Progressive NO2 Sensors with Rapid Alarm and Persistent Memory-Type Responses for Wide-Range Sensing Using Antimony Triselenide Nanoflakes

Antimony triselenide (Sb₂Se₃) nanoflake-based nitrogen dioxide (NO₂) sensors exhibit a progressive bifunctional gas-sensing performance, with a rapid alarm for hazardous highly concentrated gases, and an advanced memory-type function for low-concentration (<1 ppm) monitoring repeated under potentially fatal exposure. Rectangular and cuboid shaped Sb₂Se₃ nanoflakes, comprising van der Waals planes with large surface areas and covalent bond planes with small areas, can rapidly detect a wide range of NO₂ gas concentrations from 0.1 to 100 ppm. These Sb₂Se₃ nanoflakes are found to be suitable for physisorption-based gas sensing owing to their anisotropic quasi-2D crystal structure with extremely enlarged van der Waals planes, where they are humidity-insensitive and consequently exhibit an extremely stable baseline current. The Sb₂Se₃ nanoflake sensor exhibits a room-temperature/low-voltage operation, which is noticeable owing to its low energy consumption and rapid response even under a NO₂ gas flow of only 1 ppm. As a result, the Sb₂Se₃ nanoflake sensor is suitable for the development of a rapid alarm system. Furthermore, the persistent gas-sensing conductivity of the sensor with a slow decaying current can enable the development of a progressive memory-type sensor that retains the previous signal under irregular gas injection at low concentrations.     

Multiply Charged Conjugated Polyelectrolytes as a Multifunctional Interlayer for Efficient and Scalable Perovskite Solar Cells

A series of anionic conjugated polyelectrolytes (CPEs) is synthesized based on poly(fluorene-co-phenylene) by varying the side-chain ionic density from two to six per repeat units (MPS2-TMA, MPS4-TMA, and MPS6-TMA). The effect of MPS2, 4, 6-TMA as interlayers on top of a hole-extraction layer of poly(bis(4-phenyl)-2,4,6-trimethylphenylamine (PTAA) is investigated in inverted perovskite solar cells (PeSCs). Owing to the improved wettability of perovskites on hydrophobic PTAA with the CPEs, the PeSCs with CPE interlayers demonstrate a significantly enhanced device performance, with negligible device-to-device dependence relative to the reference PeSC without CPEs. By increasing the ionic density in the MPS-TMA interlayers, the wetting, interfacial defect passivation, and crystal growth of the perovskites are significantly improved without increasing the series resistance of the PeSCs. In particular, the open-circuit voltage increases from 1.06 V for the PeSC with MPS2-TMA to 1.11 V for the PeSC with MPS6-TMA. The trap densities of the PeSCs with MPS2,4,6-TMA are further analyzed using frequency-dependent capacitance measurements. Finally, a large-area (1 cm²) PeSC is successfully fabricated with MPS6-TMA, showing a power conversion efficiency of 18.38% with negligible hysteresis and a stable power output under light soaking for 60 s.     

Properties and applications of β‐glycosidase from Bacteroides thetaiotaomicron that specifically hydrolyses isoflavone glycosides

To modify the glycan part of glycosides, the gene encoding β‐glycosidase was cloned from Bacteroides thetaiotaomicron VPI‐5482. The cloned gene, bt_1780, was expressed in Escherichia coli MC1061 and the expressed enzyme was purified using Ni‐NTA affinity chromatography. The purified enzyme, BTBG, showed optimal activity at 50 °C and pH 5.5. Interestingly, this enzyme did not have any hydrolysing activity on ordinary β‐linkage–containing substrates such as xylobiose, lactose and cello‐oligosaccharide, but specifically hydrolysed isoflavone glycosides such as daidzin, genistin and glycitin. Compared to a commercial beta glucosidase, BTBG selectively hydrolysed isoflavone glycosides in soybean extract mixture solution. These results suggest that BTBG may be a specialized enzyme for the hydrolysis of glycosides and that the substrate specificity of BTBG is applicable for the bioconversion of isoflavone glycosides in the food industry.     

A robust control of robot manipulators for physical interaction: stability analysis for the interaction with unknown environments

Intelligent Service Robotics - A robust control designed for multiple degrees-of-freedom (DOF) robot manipulators performing complex tasks requiring frequent physical interaction with unknown...     

Partitioning of predominant lipophilic bioactives (squalene, α-tocopherol and β-sitosterol) during olive oil processing

The partitioning of predominant lipophilic olive bioactives (squalene, β-sitosterol and α-tocopherol) in olive oil processing was studied for the first time using common integrated olive milling plant. 7% of the oil and 5% of the squalene present in the olives was lost in the last pomace. β-sitosterol and α-tocopherol, which are mainly concentrated in the seed of the olive fruit, was recovered in virgin olive oil to a lesser extent (66%, 67%) while 10% were lost in last pomace. Thus, the loss of β-sitosterol and α-tocopherol was higher than that of squalene and oil, due to nonrecovered bound forms. 19% of the α-tocopherol and 21% of the β-sitosterol loss was unaccounted for, which can be attributed to degradation of α-tocopherol and incomplete recovery of sterols from the olive and pomace matrices. This study provides a basis for process development studies.     

Recording of volume hologram using a beam pattern from taperedoptical fiber

A photorefractive volume hologram was recorded and probed using light diffracted from a tapered optical fiber as a reference beam. A single-mode fiber (SMF) was chemically etched and tapered to give a complicated beam pattern, and it is shown that the tapered optical fiber can be utilized to increase the storage density of the volume hologram. Spatial selectivity of the volume hologram with this method was increased by two times compared to the normal SMF referencing, which is due to the fact that the complicated beam pattern has little correlation with its shifted version     

用于机器人轨迹定位纠正系统的回声状态神经网络研究

循环神经网络(RecurrentNeuralNetworks)是人工神经网络(ArtificialNeuralNetworks)中重要的分支,与前馈神经网络(ForwardNeuralNetworks)相比具有更好的时间序列学习能力。但长期以来其学习法一直不能脱离前馈神经网络而自成一体,回声状态神经网络(EchoStateNetworks(ESN))是打破这一局面的全新学习方法。其独特的结构,良好的短期记忆能力,方便的学习方法,不俗的非线性特性是以前循环神经网络所不可比的。本文在介绍了回声状态神经网络之后将其用于四轮机器人的位置测量系统中,有良好的表现。