Blogs
Imaging high-frequency voltage dynamics in multiple neuron classes of behaving mammals
Abstract Fluorescent genetically encoded voltage indicators report transmembrane potentials of targeted cell types. However, voltage-imaging instrumentation has lacked the sensitivity to track spontaneous or evoked high-frequency voltage oscillations in neural populations. Here, 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. With it, we uncovered cross-frequency-coupled theta- and gamma-range oscillations and characterized excitatory-inhibitory neural dynamics during hippocampal ripples and visual cortical processing. The TEMPO mesoscope images voltage activity in two cell classes across an ∼8-mm-wide field of view in head-fixed animals. In awake mice, it revealed sensory-evoked excitatory-inhibitory neural interactions and traveling gamma and 3–7 Hz waves in visual cortex and bidirectional propagation directions for both hippocampal theta and beta waves. These technologies have widespread applications probing diverse oscillations and neuron-type interactions in healthy and diseased brains.
August 7, 2025
Hyperspectral and Multispectral Image Fusion with Arbitrary Resolution Through Self-Supervised Representations
Abstract The fusion of a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) has emerged as an effective technique for achieving HSI super-resolution (SR). Previous studies have mainly concentrated on estimating the posterior distribution of the latent high-resolution hyperspectral image (HR-HSI), leveraging an appropriate image prior and likelihood computed from the discrepancy between the latent HSI and observed images. Low rankness stands out for preserving latent HSI characteristics through matrix factorization among the various priors. However, a key limitation in previous studies is the lack of generalization in fusion models with fixed resolution scales, which require retraining whenever higher output resolutions are needed. To overcome this limitation, we propose a novel continuous low-rank factorization (CLoRF) by integrating two neural representations into the matrix factorization, capturing spatial and spectral information, respectively. This approach harnesses both the low rankness from the matrix factorization and the continuity from neural representation in a self-supervised manner. By adhering to the inherently continuous nature of the underlying hyperspectral image, CLoRF recovers this data in continuous form, enabling the subsequent generation of discrete hyperspectral images at arbitrarily higher spatial or spectral resolutions. Theoretically, we prove the low-rank property and Lipschitz continuity in the proposed continuous low-rank factorization. Experimentally, our method significantly surpasses existing techniques and achieves user-desired resolutions without the need for neural network retraining. Code is available at https://github.com/wangting1907/CLoRF-Fusion .
August 3, 2025
Probing Domain-Boundary-Induced Structural Degradation in Single-Crystalline LiCoO2 by Nanoscale Imaging
Abstract Developing high-capacity cathode materials is pivotal for advancing lithium-ion battery technology. While single-crystalline materials are widely regarded as structurally superior to polycrystalline counterparts, their presumed “perfect” crystallinity has recently been challenged by observations of intrinsic lattice defects and strain heterogeneity. Critically, the lack of direct experimental evidence for these defects and their role in degradation has hindered deeper understanding of single-crystalline cathode failure mechanisms. Here, 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. These boundaries, invisible to conventional characterization techniques, are shown to govern the mechanical and electrochemical degradation of cathode particles. By correlating nanoscale imaging with electrochemical performance, we demonstrate that residual lattice strain at domain boundaries accelerates irreversible phase transitions, while targeted doping element within intragranular can stabilize these critical interfaces. Our findings emphasize that intragranular domain regulation for single-crystalline cathodes, rather than mere morphology control, is essential for designing next-generation high-energy-density batteries.
July 18, 2025
High-Quality CEST Mapping With Lorentzian-Model Informed Neural Representation
Abstract Chemical Exchange Saturation Transfer (CEST) MRI has demonstrated its remarkable ability to enhance the detection of macromolecules and metabolites with low concentrations. While CEST mapping is essential for quantifying molecular information, conventional methods face critical limitations: model-based approaches are constrained by limited sensitivity and robustness depending heavily on parameter setups, while data-driven deep learning methods lack generalizability across heterogeneous datasets and acquisition protocols. To overcome these challenges, 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. Convergence of the self-supervised training strategy is guaranteed theoretically, ensuring LINR’s mathematical validity. The superior performance of LINR in capturing CEST contrasts is revealed through comprehensive evaluations based on synthetic phantoms and in-vivo experiments (including tumor and Alzheimer’s disease models). The intuitive parameter-free design enables adaptive integration into diverse CEST imaging workflows, positioning LINR as a versatile tool for non-invasive molecular diagnostics and pathophysiological discovery.
May 28, 2025
In-device Battery Failure Analysis
Abstract Lithium-ion batteries are indispensable power sources for a wide range of modern electronic devices. However, battery lifespan remains a critical limitation, directly affecting the sustainability and user experience. Conventional battery failure analysis in controlled lab settings may not capture the complex interactions and environmental factors encountered in real-world, in-device operating conditions. This study analyzes the failure of commercial wireless earbud batteries as a model system within their intended usage context. Through multiscale and multimodal characterizations, the degradations from the material level to the device level are correlated, elucidating a failure pattern that is closely tied to the specific device configuration and operating conditions. The findings indicate that the ultimate failure mode is determined by the interplay of battery materials, cell structural design, and the in-device microenvironment, such as temperature gradients and their fluctuations. This holistic, in-device perspective on environmental influences provides critical insights for battery integration design, enhancing the reliability of modern electronics.
February 1, 2025
Disentangling multifactorial impacts on cathode thermochemical properties with explainable machine learning
Abstract Thermal safety remains a critical concern in the commercialization of lithium-ion batteries (LIBs), with extensive research dedicated to understanding the thermal behaviors of cathode materials. While a wealth of thermochemical test data is available in the literature, the variability in sample conditions and experimental testing parameters complicates the identification of fundamental relationships between the intrinsic properties and thermochemical reaction characteristics of materials. 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. By employing meticulously curated, augmented, and filtered features that characterize material properties, sample conditions, and testing parameters, we leveraged ML models to predict and validate thermochemical reaction characteristics across the chemical compositional space of layered oxide cathode materials. Through the explainability, we elucidated multidimensional relationships between input features and thermochemical reaction characteristics, revealing that material properties predominantly dictate the initiation of the reaction, while external conditions exert a greater influence on the kinetics of heat release. This approach demonstrates the effectiveness of ML in decoding complex causal factors of cathode thermochemical reaction behaviors, thereby offering valuable insights for targeted thermal optimization in battery safety design.
January 14, 2025
High-fidelity reconstruction of porous cathode microstructures from FIB-SEM data with deep learning
Abstract Accurate modeling of lithium-ion battery (LIB) electrode microstructures provides essential references for understanding degradation mechanisms and optimizing materials. Traditional segmentation methods often struggle to accurately capture the complex microstructures of porous LIB electrodes in focused ion beam scanning electron microscopy (FIB-SEM) data. In this work, we develop a deep learning model based on the Swin Transformer to segment FIB-SEM data of a lithium cobalt oxide electrode, utilizing fused secondary and backscattered electron images. The proposed approach outperforms other deep learning methods, enabling the acquirement of 3D microstructure with reduced particle elongated artifacts. Analyses of the segmented microstructures reveal improved electrode tortuosity and pore connectivity crucial for ion and electron transport, emphasizing the necessity of accurate 3D modeling for reliable battery performance predictions. These results suggest a path toward voxel-level degradation analysis through more sensible battery simulation on high-fidelity microstructure models directly twinned from real porous electrodes.
October 25, 2024
Compression of Battery X-Ray Tomography Data with Machine Learning
Abstract With the increasing demand for high-resolution x-ray tomography in battery characterization, the challenges of storing, transmitting, and analyzing substantial imaging data necessitate more efficient solutions. Traditional data compression methods struggle to balance reduction ratio and image quality, often failing to preserve critical details for accurate analysis. This study proposes a machine learning-assisted compression method tailored for battery x-ray imaging data. Leveraging physics-informed representation learning, our approach significantly reduces file sizes without sacrificing meaningful information. We validate the method on typical battery materials and different x-ray imaging techniques, demonstrating its effectiveness in preserving structural and chemical details. Experimental results show an up-to-95 compression ratio while maintaining high fidelity in the projection and reconstructed images. The proposed framework provides a promising solution for managing large-scale battery x-ray imaging datasets, facilitating significant advancements in battery research and development.
September 19, 2024