Superhydrophobic and Superlipophilic Low Density Polyethylene/Styrene–Butadiene Rubber Thermoplastic Vulcanizate Film for Pressure‐Responsive Continuous Oil–Water Separation

Abstract In recent years, with the continuous discharge of wastewater, which has caused serious environmental pollution, it is a task to separate oil or water from wastewater. Therefore, an efficient and low‐cost oil–water separation method is needed to separate the oil–water mixture. Here, a superhydrophobic/superoleophilic low density polyethylene/styrene‐butadiene rubber (LDPE/SBR) thermoplastic vulcanizate (TPV) film (oil contact angle of 0° and water contact angle of 161.1° ± 1.7°) is prepared using an etched aluminum foil as a template and applied to a laboratory‐assembled oil–water separation device, which is a new method for oil–water separation via a pressure response valve. The LDPE/SBR TPV film is rolled up and stuffed into the through‐valve, and the gap between the films is used as the pressure response channel for oil and water separation, thus achieving oil and water separation. When the film gap is 25 or 50 µm, the separation efficiency of TPV film is greater than 99% with the variation of external pumping force, indicating that this method can achieve complete oil–water separation under a suitable external pumping force. This functional TPV film has good recyclability, environmental stability, chemical stability, mechanical durability, as well as thermal stability, which makes it have great application potential

An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed

The accurate spraying of herbicides and intelligent mechanical weeding operations are the main ways to reduce the use of chemical pesticides in fields and achieve sustainable agricultural development, and an important prerequisite for achieving these is to identify field crops and weeds accurately and quickly. To this end, a semantic segmentation model based on an improved U-Net is proposed in this paper to address the issue of efficient and accurate identification of vegetable crops and weeds. First, the simplified visual group geometry 16 (VGG16) network is used as the coding network of the improved model, and then, the input images are continuously and naturally down-sampled using the average pooling layer to create feature maps of various sizes, and these feature maps are laterally integrated from the network into the coding network of the improved model. Then, the number of convolutional layers of the decoding network of the model is cut and the efficient channel attention (ECA) is introduced before the feature fusion of the decoding network, so that the feature maps from the jump connection in the encoding network and the up-sampled feature maps in the decoding network pass through the ECA module together before feature fusion. Finally, the study uses the obtained Chinese cabbage and weed images as a dataset to compare the improved model with the original U-Net model and the current commonly used semantic segmentation models PSPNet and DeepLab V3+. The results show that the mean intersection over union and mean pixel accuracy of the improved model increased in comparison to the original U-Net model by 1.41 and 0.72 percentage points, respectively, to 88.96% and 93.05%, and the processing time of a single image increased by 9.36 percentage points to 64.85 ms. In addition, the improved model in this paper has a more accurate segmentation effect on weeds that are close to and overlap with crops compared to the other three comparison models, which is a necessary condition for accurate spraying and accurate weeding. As a result, the improved model in this paper can offer strong technical support for the development of intelligent spraying robots and intelligent weeding robots

Reconstruction of the first metatarsophalangeal joint by vascular anastomotic transplantation of fibular head: A case report

Foot injury with soft tissue and bone defects is very common, and it is very difficult to reconstruct the irreparable first metatarsophalangeal joint in clinical work. In this paper, partial fibular head free transplantation was used to reconstruct the articular surface defect of the first metatarsal head and restore the first metatarsophalangeal joint in a clinic case. After 18 months of follow-up, the patient achieved satisfactory first metatarsophalangeal joint function

Gelatin Nanoparticle-Coated Silicon Beads for Density-Selective Capture and Release of Heterogeneous Circulating Tumor Cells with High Purity

Background: Circulating tumor cells (CTCs) are a burgeoning topic in cancer biomarker discovery research with minimal invasive blood draws. CTCs can be used as potential biomarkers for disease prognosis, early cancer diagnosis and pharmacodynamics. However, the extremely low abundance of CTCs limits their clinical utility because of technical challenges such as the isolation and subsequent detailed molecular and functional characterization of rare CTCs from patient blood samples. Methods: In this study, we present a novel density gradient centrifugation method employing biodegradable gelatin nanoparticles coated on silicon beads for the isolation, release, and downstream analysis of CTCs from colorectal and breast cancer patients. Results: Using clinical patient/spiked samples, we demonstrate that this method has significant CTC-capture efficiency (>80%) and purity (>85%), high CTC release efficiency (94%) and viability (92.5%). We also demonstrate the unparalleled robustness of our method in downstream CTC analyses such as the detection of PIK3CA mutations. Conclusion: The efficiency and versatility of the multifunctional density microbeads approach provides new opportunities for personalized cancer diagnostics and treatments