Kink-Controlled Gold Nanoparticles for Electrochemical Glucose Oxidation.

Enzymes in nature efficiently catalyze chiral organic molecules by elaborately tuning the geometrical arrangement of atoms in the active site. However, enantioselective oxidation of organic molecules by heterogeneous electrocatalysts is challenging because of the difficulty in controlling the asymmetric structures of the active sites on the electrodes. Here, we show that the distribution of chiral kink atoms on high-index facets can be precisely manipulated even on single gold nanoparticles; and this enabled stereoselective oxidation of hydroxyl groups on various sugar molecules. We characterized the crystallographic orientation and the density of kink atoms and investigated their specific interactions with the glucose molecule due to the geometrical structure and surface electrostatic potential.

Anomalous optical excitations from arrays of whirlpooled lattice distortions in moiré superlattices.

Moiré superlattices formed by stacking two-dimensional crystals have reinvigorated the pursuit for emergent functionalities of engineered superlattices. Unique optical characteristics can be realized from the interplay between the electronic excitations and the atomic rearrangements owing to their intrinsic softness. Although large-scale reconstructions have been identified at small twist angles, they have been treated as being rigid at large twist angles. Here, we report that moiré superlattices made from single layers of MoS2 and WSe2 exhibit a pair of torsional strains with opposite chirality irrespective of the twist angle. The whirlpool-shaped periodic lattice distortions introduce fuzziness in the Raman spectra and universal redshifts to the intralayer excitons for all twist angles. We show that both of these modulations become weaker as the twist angle increases but do not disappear, whereas they are turned off when the constituent layers are not tightly coupled, thus establishing an essential structure-property relationship for moiré superlattices.

Strain-Induced Modulation of Localized Surface Plasmon Resonance in Ultrathin Hexagonal Gold Nanoplates.

Anisotropic gold nanoplates (NPLs) have raised the interesting possibility that their reduced geometrical symmetry allows fine tuning of their optical properties associated with the excitation of localized surface plasmon resonances (LSPRs). Recent developments have greatly improved LSPR tunability by utilizing the spatial distribution of LSPR modes. However, the nanoscale interplay between defect-induced mechanical strain and the spatial variation of LSPR modes remains poorly understood. In this work, the combination of high spatial- and spectral-resolution mapping of LSPR modes and nanoscale strain mapping using aberration-corrected transmission electron microscopy are applied to investigate the nanoscale distribution of LSPR modes in an ultrathin single hexagonal gold NPL and the effect of defect-induced strains on its LSPR properties. The electron energy-loss spectral maps reveal four distinct LSPR components and intensity distributions of all LSPR modes in a hexagonal gold NPL. Furthermore, the strain maps provide experimental evidence that the tensile strain field induced by a Z-shaped faulted dipole is responsible for the asymmetric distribution of LSPR intensity in a hexagonal gold NPL.

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.