Tactile sensing is crucial for the safety, accuracy and robustness of the human-robot interactions in the fields of wearable equipment, service robots and healthcare robots. Although many efforts have been made, it still requires much work to develop functional and reliable flexible tactile sensors with superior sensitivity, wide measurement range, high spatial resolution and low cost based on simple structures and easy fabrication. Here, this paper introduces a flexible supercapacitive tactile sensor with outstanding and balanced performance. The tactile sensor contains two layers of flexible electrodes and a layer of ionic-gel coated microfiber matrix to form a supercapacitive sensing structure. The flexible electrodes and the ionic microfiber matrix are processed with scalable techniques such as screen printing and gel-coating, which guarantee the ultra-flexibility and low fabrication cost. The experimental data suggests strong linearity in the pressure-capacitance relationship and high sensitivity (135.9 nF•kPa −1 •cm 2 or 27.11 kPa −1). Wide pressure measurement range (from 0 kPa to 1200 kPa) is also achieved by balancing structure parameters. Dynamic responses of the tactile sensors could accurately reflect the applied pressure cycles from human-finger tapping and machine pressing. The tactile sensor can map the pressure distribution with a high spatial resolution (>2 points•mm −2) when connected with the specially designed electric circuitry. The spatial resolution from sub-mm to large area makes it promising for various sensing applications in human-robot interactions, from finger touch to body contact. The developed tactile sensor in this study owns superior applicability and universality which makes it a trustworthy candidate to benefit various applications in robotics, flexible electronics and bioengineered equipment.

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Soil microbial communities play a key role in the functioning of terrestrial ecosystems, in particular through their interaction with above-ground plants and weathering of rocks. In this study, the chemical properties and microbial diversity of soils covered by different organisms on Leshan Giant Buddha body were analyzed. The results showed that the concentration of soil total organic carbon (TOC), total nitrogen (TN) and total phosphorus (TP) increased significantly with the change of above-ground organisms from lichens to bryophytes and vascular plants. TOC, TN, TP, C:N, and C:P were significantly correlated with the composition of microbial community. Bacterial and fungal diversity responded differently to the change of organisms, and the diversity of bacterial communities changed significantly among different sites. The settlement of Embryogenic plants increased the α-diversity indices including Sobs, Shannon, Ace and Chao indices, which were highest in sites covered with Ferns. The relative abundances of Chloroflexi, Acidobacteria, Nitrospirae and Planctomycetes increased with the order of Bryophyte, Fern, Grass and Shrub, and Cyanobacteria was opposite, with the highest in samples covered with lichens. These results improve understanding of plant–fungi–bacteria interactions during the early stages of soil development, and provide a scientific basis for protection of Leshan Giant Buddha.

With the increasing problem of water pollution, oil–water separation technology has attracted widespread attention worldwide. In this study, we proposed laser electrochemical deposition hybrid preparation of an oil–water separation mesh and introduced a back-propagation (BP) neural network model to realize the regulation of metal filter mesh. Among them, the coating coverage and electrochemical deposition quality were improved by laser electrochemical deposition composite processing. Based on the BP neural network model, the pore size after electrochemical deposition could be obtained only by inputting the processing parameters into the model, enabling the prediction and control of the pore size of the processed stainless-steel mesh (SSM), and the maximum residual difference between the predicted value and the experimental value was 1.5%. According to the oil–water separation theory and practical requirements, the corresponding electrochemical deposition potential and electrochemical deposition time were determined by the BP neural network model, which reduced the cost and time loss. In addition, the prepared SSM was found to achieve efficient separation of oil and water mixtures, reaching 99.9% separation efficiency in a combination with oil–water separation, along with other performance tests without chemical modification. The prepared SSM showed good mechanical durability and the separation efficiency exceeded 95% after sandpaper abrasion, thus, still maintaining the separation ability of oil–water mixture. Compared to other similar preparation methods, the method proposed in this study has the advantages of controllable pore size, simplicity, convenience, environmental friendliness, and durable wear resistance, offering important application potential in the treatment of oily wastewater.

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