arXiv subjects

Xiaoya He

Publications and source records attributed to Xiaoya He.

4 recordsLinked to original sources

arXiv2026

Visualizing surface-reaction-driven chloride migration and cascading failure in sulfide solid electrolytes

Moisture instability of sulfide solid electrolytes limits practical use in all-solid-state batteries. Spatially resolving gas--solid reactions during moisture exposure remains challenging. Here, cryogenic four-dimensional scanning transmission electron microscopy (cryo-4D-STEM) maps nanoscale degradation in low- and high-Cl argyrodite electrolytes, Li\(_6\)PS\(_5\)Cl and Li\(_{5.5}\)PS\(_{4.5}\)Cl\(_{1.5}\). Li\(_6\)PS\(_5\)Cl undergoes predominantly surface-localized degradation that progressively extends inward, whereas Li\(_{5.5}\)PS\(_{4.5}\)Cl\(_{1.5}\) exhibits rapid interior amorphization accompanied by outward chloride redistribution. Density functional theory calculations indicate a lower chloride-migration barrier along the modeled pathway in the higher-Cl composition, while chloride removal and vacancy formation distort the PS\(_4^{3-}\) framework and may destabilize the lattice. Surface treatment suppresses detectable large-scale chloride segregation and improves moisture tolerance, retaining 99.9% of the capacity measured at the start of 0.33 C cycling after 150 cycles. These results identify chloride redistribution as a key process in degradation propagation and suggest a route toward moisture-tolerant solid-state batteries.

cond-mat.mtrl-sci↗

arXiv2026

4DMulti: automated multicomponent identification at complex material interfaces

Mapping crystalline phases at heterogeneous interfaces is essential for understanding material performance and degradation. However, structural heterogeneity, phase overlap, and local disorder complicate diffraction interpretation, while growing data volumes make manual analysis increasingly impractical. We introduce 4DMulti, a physics-guided learning framework for automated multicomponent identification from large-scale four-dimensional scanning transmission electron microscopy (4D-STEM) data. The supporting diffraction data resource comprises over 6 million high-quality experimental patterns and labeled patterns generated by Sim2real. A retrieval-conditioned latent diffusion transformer (Sim2real) translates simulated patterns into experimental-style examples under constraints designed to preserve Bragg geometry, while a rotation-invariant coordinate convolutional network identifies phases across in-plane rotations. 4DMulti achieves 98.82% classification accuracy on a five-phase experimental nanoparticle benchmark, with ablation studies supporting the complementary benefits of domain adaptation and rotation-invariant classification. We define diffraction-inferred structural complexity (DISC), a normalized predictive entropy score that quantifies phase-assignment ambiguity within a specified candidate phase library. We apply 4DMulti to generate structural maps of superconducting heterostructures, corroded alloy surfaces, and degraded solid-state battery interfaces down to single-nanometer spatial resolution. 4DMulti connects simulation-derived crystallographic knowledge to automated experimental interpretation, establishing a foundation for scalable analysis of complex interfaces and data-driven discovery of interfacial design principles.

cond-mat.mtrl-sci↗

arXiv2025

4D-PreNet: A Unified Preprocessing Framework for 4D-STEM Data Analysis

Automated experimentation with real time data analysis in scanning transmission electron microscopy (STEM) often require end-to-end framework. The four-dimensional scanning transmission electron microscopy (4D-STEM) with high-throughput data acquisition has been constrained by the critical bottleneck results from data preprocessing. Pervasive noise, beam center drift, and elliptical distortions during high-throughput acquisition inevitably corrupt diffraction patterns, systematically biasing quantitative measurements. Yet, conventional correction algorithms are often material-specific and fail to provide a robust, generalizable solution. In this work, we present 4D-PreNet, an end-to-end deep-learning pipeline that integrates attention-enhanced U-Net and ResNet architectures to simultaneously perform denoising, center correction, and elliptical distortion calibration. The network is trained on large, simulated datasets encompassing a wide range of noise levels, drift magnitudes, and distortion types, enabling it to generalize effectively to experimental data acquired under varying conditions. Quantitative evaluations demonstrate that our pipeline reduces mean squared error by up to 50% during denoising and achieves sub-pixel center localization in the center detection task, with average errors below 0.04 pixels. The outputs are bench-marked against traditional algorithms, highlighting improvements in both noise suppression and restoration of diffraction patterns, thereby facilitating high-throughput, reliable 4D-STEM real-time analysis for automated characterization.

cs.CV↗

arXiv2025

4D-MISR: A unified model for low-dose super-resolution imaging via feature fusion

While electron microscopy offers crucial atomic-resolution insights into structure-property relationships, radiation damage severely limits its use on beam-sensitive materials like proteins and 2D materials. To overcome this challenge, we push beyond the electron dose limits of conventional electron microscopy by adapting principles from multi-image super-resolution (MISR) that have been widely used in remote sensing. Our method fuses multiple low-resolution, sub-pixel-shifted views and enhances the reconstruction with a convolutional neural network (CNN) that integrates features from synthetic, multi-angle observations. We developed a dual-path, attention-guided network for 4D-STEM that achieves atomic-scale super-resolution from ultra-low-dose data. This provides robust atomic-scale visualization across amorphous, semi-crystalline, and crystalline beam-sensitive specimens. Systematic evaluations on representative materials demonstrate comparable spatial resolution to conventional ptychography under ultra-low-dose conditions. Our work expands the capabilities of 4D-STEM, offering a new and generalizable method for the structural analysis of radiation-vulnerable materials.

cs.CV↗