Synthesis of wafer-scale graphdiyne/graphene heterostructure for scalable neuromorphic computing and artificial visual systems

Graphdiyne (GDY) is emerging as a promising material for various applications owing to its unique structure and fascinating properties. However, the application of GDY in electronics and optoelectronics are still in its infancy, primarily owing to the huge challenge in the synthesis of large-area and uniform GDY film for scalable applications. Here a modified van der Waals epitaxy strategy is proposed to synthesize wafer-scale GDY film with high uniformity and controllable thickness directly on graphene (Gr) surface, providing an ideal platform to construct large-scale GDY/Gr-based optoelectronic synapse array. Essential synaptic behaviors have been realized, and the linear and symmetric conductance-update characteristics facilitate the implementation of neuromorphic computing for image recognition with high accuracy and strong fault tolerance. Logic functions including “NAND” and “NOR” are integrated into the synapse which can be executed in an optical pathway. Moreover, a visible information sensing-memory-processing system is constructed to execute real-time image acquisition, in situ image memorization and distinction tasks, avoiding the time latency and energy consumption caused by data conversion and transmission in conventional visual systems. These results highlight the potential of GDY in applications of neuromorphic computing and artificial visual systems.

     

一种基于电子鼻的辛味中药材的分类鉴别方法研究

为获取新的气味识别方法以提高智能传感器模式分类识别准确率和速度,使用了内置10个传感器的便携式电子鼻PEN3对辛味中药材进行气味采集检测.将辛味中药材在烧杯中进行密封静置待其形成稳定的气味顶空环境时,运用电子鼻对其进行检测采样得到样品高维气味数据信息.与传统的线性数据分析方法不同,针对气味蕴含多种诸如浓度、各种挥发性物质成分等特征,可知气味非线性的本质特征,在本次分析中采用了流形算法中的非线性的局部线性嵌入（Locally Linear Embedding,LLE）算法对非线性的气味数据进行特征提取与降维,再采用基于Fisher的线性判别分析（Linear Discriminant Analysis,LDA）实现对特征子空间的模式聚类与分类,通过多次实验优化LLE算法的参数,得到了最佳的辛味中药材的模式识别效果.分析结果表明,运用LLE和LDA相结合的算法（即LLE_LDA）可以很好地完成不同种类辛味中药材的挥发性气味信息的模式分类,为深层次地分析基于电子鼻的气味数据信息提供了一种新方法.     

基于TEM小室的电场探头校准及其不确定度评定

介绍了能够产生标准电磁场的横电磁波传播室(TEM小室)的基本结构及原理,着重研究了TEM小室的场均匀性.构建了一套采用TEM小室对电场探头校准的系统.为了评定校准结果的质量,本文还对TEM小室产生标准电场的影响因素进行了详细分析,进而对TEM小室产生标准电场的不确定度进行评定.     

Ultrastrong,Thermally Stable,and Food-Safe Seaweed-Based Structural Material for Tableware

Widely used disposable plastic tableware is usually buried or directly discharged into the natural environment after using, which poses potential threats to the natural environment and human health. To solve this problem, nondegradable plastic tableware needs to be replaced by tableware composed of biodegradable structural materials with both food safety and the excellent mechanical and thermal properties. Here, a food-safe sargassum cellulose nanofiber (SCNF) is extracted from common seaweed in an efficient and low energy consuming way under mild reaction conditions. Then, by assembling the SCNF into a dense bulk material, a strong sargassum cellulose nanofiber structural material (SCNSM) with high strength (283 MPa) and high thermal stability (>160 °C) can be prepared. The SCNSM also possesses good machinability, which can be processed into tableware with different shapes, e.g., knives and forks. The overall performance of the SCNSM-based tableware is better than commercial plastic, wood-based, and poly(lactic acid) tableware, which shows great application potential in the tableware field.