Seojoung Park

Achieving a comprehensive understanding of battery systems necessitates multi-length scale analysis, from the atomic- to macro-scale, to grasp the complex interplay of phenomena influencing performance. However, studies to understand these phenomena in all-solid-state batteries (ASSBs) poses significant challenges due to the complex microstructural evolution involved, including the pore formation and contact loss resulting from cathode material breathing, chemical degradation at interfaces, and their interplay. Herein, we investigate the impact of chemical degradation on the reaction behavior and microstructural evolution of Ni-rich cathode particle (LiNi_0.6Co_0.2Mn_0.2O₂) within composite cathodes of sulfide-based ASSBs, using a well-defined model system incorporating Li-In alloy anodes and a non-decomposable coating layer that solely alters the interfacial chemical reactivity. By using lithium difluorophosphate (LiDFP) to suppress chemical degradation, we observed that this suppression enhances the reaction uniformity among particles and homogenizes mechanical degradation, albeit increasing pore formation and tortuosity. In addition, unbridled chemical degradation induces significant reaction heterogeneity and non-uniform mechanical degradation, with fewer pores and lower tortuosity. These findings complement the understanding of mechanical degradation, which is traditionally described using the metrics of contact loss and tortuosity, and underscore the critical role of coating layers in promoting lithium conduction by maintaining contact with the cathode surface.

Achieving a comprehensive understanding of battery systems necessitates multi-length scale analysis, spanning from the atomic to macro scale, to grasp the complex interplay of phenomena influencing performance. However, studies to understand these phenomena in all-solid-state batteries (ASSBs) poses significant challenges due to the complex microstructural evolution involved, such as the pore formation and contact loss resulting from cathode material breathing, chemical degradation at interfaces, and their interplay. Herein, we investigate the impact of chemical degradation at the cathode/solid-electrolyte interface on the cathode particle reaction behavior and microstructural evolution in composite cathodes of sulfide-based ASSBs, using a well-defined model system incorporating a non-decomposable coating layer that solely alters the interfacial chemical reactivity. By using lithium difluorophosphate (LiDFP) to suppress chemical degradation, we observed that this suppression enhances the reaction uniformity among particles and homogenizes mechanical degradation, albeit increasing pore formation and tortuosity. In addition, unbridled chemical degradation induces significant reaction heterogeneity and non-uniform mechanical degradation, with fewer pores and lower tortuosity. These findings complement the understanding of mechanical degradation, which is traditionally described using the metrics of contact loss and tortuosity, and underscore the critical role of coating layers in promoting lithium conduction by maintaining contact with the cathode surface. This research not only offers novel insights into the intertwined chemo-mechanical degradation and the functionality of the coating material but also paves the way for the multi-length scale understanding required for the development of advanced ASSBs.

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

As the electric vehicle market continues to surge, the proper assessment of used batteries has become increasingly important. However, current technologies for assessing used batteries, which involve separately estimating the State-of-Health (SoH) of the pack and its individual modules, require multiple times of cycling tests and lead to time inefficiency and power consumption. The proposed DeepSUGAR, a deep learning-based framework for SoH estimation using a generative algorithm based on graphical representation techniques to reveal individual module health, offers the advantage of estimating the status of internal modules replying on battery pack SoH. The cycling profiles of a simultaneously measured 14S7P pack and its constituent modules were analyzed, and a convolutional neural network (CNN) was trained by spatializing cycling curves to estimate SoH. DeepSUGAR, trained on pack data, showed outstanding performance with an RMSE of 5.31 × 10⁻³ and its applicability was validated by testing with module data, resulting in an RMSE of 7.38 × 10⁻³. Furthermore, the generated module cycling profiles from pack SoH using the deep generative model were fed into the trained CNN and showed a remarkable performance with an RMSE of 8.38 × 10⁻³. DeepSUGAR can significantly reduce power consumption, processing cost, and carbon dioxide emissions by integrating module-level diagnosis within the pack-level assessment process.

Accurately estimating the state-of-health (SOH) of lithium-ion batteries is emerging as a hot topic because of the rapid increase in electric appliance usage. However, versatile applicability to various battery compositions and diverse cycling conditions, and prediction only with partial data still remain challenges. In this paper, a Deep-learning-based Graphical approach to Estimation of Lithium-ion batteries SOH (D-GELS) was developed to predict the SOH covering three cathode materials, LiFePO₄, LiNiCoAlO₂, and LiNiCOMnO₂. D-GELS shows an accurate performance for SOH prediction, less than 0.012 of RMSE, was predicted regardless of cathode materials, and its applicability was confirmed. Furthermore, D-GELS was capable of predicting the SOH using partially-cycled data, since less than 0.046 of RMSE was observed even with 50% of the image missing. When using partially-cycled profiles, significant economic benefits can be seen in used battery management, as the number of assessed batteries increases greatly, leading to cost savings.