† corresponding author, * equal contribution
J. Li†, S. Chen, D. Ratner, T. Blu, P. Pianetta, and Y. Liu†. "Nanoscale chemical
imaging with
structured X-ray
illumination", Proceedings of the National Academy of Sciences of the United States of America (PNAS), 120
(49),
e2314542120 (2023).
Based on structured illumination, we develop a single-pixel X-ray imaging approach coupled with a generative image reconstruction model for mapping the compositional heterogeneity with nanoscale resolvability. We experimentally demonstrate the effectiveness of our approach by imaging a battery sample composed of mixed cathode materials and successfully retrieving the compositional variations of the imaged cathode particles.
J. Li*, N. Sharma*, Z. Jiang, Y. Yang, F. Monaco, Z. Xu, D. Hou, D. Ratner, P. Pianetta, P. Cloetens, F. Lin†, K. Zhao†, Y. Liu†. “Dynamics of particle network in composite battery cathodes”, Science, 376(6592), 517-521 (2022).
Utilizing hard x-ray holotomography and a morphology-informed neural network, we identified how particle behaviors correlate with performance and deterioration. Our insights reveal that both individual particles and their neighbors influence damage over time, guiding better electrode designs.
J. Li, X. Huang, P. Pianetta, and Y. Liu†. “Machine-and-data intelligence for synchrotron science“, Nature Reviews Physics, 3, 766–768 (2021).
Integrated approaches with advanced machine learning techniques are becoming necessary to take full advantage of the advanced experimental capabilities of next-generation synchrotrons. We discuss the emergence of synergistic machine-and-data intelligence in synchrotron technology, and how it may accelerate scientific discovery.
Featured in Pervasive machine learning in physics
Z. Jiang*, J. Li*, Y. Yang*, L. Mu, C. Wei, X. Yu, P. Pianetta, K. Zhao†, P. Cloetens†, F. Lin† and Y. Liu†. “Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes”, Nature Communications, 11, 2310 (2020).
Using a modified Mask-RCNN model, we automatically identified and quantified over 650 NMC particles in high-resolution X-ray nano-tomography. Analysis shows particle detachment increases with charging rate, and smaller particles demonstrate more detachment variability from the carbon/binder matrix.
J. Li*, S. Li*, Y. Zhang*, Y. Yang, S. Russi, G. Qian, L. Mu, S.-J. Lee, Z. Yang, J.-S. Lee, P. Pianetta, J. Qiu, D. Ratner, P. Cloetens†, K. Zhao†, F. Lin†, Y. Liu†. “Multiphase, multiscale chemomechanics at extreme low temperatures: battery electrodes for operation in a wide temperature range”, Advanced Energy Materials, 37, 2102122 (2021).
We investigate the structural deformation and mechanical damage of LIB composite LiNixMnyCozO2 (NMC, x + y + z = 1) cathode upon exposure to low-temperature conditions. Our results suggest that, in order to design batteries for use in a wide temperature range, it is critical to develop electrode components that are structurally and morphologically robust when the cell is switched between different temperatures.
Featured in ESRF Highlights
Front Cover
J. Li†, F. Luisier, T. Blu. “PURE-LET image deconvolution”, IEEE Trans. Image Process., 27(1), 92-105 (2018).
We introduce a non-iterative image deconvolution algorithm tailored for Poisson or mixed Poisson-Gaussian noise. Unlike current methods, ours simply involves solving a linear system, ensuring quick and precise solutions. Simulations across varied convolution kernels and noise intensities show our approach surpasses existing techniques in restoration quality and computational efficiency.
Related work received two best conference paper awards from IEEE Signal Processing Society.
J. Li†, F. Xue, and T. Blu. “Fast and accurate three-dimensional point spread function computation for fluorescence microscopy”, Journal of the Optical Society of America A, 34(6), 1029-1034 (2017).
An accurate PSF model enhances performance in deconvolution microscopy and precision in single-molecule microscopy. We offer a rapid and precise approximation to the Gibson-Lanni model, representing its integral as a combination of rescaled Bessel functions. This eliminates integral-based calculations. Our method notably reduces computation time compared to quadrature methods and is adaptable to other microscopy PSF models.
