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

Abstract

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\u2009mV at a current density of 0.05\u2009mA/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.