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Charge extraction layers with excellent charge extraction capability are essential for achieving high photovoltaic performance in cells. In this work, a hole extraction layer (HEL) is developed by doping conductive polymer TFB into CuSCN (CuSCN:TFB(X)), which exhibits good light transparency and high affinity for the light absorber. Compared to the reference cell, the CuSCN:TFB(X) HEL-based cells show impressive enhancement owing to the increased exciton dissociation and charge extraction processes and weak recombination losses. Furthermore, matched work function, better interface contact, and appropriate domain size also contribute to the enhanced power conversion efficiency. As a consequence, the highest conversion efficiency of 15.28% is observed in a cell based on the PM6:Y6 blend film and CuSCN:TFB(1.0%) HEL, which is >16% higher than the efficiency of 13.13% in a cell with CuSCN HEL.

Powering marine distributed nodes (e.g. monitoring buoys) has always been challenging under the harsh conditions in oceans. Triboelectric nanogenerator (TENG), on account of its robustness and cost, provide a novel solution to this problem. In this study, a stackable TENG (S-TENG) consisting of polytetrafluoroethylene pellets, an acrylic plates coated with aluminum film and a 3D-printed brace, has been proposed to harvest wave energy efficiently. Experiments show that each S-TENG unit produces a maximum peak power of 25.22mW while a power density of 34.65W/m3 is achieved. The output characteristic of multiple S-TENG units parallel connected is studied. It turns out that the output performance yields linear increase with the unit number without having to use rectifier bridge. This feature indicates that self-powered marine monitoring buoy is achievable by S-TENG integration.

Image enhancement technology is often used to improve the quality of medical images and helps doctors or expert systems identify and diagnose diseases. This paper aimed at the characteristics of magnetic resonance imaging (MRI) with complex and difficult-to-enhance details and to propose a nonsubsampled contourlet transform- (NSCT-) based enhancement algorithm called MIE-NSCT. NSCT was used for MRI sub-band decomposition. For high-pass sub-bands, four fuzzy rules were proposed to enhance multiscale and multidirectional edge contour details from adjacent eight directions, whilst for low-pass sub-bands, a new adaptive histogram enhancement algorithm was proposed. The problem of noise amplification and loss of details during the enhancement process was solved. The algorithm was verified on the public dataset BraTS2017 and compared with other advanced methods. Experimental results showed that MIE-NSCT had obvious advantages in improving the quality of medical images, and high-quality medical images showed enhanced performance in grading tumour. MIE-NSCT is suitable for integration into an interactive expert system to provide support for the visualization of disease diagnosis.

The use of medical image synthesis with generative adversarial networks (GAN) is effective for expanding medical samples. The structural consistency between the synthesized and actual image is a key indicator of the quality of the synthesized image, and the region of interest (ROI) of the synthesized image is related to its usability, and these parameters are the two key issues in image synthesis. In this paper, the fusion-ROI patch GAN (Fproi-GAN) model was constructed by incorporating a priori regional feature based on the two-stage cycle consistency mechanism of cycleGAN. This model has improved the tissue contrast of ROI and achieved the pairwise synthesis of high-quality medical images and their corresponding ROIs. The quantitative evaluation results in two publicly available datasets, INbreast and BRATS 2017, show that the synthesized ROI images have a DICE coefficient of 0.981 ± 0.11 and a Hausdorff distance of 4.21 ± 2.84 relative to the original images. The classification experimental results show that the synthesized images can effectively assist in the training of machine learning models, improve the generalization performance of prediction models, and improve the classification accuracy by 4% and sensitivity by 5.3% compared with the cycleGAN method. Hence, the paired medical images synthesized using Fproi-GAN have high quality and structural consistency with real medical images.

Powering marine distributed buoys sustainably and cost-efficiently has always been challenging in the ocean environment. Triboelectric nanogenerators (TENG), thanks to their robustness, high efficiency and low cost in energy harvesting, has provided a novel solution to the long-term electrical power supply for a series of distributed systems (including buoys). In this study, a wave energy harvesting sandwich-like TENG (S-TENG) has been proposed. In the on-land forced motion experiments, the S-TENG yields much superior performances compared to previous tower-like TENG in terms of the power density. The studies also reveal that the electrical output increases linearly even with larger number of parallel connected S-TENG layers. Consequently, a buoy integrated with 7 parallel connected S-TENGs is studied through systematic wave basin experiments and the relevance of most hydrodynamic parameters (including wave height, frequency, direction as well as mooring system) have been revealed. The presented wave basin experiments are original in wave energy harvesting TENG research practice and prove to be very helpful in that they simulate the most close-to-real ocean environments in the lab. The studies are concluded with a 12 W high-brightness LED lit up on a navigation supporting buoy. This study has set a benchmark of achieving self-powered buoy with TENG, which can be extended to other marine distributed systems such as sensors and small robotics.

The integration of traditional wind barriers and novel wind energy harvesters is highly desirable for the conversion of wind energy to electricity while protecting transportations from strong wind. In this work, a multi-functional wind barrier integrated by manifold triboelectric nanogenerator (TENG) units is proposed and investigated. The TENG unit consists of two copper electrodes and one strip of fluorinated ethylene propylene (FEP) membrane whose two ends are fixed on a 3D printed channel. Based on the optimized structure of each TENG unit, a wind barrier is presently constructed by 66 TENG units connecting in parallel. It is found that the wind barrier can generate the output current and power of up to 440 μ A and 26 mW at the wind speed of 10 m/s. It is also significant that the TENG-based wind barrier can harvest slipstream energy induced by passing vehicles. More importantly, the windshield efficiency of the TENG-based wind barrier is 35% higher than that of the traditional porous wind barrier, significantly improving the safety of transportations. In addition, each TENG unit of the wind barrier can work as a self-powered anemometer to monitor the condition of the wind barrier. Therefore, the present novel wind barrier has a great potential to apply for wind energy harvesting, self-powered wind speed sensing and highly efficient wind protection.

There is an obvious pressing need for the rapid, sensitive and highly portable identification of toxic gases. In this study, a battery-operated, low-cost and portable embedded gas detection device is fabricated with ARM9 S3C2440A core as the main application processor. And a quite simple and effective cross-responsive sensor array is utilized here for toxic gases detection based on cross-reactive recognition mechanism. This device shows how the discriminatory capacity of sensor arrays utilizing pattern recognition operating in environments. It collects images of chemical sensors before and after exposing to the target gas, and then processes those images to extract the unique characteristic for each gas. The gas chamber model was optimized by simulating with FLUENT software, to ensure a sufficiently reaction between toxic gases and cross-responsive sensor arrays. Then five toxic gases were detected by the proposed device and experiments showed the validity of the system for the qualitative and quantitative identification of toxic gases. It has shown successful classification of the five toxic gases by HCA and PCA analysis at ppb level. Importantly, this developed equipment provides a potential method for the qualitative and quantitative identification of toxic gases.