A simple and sensitive approach is proposed and evaluated for determination of ultratrace Zn and Cd in limited amounts of samples or tens of cells based on a novel single drop (5-20 μL) solution electrode glow discharge assisted-chemical vapor generation technique. Volatile species of Zn and Cd were immediately generated and separated from the liquid phase for transporting to atomic fluorescence or atomic mass spectrometric detectors for their determination only using hydrogen when the glow discharge was ignited between the surface of a liquid drop and the tip of a tungsten electrode. Limits of detection are better than 0.01 μg L(-1) (0.2 pg) for Cd and 0.1 μg L(-1) (2 pg) for Zn, respectively, and comparable or better than the previously reported results due to only a 20 μL sampling volume required, which makes the proposed technique convenient for the determination of Zn and Cd in limited amounts of samples or even only tens of cells. The proposed method not only retains the advantages of conventional chemical vapor generation but also provides several unique advantages, including better sensitivity, lower sample and power consumption, higher chemical vapor generation efficiencies and simpler setup, as well as greener analytical chemistry. The utility of this technique was demonstrated by the determination of ultratrace Cd and Zn in several single human hair samples, Certified Reference Materials GBW07601a (human hair powder) and paramecium cells.

To solve the stability issue of cost-effective nonfluorinated membranes, an ether-free poly(arylene piperidinium) (PBPip)-based membrane is first applied in redox flow batteries (RFBs). For improved efficiencies of RFB, amphoteric side chains are introduced onto the PBPip. Without an ether bond in the polymer backbone, the membrane shows a good stability in a strong oxidation environment. The Fourier transform infrared (FTIR) spectra exhibit no obvious changes over 30 days of oxidation test. Different from traditional blended amphoteric membranes, the amphoteric side chain allows both cation-and anion-exchange capacities to increase with grafting degree, which leads to a very high total ion-exchange capacity (IEC) (4.19 mmol g −1 ). Outstanding ion-conduction ability (area resistance: 0.22 Ω cm 2 ) comparable to Nafion 212 (0.24 Ω cm 2 ) is consequently achieved. Ionic cross-linking structure between cationic and anionic groups results in a low swelling rate (13.9%). Combined with the repelling effect of positively charged piperidinium, a low VO 2+ permeability (1.31 × 10 −8 cm 2 s −1 ) is accomplished. On the basis of these good properties, the membrane exhibits excellent vanadium battery performances, especially at high current densities. The VE and EE both exceed 80% even at 200 mA cm −2 . The battery performances have no obvious reductions after 500 cycles. These results indicate that this work provides a new orientation to design the membrane for RFB.

With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.