Direct Three-Dimensional Observation of the Plasmonic Near-Fields of a Nanoparticle with Circular Dichroism.

Characterizing the spatial distribution of the electromagnetic fields of a plasmonic nanoparticle is crucial for exploiting its strong light-matter interaction for optoelectronic and catalytic applications. However, observing the near-fields in three dimensions with a high spatial resolution is still challenging. To realize efficient three-dimensional (3D) nanoscale mapping of the plasmonic fields of nanoparticles with complex shapes, this work established autoencoder-embedded electron energy loss spectroscopy (EELS) tomography.

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 MnCaO 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.

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.

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.