Review Articles
Review Articles#
† indicates corresponding author, * indicates equal contribution
B. Su, J. Li†, B. Deng†, P. Pianetta, and Y. Liu†. “Multi-modal X-ray microscopy for chemical analysis”, TrAC Trends in Analytical Chemistry, doi: 10.1016/j.trac.2023.117491 (2023).
The rapid developments in X-ray instrumentation and the
associated analytical methods have demonstrated a tremendous impact on various fundamental and applied sciences. We
present a systematic overview of the state-of-the-art
multimodal X-ray imaging
technique for probing the structural complexity and chemical heterogeneity of real-world samples.
Z. Xue, J. Li, P. Pianetta, and Y. Liu†. “Data-driven lithium-ion battery cathode research with state-of-the-art synchrotron X-ray techniques”, Accounts of Materials Research, 3 (8), 854–865 (2022).
Combining advanced synchrotron X-ray techniques with machine
learning (ML) methods has been demonstrated as a powerful tool for uncovering the fundamental reaction and aging mechanisms in LIB and is emerging as an important research frontier. We showcase the integration of synchrotron and ML
techniques for LIB cathode research. We review our recent findings on charge–lattice–morphology–kinetics in LIB cathode materials via this approach.
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.
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