Adsorption, Segregation and Magnetization of a Single Mn Adatom on the GaAs (110) Surface

Density functional calculations with a large unit cell have been conducted to investigate adsorption, segregation and magnetization of Mn monomer on GaAs(110). The Mn adatom is rather mobile along the trench on GaAs(110), with an energy barrier of 0.56 eV. The energy barrier for segregation across the trenches is nevertheless very high, 1.67 eV. The plots of density of states display a wide gap in the majority spin channel, but show plenty of metal-induced gap states in the minority spin channel. The Mn atoms might be invisibl in scanning tunneling microscope (STM) images taken with small biases, due to the directional p-d hybridization. For example, one will more likely see two bright spots on Mn/GaAs(110), despite the fact that there is only one Mn adatom in the system

Broadband RCS Reduction of Microstrip Patch Antenna Using Bandstop Frequency Selective Surface

In this article, a simple and effective approach is presented to reduce the Radar Cross Section (RCS) of microstrip patch antenna in ultra broad frequency band. This approach substitutes a metallic ground plane of a conventional patch antenna with a hybrid ground consisting of bandstop Frequency Selective Surface (FSS) cells with partial metallic plane. To demonstrate the validity of the proposed approach, the influence of different ground planes on antenna’s performance is investigated. Thus, a patch antenna with miniaturized FSS cells is proposed. The results suggest that this antenna shows 3dB RCS reduction almost in the whole out-of operating band within 1-20GHz for wide incident angles when compared to conventional antenna, while its radiation characteristics are sustained simultaneously. The reasonable agreement between the measured and the simulated results verifies the efficiency of the proposed approach. Moreover, this approach doesn’t alter the lightweight, low-profile, easy conformal and easy manufacturing nature of the original antenna and can be extended to obtain low-RCS antennas with metallic planes in broadband that are quite suitable for the applications which are sensitive to the variation of frequencies

Red Soundscape Index (RSI): An index with the potential to assess soundscape quality

It is not enough to define urban soundscape just using the green soundscape index (GSI), which is the ratio of the perception of natural sounds to the perception of traffic noises. Therefore, in the present study, red soundscape index (RSI), defined as the ratio of perception of natural sounds to perception of human sounds, was introduced. The data for calculating RSI were collected from sound environment measurements and a questionnaire-based survey in seven urban parks in Harbin city, China. The results revealed the following: (1) RSI was correlated with the overall soundscape quality; (2) RSI was correlated with the maximum and minimum instantaneous sound pressure levels and with equivalent sound pressure levels; and (3) The urban sound environment as well as sound quality can be classified by RSI. It was confirmed that RSI could be used as a supplement to GSI in urban soundscape planning

A machine learning method to quantitatively predict alpha phase morphology in additively manufactured Ti-6Al-4V

Quantitatively defining the relationship between laser powder bed fusion (LPBF) process parameters and the resultant microstructures for LPBF fabricated alloys is one of main research challenges. To date, achieving the desired microstructures and mechanical properties for LPBF alloys is generally done by time-consuming and costly trial-and-error experiments that are guided by human experience. Here, we develop an approach whereby an image-driven conditional generative adversarial network (cGAN) machine learning model is used to reconstruct and quantitatively predict the key microstructural features (e.g., the morphology of martensite and the size of primary and secondary martensite) for LPBF fabricated Ti-6Al-4V. The results demonstrate that the developed image-driven machine learning model can effectively and efficiently reconstruct micrographs of the microstructures within the training dataset and predict the microstructural features beyond the training dataset fabricated by different LPBF parameters (i.e., laser power and laser scan speed). This study opens an opportunity to establish and quantify the relationship between processing parameters and microstructure in LPBF Ti-6Al-4V using a GAN machine learning-based model, which can be readily extended to other metal alloy systems, thus offering great potential in applications related to process optimisation, material design, and microstructure control in the additive manufacturing field