Boosted Heterogeneous Catalysis by Surface-Accumulated Excess Electrons of Non-Oxidized Bare Copper Nanoparticles on Electride Support.

Engineering active sites of metal nanoparticle-based heterogeneous catalysts is one of the most prerequisite approaches for the efficient production of chemicals, but the limited active sites and undesired oxidation on the metal nanoparticles still remain as key challenges. Here, it is reported that the negatively charged surface of copper nanoparticles on the 2D [Ca2 N]+ ∙e- electride provides the unrestricted active sites for catalytic selective sulfenylation of indoles and azaindoles with diaryl disulfides. Substantial electron transfer from the electride support to copper nanoparticles via electronic metal-support interactions results in the accumulation of excess electrons at the surface of copper nanoparticles. Moreover, the surface-accumulated excess electrons prohibit the oxidation of copper nanoparticle, thereby maintaining the metallic surface in a negatively charged state and activating both (aza)indoles and disulfides under mild conditions in the absence of any further additives. This study defines the role of excess electrons on the nanoparticle-based heterogeneous catalyst that can be rationalized in versatile systems.

Non-oxidized bare copper nanoparticles with surface excess electrons in air.

Copper (Cu) nanoparticles (NPs) have received extensive interest owing to their advantageous properties compared with their bulk counterparts. Although the natural oxidation of Cu NPs can be alleviated by passivating the surfaces with additional moieties, obtaining non-oxidized bare Cu NPs in air remains challenging. Here we report that bare Cu NPs with surface excess electrons retain their non-oxidized state over several months in ambient air. Cu NPs grown on an electride support with excellent electron transfer ability are encapsulated by the surface-accumulated excess electrons, exhibiting an ultralow work function of ~3.2 eV. Atomic-scale structural and chemical analyses confirm the absence of Cu oxide moiety at the outermost surface of air-exposed bare Cu NPs. Theoretical energetics clarify that the surface-accumulated excess electrons suppress the oxygen adsorption and consequently prohibit the infiltration of oxygen into the Cu lattice, provoking the endothermic reaction for oxidation process. Our results will further stimulate the practical use of metal NPs in versatile applications.

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration.

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

Nanoscale effects of beverages on enamel surface of human teeth: An atomic force microscopy study.

Dental erosion has become a prevalence disease and attracted increasing attention worldwide. In this research, we quantitatively evaluate the mechanical and morphological changes in the very early stages of softening and weakening of human enamel surfaces induced by soft drinks using atomic force microscopy (AFM). With an increase of the immersion time in soft drinks, we found a significant increase of surface roughness (Rq) of the enamel surface. The prismatic structure of enamel was clearly observed after a 1-h immersion in Coca-Cola®, which shows its strong erosion effect. According to the elastic modulus mapping images obtained by AFM, a considerable decrease of elastic modulus (E) of enamel surface has been found as the enamel surface structures are etched away by soft drinks. A high surface roughness of enamel will result in a high chance of cavities due to easier bacterial adhesion on rougher surface, while a drastic deterioration of the mechanical properties of the enamel will weaken its protection property. Our findings show the serious influence of acidic drinks on enamel surface at the very beginning stage of etching process, which is quite meaningful for people to prevent dental erosion and keep dental health.