Growth Behaviors of ZnO Nanorods Grown with the Sn-Based Bilayer Catalyst-Covered Substrates

The growth of ZnO nanorods performed at 700°C with the mixture of Zn and ZnO as the Zn source was investigated by having the catalysts in bilayer configurations of Sn (top)/Au (bottom), Sn/Al, Sn/Ni, and Sn/In. These catalyst layers were preannealed at 700°C or 850°C in a gas mixture of argon and hydrogen. The variations in the process parameters are to give the modulations in growing ZnO rods for the purpose of investigating the growth behaviors. The results show that the different compositions and configurations of bilayer catalysts can lead to different reactions and interdiffusions or in different kinetic performance, which will produce different sizes and states of catalyst templates for growing different sizes of the ZnO rods. The small-diameter ZnO nanorods with a hexagonal cross-section at the size of 70–150 nm were obtained from the Sn/Ni catalyst systems preannealed at 850°C

High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications

Invited; Developing low-temperature defect passivation technology with supercritical fluid technology

Current technology nodes in the process of semiconductor manufacturing have faced many bottlenecks. Therefore, a disruptive-innovative semiconductor processing technology is crucially needed to make a significant breakthrough. Our research team has developed a low temperature (RT~250°C), defect passivation technology based on the supercritical fluid (SCF) technology applied in the nano-scale device processing to overcome the key issues. The SCF technology was originally applied in the field of the extraction and the cleaning of biotechnologies. However, our research team firstly applies this technology in the optoelectronic device. Compared to current high pressure annealing (HPA) and rapid thermal annealing (RTA) methods, the SCF-based defect passivation technology features low temperature, and can be applied for various materials and devices including photoelectric device, advanced nano-device, memory device, and wide bandgap device. Currently, the prototype of the 12” supercritical fluid processing equipment has already been built, and related recipes including nitridation, oxidation, hydrogenation, and sulfurization are also implemented for various devices and applications. In this talk, we will introduce related SCF defect passivation technology and future developments for the SCF applications

Predictive Solution for Radiation Toxicity Based on Big Data

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients’ cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system

Mutation of SLC35D3 causes metabolic syndrome by impairing dopamine signaling in striatal D1 neurons

We thank Dr. Ya-Qin Feng from Shanxi Medical University, Dr. Tian-Yun Gao from Nanjing University and Dr. Yan-Hong Xue from Institute of Biophysics (CAS) for technical assistance in this study. We are very thankful to Drs. Richard T. Swank and Xiao-Jiang Li for their critical reading of this manuscript and invaluable advice. Funding: This work was partially supported by grants from National Basic Research Program of China (2013CB530605; 2014CB942803), from National Natural Science Foundation of China 1230046; 31071252; 81101182) and from Chinese Academy of Sciences (KSCX2-EW-R-05, KJZD-EW-L08). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD