News & Updates
Latest updates, announcements, and achievements from our group
Article published in Patterns
We presents a data-driven solution for X-ray spectro-tomography that bypasses the need for manual markers or extensive training datasets, offering a robust, automated path to high-fidelity chemical imaging.
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Commentary published in Photon Science
We discuss the transformative potential of Agentic AI, an emerging paradigm that automates entire research workflows to revolutionize material characterization in synchrotron science.
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Article published in IJCV
We propose a novel continuous low-rank factorization (CLoRF) by integrating two neural representations into the matrix factorization, capturing spatial and spectral information, respectively.
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Article published in ACS Nano
By employing super-resolved nanoscale X-ray computed tomography (Nano-CT), scanning probe nanodiffraction imaging (SPNDI), and advanced data-driven statistical analysis, we unveil the ubiquitous presence of nanoscale domain boundaries within micrometer-sized LiCoO2 single crystals, which act as primary hotspots for strain accumulation and microcrack formation during cycling.
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Article published in Cell
We describe two complementary TEMPO (transmembrane electrical measurements performed optically) voltage-sensing technologies that capture neural oscillations up to 100 Hz. Fiber-optic TEMPO achieves 10-fold greater sensitivity than prior photometric voltage sensing, allows hour-long recordings, and monitors two neuron classes per fiber-optic probe in freely moving mice.
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Article published in IEEE TBME
We propose a Lorentzian-model Informed Neural Representation (LINR) framework for high-quality CEST mapping. LINR employs a self-supervised neural architecture embedding the Lorentzian equation, the fundamental biophysical model of CEST signal evolution, to directly reconstruct high-sensitivity parameter maps from raw z-spectra, eliminating dependency on labeled training data.
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Article published in Advanced Materials
This study analyzes the failure of commercial wireless earbud batteries as a model system within their intended usage context.
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Article published in EES Batteries
This study utilizes explainable machine learning (ML) methodologies to tackle this challenge by analyzing a comprehensive database derived from published differential scanning calorimeter (DSC) testing results.
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Article published in Appl. Phys. Lett.
We utilize fused secondary and backscattered electron images to reconstruct 3D microstructures of porous cathodes with high fidelity.
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Jizhou elevated to IEEE Senior Member
Jizhou Li was elected as the Senior Member of IEEE. Senior Member is the highest grade for which IEEE members can apply.
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Article published in Chin. Phys. Lett.
With physics-informed representation learning, we introduce a data compression method tailored for battery X-ray tomography.
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