Conformational molecular switch of the azobenzene molecule: A scanning tunneling microscopy study

We propose to utilize azobenzene as a nanomolecular switch which can be triggered by transmitting electrons above threshold biases. The effect is explained by an electron impact trans-cis conformational change of the isolated azobenzene molecules. The molecular electronic states of both isomers have been measured with spatially resolved scanning tunneling microscopy or spectroscopy, leading to suggested transition pathways of the electron-induced isomerization.open21716

Paired gap states in a semiconducting carbon nanotube: Deep and shallow levels

Several paired, localized gap states were observed in semiconducting single-wall carbon nanotubes using spatially resolved scanning tunneling spectroscopy. A pair of gap states is found far from the band edges, forming deep levels, while the other pair is located near the band edges, forming shallow levels. With the help of a first-principles study, the former is explained by a vacancy-adatom complex while the latter is explained by a pentagon-heptagon structure. Our experimental observation indicates that the presence of the gap states provides a means to perform local band-gap engineering as well as doping without impurity substitution.open433

Contribution of coastal seiches to sediment transport in a microtidal semi-enclosed bay

Moorings and axial surveys using acoustic Doppler current profilers in microtidal Masan Bay were conducted to reveal impacts of coastal seiches on sediment behaviors. The hydrodynamic circulation in the bay was dominated by sluggish tidal and residual currents, with which the coastal seiches with a 1-h period were detected. The coastal seiches velocity (useiche) accounted for approximately 30% of the total velocities, causing back-and-forth water motions along the channel. This was insufficient to resuspend bed sediments without external forcings. Nevertheless, it influenced the suspended sediment concentration (SSC) of turbidity maximum (~40 mg l−1) at the central part of bay, showing SSC anomaly of 8 mg l−1. Although the seiche-induced sediment fluxes were only 1% of the total fluxes due to offsetting effect of bidirectional flows, they reached up to 0.040×10−3 kg m−2 s−1 at each pulse of coastal seiches. Repetitive coastal seiches lifted the sediment particles to the upper layer where they would not have risen if not for seiche vertical motion. However, the distance that the coastal seiches can transport the suspended sediments was too short compared to their transportable amounts. Even if sediment particles within turbidity maximum were advected by coastal seiches, they could not leave the region. This process was intensified toward the land because the useiche slowed down the further as it moved away from the node. As long as the bed sediments were resuspended, the coastal seiches were expected to enhance the potential for water pollution by causing repetitive sediment redistribution

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.Comment: 21 pages, 4 figure