Iridium-Cooperated, Symmetry-Broken Manganese Oxide Nanocatalyst for Water Oxidation.

The water oxidation reaction, the most important reaction for hydrogen production and other sustainable chemistry, is efficiently catalyzed by the Mn4CaO5 cluster in biological photosystem II. However, synthetic Mn-based heterogeneous electrocatalysts exhibit inferior catalytic activity at neutral pH under mild conditions. Symmetry-broken Mn atoms and their cooperative mechanism through efficient oxidative charge accumulation in biological clusters are important lessons but synthesis strategies for heterogeneous electrocatalysts have not been successfully developed. Here, we report a crystallographically distorted Mn-oxide nanocatalyst, in which Ir atoms break the space group symmetry from I41/amd to P1. Tetrahedral Mn(II) in spinel is partially replaced by Ir, surprisingly resulting in an unprecedented crystal structure. We analyzed the distorted crystal structure of manganese oxide using TEM and investigated how the charge accumulation of Mn atoms is facilitated by the presence of a small amount of Ir.

Overcharge-Induced Phase Heterogeneity and Resultant Twin-Like Layer Deformation in Lithium Cobalt Oxide Cathode for Lithium-Ion Batteries.

Overcharging is expected to be one of the solutions to overcome the current energy density limitation of lithium-ion battery cathodes, which will support the rapid growth of the battery market. However, high-voltage charging often poses a major safety threat including fatal incendiary incidents, limiting further application. Numerous researches are dedicated to the disadvantages of the overcharging process; nonetheless, the urgent demand for addressing failure mechanisms is still unfulfilled. Herein, it is revealed that overcharging induces phase heterogeneity into layered and cobalt oxide phases, and consequent "twin-like deformation" in lithium cobalt oxide. The interplay between the uncommon cobalt(III) oxide and the deformation is investigated by revealing the atomistic formation mechanism. Most importantly, abnormal cracking is discovered in the vicinity of the cobalt oxide where structural instability induces substantial contraction. In addition, surface degradation is widely observed in the crack boundary inside the particle. As unintentional overcharging can occur due to local imbalance in state-of-charge in severe operating conditions such as fast charging, the issues on overcharging should be emphasized to large extent and this study provides fundamental knowledge of overcharge by elucidating the crack development mechanism of layered cathodes, which is expected to broaden the horizon into high voltage operation.

Atomic Reconstruction and Oxygen Evolution Reaction of Mn3O4 Nanoparticles.

Understanding the chemical states of individual surface atoms and their arrangements is essential for addressing several current issues such as catalysis, energy stroage/conversion, and environmental protection. Here, we exploit a profile imaging technique to understand the correlation between surface atomic structures and the oxygen evolution reaction (OER) in Mn3O4 nanoparticles. We image surface structures of Mn3O4 nanoparticles and observe surface reconstructions in the (110) and (101) planes. Mn3+ ions at the surface, which are commonly considered as the active sites in OER, disappear from the reconstructed planes, whereas Mn3+ ions are still exposed at the edges of nanoparticles. Our observations suggest that surface reconstructions can deactivate low-index surfaces of Mn oxides in OER. These structural and chemical observations are further validated by density functional theory calculations. This work shows why atomic-scale characterization of surface structures is crucial for a molecular-level understanding of a chemical reaction in oxide nanoparticles.

Dimensionality reduction and unsupervised clustering for EELS-SI.

A novel combination of machine learning algorithms is proposed for the differentiation of distinct spectra in a large electron energy loss spectroscopy spectrum image (EELS-SI) dataset. For clustering of the EEL spectra including similar fine structures in an efficient space, linear and nonlinear dimensionality reduction methods are used to project the EEL spectra onto a low-dimensional space. Then, a density-based clustering algorithm is applied to distinguish the meaningful data clusters. By applying this strategy to various experimental EELS-SI datasets, differentiation of several groups of EEL spectra representing specific fine structures was achieved. It is possible to investigate particular fine structures by averaging all of the spectra in each cluster. Also, the spatial distributions of each cluster in the scanning regions can be observed, which enables investigation of the locations of different fine structures in materials. This method does not require any prior knowledge, i.e., it is a data-driven analysis; therefore, it can be applied to any hyperspectral image.