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Aqueous Zn/MnO 2 batteries with their environmental sustainability and competitive cost, are becoming a promising, safe alternative for grid-scale electrochemical energy storage. Presented as a promising design principle to deliver a higher theoretical capacity, this work offers fundamental understanding of the dissolution–deposition mechanism of Zn/β-MnO 2 . A multimodal synchrotron characterization approach including three operando X-ray techniques (powder diffraction, absorption spectroscopy, and fluorescence microscopy) is coupled with elementally resolved synchrotron X-ray nano-tomography. Together they provide a direct correlation between structural evolution, reaction chemistry, and 3D morphological changes. Operando synchrotron X-ray diffraction and spectroscopy show a crystalline-to-amorphous phase transition. Quantitative modeling of the operando data by Rietveld refinement for X-ray diffraction and multivariate curve resolution (MCR) for X-ray absorption spectroscopy are used in a complementary fashion to track the structural and chemical transitions of both the long-range (crystalline phases) and short-range (including amorphous phases) ordering upon cycling. Scanning X-ray microscopy and full-field nano-tomography visualizes the morphology of electrodes at different electrochemical states with elemental sensitivity to spatially resolve the formation of the Zn- and Mn-containing phases. Overall, this work critically indicates that for Zn/MnO 2 aqueous batteries, the reaction pathways involving Zn–Mn complex formation upon cycling become independent of the polymorphs of the initial electrode and sheds light on the interplay among structural, chemical, and morphological evolution for electrochemically driven phase transitions. 

Abstract Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films. 

Thin-film solid-state interfacial dealloying (thin-film SSID) is an emerging technique to design nanoarchitecture thin films. The resulting controllable 3D bicontinuous nanostructure is promising for a range of applications including catalysis, sensing, and energy storage. Using a multiscale microscopy approach, we combine X-ray and electron nano-tomography to demonstrate that besides dense bicontinuous nanocomposites, thin-film SSID can create a very fine (5–15 nm) nanoporous structure. Not only is such a fine feature among one of the finest fabrications by metal-agent dealloying, but a multilayer thin-film design enables creating nanoporous films on a wider range of substrates for functional applications. Through multimodal synchrotron diffraction and spectroscopy analysis with which the materials’ chemical and structural evolution in this novel approach is characterized in details, we further deduce that the contribution of change in entropy should be considered to explain the phase evolution in metal-agent dealloying, in addition to the commonly used enthalpy term in prior studies. The discussion is an important step leading towards better explaining the underlying design principles for controllable 3D nanoarchitecture, as well as exploring a wider range of elemental and substrate selections for new applications. 

Abstract Three-dimensional bicontinuous porous materials formed by dealloying contribute significantly to various applications including catalysis, sensor development and energy storage. This work studies a method of molten salt dealloying via real-time in situ synchrotron three-dimensional X-ray nano-tomography. Quantification of morphological parameters determined that long-range diffusion is the rate-determining step for the dealloying process. The subsequent coarsening rate was primarily surface diffusion controlled, with Rayleigh instability leading to ligament pinch-off and creating isolated bubbles in ligaments, while bulk diffusion leads to a slight densification. Chemical environments characterized by X-ray absorption near edge structure spectroscopic imaging show that molten salt dealloying prevents surface oxidation of the metal. In this work, gaining a fundamental mechanistic understanding of the molten salt dealloying process in forming porous structures provides a nontoxic, tunable dealloying technique and has important implications for molten salt corrosion processes, which is one of the major challenges in molten salt reactors and concentrated solar power plants.