journal article·2021-09Open access

Revealing meso-structure dynamics in additive manufacturing of energy storage via operando coherent X-ray scattering

Cheng-Hung Lin, Karol Dyro, Olivia Chen, Dean Yen, Bingqian Zheng, Maria Torres Arango, Surita Bhatia, Ke Sun, Qingkun Meng, Lutz Wiegart, Yu-chen Karen Chen-Wiegart

Abstract

Abstract 3D printing is an emerging technology for the fabrication of energy storage devices, offering advantages over traditional manufacturing methods. However, optimization and design of such devices requires an understanding of the meso‑structure formation during the 3D printing process. This study utilizes operando coherent X-ray scattering, X-ray Photon Correlation Spectroscopy (XPCS), to study the spatiotemporally-resolved far-from-equilibrium dynamics during direct ink writing 3D printing. Lithium Titanate (LTO) based ink is prepared and rheologically tested for its shear-thinning properties. Two-time intensity-intensity functions are calculated to be used in subsequent quantitative analysis, which allows for an overall characterization of the dynamics, description of an initial fast decorrelation and identification of sudden rearrangements of subdomains of the sample. The results show the dynamics to be anisotropic, spatiotemporally heterogenous and marked by distinct rearrangement events, all of which impact the electrochemical performance of energy storage devices. The studied 3D printing ink is used to fabricate electrodes which are then electrochemically tested, showing good performance in cycling and retaining structural integrity. This work furthers the understanding of the far-from-equilibrium material dynamics during 3D printing, giving quantitative characterization of this process, and highlights aspects of structure formation relevant to the electrochemical performance of the resultant energy storage device.
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Research context

OpenAlex topics: Advanced Electron Microscopy Techniques and Applications · Machine Learning in Materials Science · Transition Metal Oxide Nanomaterials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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