Yimei Zhu

Metal oxidation is traditionally described as a dichotomy: oxide scales grow either outward via cation diffusion or inward via anion diffusion, with the dominant pathway determined by the prevailing point defects in the oxide lattice. Here, we demonstrate that these two modes are not mutually exclusive but intrinsically coupled within a single oxidation process. Using in situ environmental transmission electron microscopy to monitor the oxidation of copper—a canonical cationic system—we directly visualize simultaneous oxide propagation at both the O ₂ /Cu ₂ O surface interface and the buried Cu ₂ O/Cu interface. Complementary simulations reveal the mechanistic origin of this behavior. Outward cation transport during surface oxide growth injects a sustained flux of vacancies into the underlying metal lattice, which facilitates inward oxygen incorporation and drives oxide growth at the buried interface. This vacancy-mediated feedback couples cationic and anionic transport process, providing a unified framework in which surface cationic growth directly enables inward anionic-driven growth. By identifying vacancy injection as a key mediator of interfacial mass transport, this work provides a mechanistic basis for predicting oxidation kinetics and defect-regulated solid-state transformation pathways in reactive environments.

Phase transition materials such as NdNiO ₃ (NNO) when coupled with low damping ferromagnets such as La _0.67 Sr _0.33 MnO ₃ (LSMO) hold the promise for developing new multi-functional material systems harnessing the interplay of charge, spin, and orbital degrees of freedom. In this study, we probe the evolution of spin-to-charge conversion in epitaxial all-oxide LSMO (12 nm)/NNO (4, 8, and 16 nm) bilayers. Using spin pumping ferromagnetic resonance, we track the spin-to-charge conversion within the NNO layer through the paramagnetic metal to antiferromagnetic insulator transition and observe a pronounced enhancement of the inverse spin Hall effect signal at the onset of this phase transition. We attribute this enhancement to the electronic and magnetic disorder in NNO at the first-order phase transition, thereby providing insights into the mechanism of spin transport through the phase transition. The tunability of spin-to-charge conversion in this low damping bilayer system offers a pathway for developing reconfigurable, energy-efficient spintronic devices.

A number of intriguing phenomena, including exciton condensation, orbital ordering, and emergence of chirality, have been proposed to accompany charge-density-wave (CDW) formation in the layered transition metal dichalcogenide 1T-TiSe_{2}. Explaining these effects relies on knowledge of the atomic displacement pattern underlying the CDW, yet structural proposals based on spatially averaging bulk crystal diffraction and surface-dependent scanning tunneling microscopy have remained inconsistent. Here, we revisit the CDW superlattice structure of 1T-TiSe_{2} with selected-area electron diffraction, a bulk-sensitive probe capable of capturing submicrometer spatial variations while maintaining high momentum resolution. We observed superlattice peak extinction rules that are incompatible with previously reported atomic displacement patterns, and we found two categories of CDW phases characterized by distinct interlayer orderings. Our analysis identifies a set of possible superlattice structures consistent with the extinction rules, all featuring a one-dimensional displacement pattern for each atomic type and possessing a large number of nearly degenerate CDW domains. These findings shed light on the longstanding discrepancy in the low-temperature space group of 1T-TiSe_{2} and provide a new basis for understanding the gyrotropic electronic order and metastability in this material. Our results further underscore the importance of bulk-sensitive mesoscopic techniques in investigating materials that host unconventional superlattices.

Uncertainty quantification is critical in scientific inverse problems to distinguish identifiable parameters from those that remain ambiguous given available measurements. The Conditional Diffusion Model-based Inverse Problem Solver (CDI) has previously demonstrated effective probabilistic inference for one-dimensional temporal signals, but its applicability to higher-dimensional spatial data remains unexplored. We extend CDI to two-dimensional spatial conditioning, enabling probabilistic parameter inference directly from spatial observations. We validate this extension on convergent beam electron diffraction (CBED) parameter inference -- a challenging multi-parameter inverse problem in materials characterization where sample geometry, electronic structure, and thermal properties must be extracted from 2D diffraction patterns. Using simulated CBED data with ground-truth parameters, we demonstrate that CDI produces well-calibrated posterior distributions that accurately reflect measurement constraints: tight distributions for well-determined quantities and appropriately broad distributions for ambiguous parameters. In contrast, standard regression methods—while appearing accurate on aggregate metrics—mask this underlying uncertainty by predicting training set means for poorly constrained parameters. Our results confirm that CDI successfully extends from temporal to spatial domains, providing the genuine uncertainty information required for robust scientific inference.

In film-substrate systems, the substrate role is often considered to be limited to providing static mechanical constraints. Dynamic film-substrate interactions when a structural change in the film modifies the substrate are generally disregarded. Using combined X-ray and electron microscopies, we observed that the electrically induced filament in a VO ₂ film created strong asymmetric strain in the underlying Al ₂ O ₃ substate. This asymmetric substrate strain fed back into the film and defined the filament expansion direction, revealing the importance of film-substrate dynamic interactions in determining film functionality. Furthermore, the strain imprint propagated at least tens of microns deep into the substrate, exceeding the film thickness more than 200 times, potentially enabling substrate functionalization as an active mechanical coupling media in 3D-integrated microelectronics architectures.

Microscopy techniques can uncover the physical properties and dynamic behaviours of materials, driving the discovery of emergent phenomena and guiding the design of next-generation computing hardware. As artificial intelligence becomes pervasive, the demand for high-performance materials to support sustainable information technologies is growing. This Review highlights state-of-the-art imaging from electron and X-ray to optical techniques to probe the dynamics of neuromorphic materials, including operando characterization of devices. We examine design principles for neuromorphic materials, along with obstacles that hinder their development. Emphasis is placed on spatially and temporally resolved approaches that capture state changes including phase transitions, ferroic switching and spin-wave propagation that emulate biological components such as neurons, synapses and their connectivity. We discuss challenges in operando characterization and the integration of artificial intelligence-driven analysis for feedback-guided material discovery. Finally, we outline opportunities for real-time imaging of neuromorphic systems, paving the way towards adaptive, brain-inspired hardware.

Synchronization is conventionally regarded as a mechanism for suppressing variability and enforcing order in coupled systems, from pendula and lasers to neurons and electronic oscillators. Here, we show that synchronization can also embed stochasticity at finer scales. We observe this phenomenon in thermally coupled VO₂ neuristors, where robust in-phase synchronization at the microsecond scale coexists with spike onset fluctuations at the nanosecond scale, with no fixed leader. The coexistence of order and disorder originates from stochastic domain-level physics of the insulator–metal and metal–insulator transitions, where local variations in transition temperature drive cycle-to-cycle randomness in nucleation, percolation, and relaxation. A stochastic domain model reproduces this effect by generating synchronized spike trains with random lead–lag jitter, and experimental interspike interval statistics confirm the persistence of fine-scale variability despite macroscopic phase locking. These findings establish that synchronization and stochasticity can coexist within the same physical platform, revealing hidden disorder within collective order. This insight reframes synchronization as not purely deterministic, but as a universal context where microscopic variability can persist, with implications for electronics, cryptography, and the fundamental physics of order–disorder coexistence.