential learning is used to guide design and optimization of the printing ink formula as well as the rheological and operating parameters of the 3D printing process. Inks are patterned directly onto the NASICON-type Li1+xAlx3+M2−x4+(PO4)3 (LATP) electrolyte, yielding scaffolds with a range of pore sizes. The printed scaffolds and the electrode-electrolyte interface are characterized using symmetric cell cycling, X-ray photoelectron spectroscopy, and scanning electron microscopy. The characterization results show that the 3D printed structure benefits both interfacial stability and the suppression of lithium dendrite growth. The Li|Cu@LATP@Cu|Li symmetrical cell with a 3D printed Cu scaffold exhibits a polarization voltage of 60 mV at a current density of 0.05 mA/cm2. This work shows that machine learning based on experimental design and statistical analysis leads to reduced experimental effort in optimizing the 3D printing process.</description><identifier>ISSN: 0924-0136</identifier><identifier>EISSN: 1873-4774</identifier><identifier>DOI: 10.1016/j.jmatprotec.2021.117159</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>3-D printers ; 3D printing ; Anode ; Anode effect ; Chemical composition ; Copper ; Dendritic structure ; Design of experiments ; Design optimization ; Electrode polarization ; Electrolytes ; Inks ; Interface stability ; Lithium ; Lithium batteries ; Lithium battery ; Machine learning ; Photoelectrons ; Rheological properties ; Scaffolds ; Sequential learning ; Solid state ; Solid-state electrolyte ; Statistical analysis ; Three dimensional printing</subject><ispartof>Journal of materials processing technology, 2021-09, Vol.295, p.117159, Article 117159</ispartof><rights>2021 Elsevier B.V.</rights><rights>Copyright Elsevier BV Sep 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c346t-521439eb430d7f0701a6f89246690ba4ffecc8944d971ef69c9d70d3b3afa3d33</citedby><cites>FETCH-LOGICAL-c346t-521439eb430d7f0701a6f89246690ba4ffecc8944d971ef69c9d70d3b3afa3d33</cites><orcidid>0000-0002-8888-2444 ; 0000-0002-9017-0809 ; 0000-0003-2986-418X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0924013621001199$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27903,27904,65309</link.rule.ids></links><search><creatorcontrib>Cipollone, Domenic</creatorcontrib><creatorcontrib>Yang, Hui</creatorcontrib><creatorcontrib>Yang, Feng</creatorcontrib><creatorcontrib>Bright, Joeseph</creatorcontrib><creatorcontrib>Liu, Botong</creatorcontrib><creatorcontrib>Winch, Nicholas</creatorcontrib><creatorcontrib>Wu, Nianqiang</creatorcontrib><creatorcontrib>Sierros, Konstantinos A.</creatorcontrib><title>3D printing of an anode scaffold for lithium batteries guided by mixture design-based sequential learning</title><title>Journal of materials processing technology</title><description>The application of safe, high energy-density solid-state lithium (Li) metal batteries is hindered by dendrite growth, poor interfacial contact, and side reactions between the Li metal and the solid-state ceramic electrolyte. The use of a three-dimensional (3D) porous copper (Cu) scaffold has shown to be an effective solution to enabling a Li metal anode. However, it is difficult to fabricate such 3D structures rapidly and controllably. Herein, a 3D printing approach has been developed to fabricate a 3D anode structure with controlled dimension, geometry, and chemical composition. In addition, mixture design-based sequential learning is used to guide design and optimization of the printing ink formula as well as the rheological and operating parameters of the 3D printing process. Inks are patterned directly onto the NASICON-type Li1+xAlx3+M2−x4+(PO4)3 (LATP) electrolyte, yielding scaffolds with a range of pore sizes. The printed scaffolds and the electrode-electrolyte interface are characterized using symmetric cell cycling, X-ray photoelectron spectroscopy, and scanning electron microscopy. 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subjects 3-D printers
3D printing
Anode
Anode effect
Chemical composition
Copper
Dendritic structure
Design of experiments
Design optimization
Electrode polarization
Electrolytes
Inks
Interface stability
Lithium
Lithium batteries
Lithium battery
Machine learning
Photoelectrons
Rheological properties
Scaffolds
Sequential learning
Solid state
Solid-state electrolyte
Statistical analysis
Three dimensional printing
title 3D printing of an anode scaffold for lithium batteries guided by mixture design-based sequential learning
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