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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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. 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><subject>3-D printers</subject><subject>3D printing</subject><subject>Anode</subject><subject>Anode effect</subject><subject>Chemical composition</subject><subject>Copper</subject><subject>Dendritic structure</subject><subject>Design of experiments</subject><subject>Design optimization</subject><subject>Electrode polarization</subject><subject>Electrolytes</subject><subject>Inks</subject><subject>Interface stability</subject><subject>Lithium</subject><subject>Lithium batteries</subject><subject>Lithium battery</subject><subject>Machine learning</subject><subject>Photoelectrons</subject><subject>Rheological properties</subject><subject>Scaffolds</subject><subject>Sequential learning</subject><subject>Solid state</subject><subject>Solid-state electrolyte</subject><subject>Statistical analysis</subject><subject>Three dimensional printing</subject><issn>0924-0136</issn><issn>1873-4774</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqFkE9LxDAQxYMouK5-h4DnrkmTJu1R17-w4EXPIU0ma0q3XZNU3G9vlgoehYGB4b03Mz-EMCUrSqi46VbdTqd9GBOYVUlKuqJU0qo5QQtaS1ZwKfkpWpCm5AWhTJyjixg7Qqgkdb1Ant3jffBD8sMWjw7rIddoAUejnRt7i90YcO_Th592uNUpQfAQ8XbyFixuD3jnv9MUAFuIfjsUrY55HuFzghyqe9yDDkNOv0RnTvcRrn77Er0_Prytn4vN69PL-nZTGMZFKqqSctZAyxmx0hFJqBauztcL0ZBWc-fAmLrh3DaSghONaawklrVMO80sY0t0PedmJvmImFQ3TmHIK1VZ8Urwiok6q-pZZcIYYwCnMoWdDgdFiTqCVZ36A6uOYNUMNlvvZivkL748BBWNh8GA9QFMUnb0_4f8AIOih_o</recordid><startdate>202109</startdate><enddate>202109</enddate><creator>Cipollone, Domenic</creator><creator>Yang, Hui</creator><creator>Yang, Feng</creator><creator>Bright, Joeseph</creator><creator>Liu, Botong</creator><creator>Winch, Nicholas</creator><creator>Wu, Nianqiang</creator><creator>Sierros, Konstantinos A.</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>H8D</scope><scope>JG9</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-8888-2444</orcidid><orcidid>https://orcid.org/0000-0002-9017-0809</orcidid><orcidid>https://orcid.org/0000-0003-2986-418X</orcidid></search><sort><creationdate>202109</creationdate><title>3D printing of an anode scaffold for lithium batteries guided by mixture design-based sequential learning</title><author>Cipollone, Domenic ; Yang, Hui ; Yang, Feng ; Bright, Joeseph ; Liu, Botong ; Winch, Nicholas ; Wu, Nianqiang ; Sierros, Konstantinos A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c346t-521439eb430d7f0701a6f89246690ba4ffecc8944d971ef69c9d70d3b3afa3d33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>3-D printers</topic><topic>3D printing</topic><topic>Anode</topic><topic>Anode effect</topic><topic>Chemical composition</topic><topic>Copper</topic><topic>Dendritic structure</topic><topic>Design of experiments</topic><topic>Design optimization</topic><topic>Electrode polarization</topic><topic>Electrolytes</topic><topic>Inks</topic><topic>Interface stability</topic><topic>Lithium</topic><topic>Lithium batteries</topic><topic>Lithium battery</topic><topic>Machine learning</topic><topic>Photoelectrons</topic><topic>Rheological properties</topic><topic>Scaffolds</topic><topic>Sequential learning</topic><topic>Solid state</topic><topic>Solid-state electrolyte</topic><topic>Statistical analysis</topic><topic>Three dimensional printing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><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><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of materials processing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cipollone, Domenic</au><au>Yang, Hui</au><au>Yang, Feng</au><au>Bright, Joeseph</au><au>Liu, Botong</au><au>Winch, Nicholas</au><au>Wu, Nianqiang</au><au>Sierros, Konstantinos A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>3D printing of an anode scaffold for lithium batteries guided by mixture design-based sequential learning</atitle><jtitle>Journal of materials processing technology</jtitle><date>2021-09</date><risdate>2021</risdate><volume>295</volume><spage>117159</spage><pages>117159-</pages><artnum>117159</artnum><issn>0924-0136</issn><eissn>1873-4774</eissn><abstract>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.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.jmatprotec.2021.117159</doi><orcidid>https://orcid.org/0000-0002-8888-2444</orcidid><orcidid>https://orcid.org/0000-0002-9017-0809</orcidid><orcidid>https://orcid.org/0000-0003-2986-418X</orcidid></addata></record> |
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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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