Molecular Mechanism of Selective AlO Atomic Layer Deposition on Self-Assembled Monolayers.

Area-selective atomic layer deposition (AS-ALD) of insulating metallic oxide layers could be a useful nanopatterning technique for making increasingly complex semiconductor circuits. Although the alkanethiol self-assembled monolayer (SAM) has been considered promising as an ALD inhibitor, the low inhibition efficiency of the SAM during ALD processes makes its wide application difficult. We investigated the deposition mechanism of AlO on alkanethiol-SAMs using temperature-dependent vibrational sum-frequency-generation spectroscopy.

Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.

We present a new deep learning framework for removing honeycomb artifacts yielded by optical path blocking of cladding layers in fiber bundle imaging. The proposed framework, HAR-CNN, provides an end-to-end mapping from a raw fiber bundle image to an artifact-free image via a convolution neural network (CNN). The synthesis of honeycomb patterns on ordinary images allows conveniently learning and validating the network without the enormous ground truth collection by extra hardware setups.

Thermoresponse of Odd-Even Effect in n-Alkanethiolate Self-Assembled Monolayers on Gold Substrates.

This study examines thermoresponse of odd-even effect in self-assembled monolayers (SAMs) of n-alkanethiolates (SC , n=3-18) formed on template-stripped gold (Au ) using macro- and microscopic analytical techniques, contact angle goniometry (CAG) and vibrational sum frequency generation (VSFG) spectroscopy, respectively. Both CAG and VSFG analyses showed that the odd-even effect in liquid-like SAMs (n=3-9) disappeared upon heating at 50-70 °C, indicating that the heating led to increased structural disorder regardless of odd and even carbon numbers. In contrast, the opposite thermoresponse was observed for odd and even SC molecules in wax- and solid-like SAMs (n=10-18).