Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network

The core challenge in directed energy deposition is to obtain high surface quality through process optimisation, which directly affects the mechanical properties of fabricated parts. However, for expensive materials like Ti-6Al-4V, the cost and time required to optimise process parameters can be excessive in inducing good surface quality. To mitigate these challenges, we propose a novel method with artificial intelligence to generate virtual surface morphology of Ti-6Al-4V parts by given process parameters. A high-resolution surface morphology image generation system has been developed by optimising conditional generative adversarial networks. The developed virtual surface matches experimental cases well with an Frechet inception distance score of 174, in the range of accurate matching. Microstructural analysis with parts fabricated with artificial intelligence guidance exhibited less textured microstructural behaviour on the surface which reduces the anisotropy in the columnar structure. This artificial intelligence guidance of virtual surface morphology can help to obtain high-quality parts cost-effectively

Feature importance measures from random forest regressor using near-infrared spectra for predicting carbonization characteristics of kraft lignin-derived hydrochar

This study investigated the feature importance of near-infrared spectra from random forest regression models constructed to predict the carbonization characteristics of hydrochars produced by hydrothermal carbonization of kraft lignin. The model achieved high coefficients of determination of 0.989, 0.988, and 0.985 with root mean square errors of 0.254, 0.003, and 0.008 when predicting the carbon content, atomic O/C ratio, and H/C ratio, respectively. The random forest models outperformed the multilayer perceptron models for all predictions. In the feature importance analysis, the spectral regions at 1600–1800 nm, the first overtone of C–H stretching vibrations, and 2000–2300 nm, the combination bands, were highly important for predicting the carbon content and O/C predictions, whereas the region at 1250–1711 nm contributed to predicting H/C. The random forest models trained with the high-importance regions achieved better prediction performances than those trained with the entire spectral range, demonstrating the usefulness of the feature importance yielded by the random forest and the feasibility of selective application of the spectral data.This study was supported by the Korea Forestry Promotion Institute through the R&D Program for Forest Science Technology funded by the Korea Forest Service (Project No. 2020215D10-2122-AC01)

Measuring Conductance of Phenylenediamine as a Molecular Sensor

We report experimental measurements of molecular conductance as a single molecular sensor by using scanning tunneling microscope-based break-junction (STM-BJ) technique. The gap was created after Au atomic point contact was ruptured, and the target molecule was inserted and bonded to the top and bottom electrodes. We successfully measured the conductance for a series of amine-terminated oligophenyl molecules by forming the molecular junctions with Au electrodes. The measured conductance decays exponentially with molecular backbone length, enabling us to detect the type of molecules as a molecular sensor. Furthermore, we demonstrated reversible binary switching in a molecular junction by mechanical control of the gap between the electrodes. Since our method allows us to measure the conductance of a single molecule in ambient conditions, it should open up various practical molecular sensing applications

Reducing Gap Distance of Ag Electrodes by Oxygen Atomic Junction Formation

Electrode–electrode gap distance (GD) of Ag electrodes was measured by using scanning tunneling microscope breaking junction (STM-BJ) technique. An oxygen atom can be expected to bridge the Ag electrodes in series as Ag–O–Ag during the thinning of Ag point contacts under elongation. The GD with the oxygen junction is smaller by about 1.5 to 2 Å than the GD without the oxygen junction as soon as the Ag contact ruptures. This result is attributed to the Ag–O atomic junction formation, which is enhanced due to oxygen insertion in Ag electrodes. Furthermore, we successfully observed the longer molecular plateau length for the small GD when compared with the large GD, where a series of amine-terminated oligophenyl and alkane molecules were formed with Ag electrodes. This study may advance the understanding of the electrical and mechanical properties in single-molecule-based devices with a smallest Ag electrode gap in future

Imaging Fermi-level hysteresis in nanoscale bubbles of few-layer MoS2

Abstract The electrical stability and reliability of two-dimensional (2D) crystal-based devices are mainly determined by charge traps in the device defects. Although nanobubble structures as defect sources in 2D materials strongly affect the device performance, the local charge-trapping behaviors in nanobubbles are poorly understood. Here, we report a Fermi-level hysteresis imaging strategy using Kelvin probe force microscopy to study the origins of charge trapping in nanobubbles of MoS2 on SiO2. We observe that the Fermi-level hysteresis is larger in nanobubbles than in flat regions and increases with the height in a nanobubble, in agreement with our oxide trap band model. We also perform the local transfer curve measurements on the nanobubble structures of MoS2 on SiO2, which exhibit enhanced current-hysteresis windows and reliable programming/erasing operations. Our results provide fundamental knowledge on the local charge-trapping mechanism in nanobubbles, and the capability to directly image hysteresis can be powerful tool for the development of 2D material-based memory devices

Synthesis of Vapochromic Dyes Having Sensing Properties for Vapor Phase of Organic Solvents Used in Semiconductor Manufacturing Processes and Their Application to Textile-Based Sensors

Two vapochromic dyes (DMx and DM) were synthesized to be used for textile-based sensors detecting the vapor phase of organic solvents. They were designed to show sensitive color change properties at a low concentration of vapors at room temperature. They were applied to cotton fabrics as a substrate of the textile-based sensors to examine their sensing properties for nine organic solvents frequently used in semiconductor manufacturing processes, such as trichloroethylene, dimethylacetamide, iso-propanol, methanol, n-hexane, ethylacetate, benzene, acetone, and hexamethyldisilazane. The textile sensor exhibited strong sensing properties of polar solvents rather than non-polar solvents. In particular, the detection of dimethylacetamide was the best, showing a color difference of 15.9 for DMx and 26.2 for DM under 300 ppm exposure. Even at the low concentration of 10 ppm of dimethylacetamide, the color change values reached 7.7 and 13.6, respectively, in an hour. The maximum absorption wavelength of the textile sensor was shifted from 580 nm to 550 nm for DMx and 550 nm to 540 nm for DM, respectively, due to dimethylacetamide exposure. The sensing mechanism was considered to depend on solvatochromism, the aggregational properties of the dyes and the adsorption amounts of the solvent vapors on the textile substrates to which the dyes were applied. Finally, the reusability of the textile sensor was tested for 10 cycles