Machine Learning Assisted Electronic/Ionic Skin Recognition of Thermal Stimuli and Mechanical Deformation for Soft Robots.

Soft robots have the advantage of adaptability and flexibility in various scenarios and tasks due to their inherent flexibility and mouldability, which makes them highly promising for real-world applications. The development of electronic skin (E-skin) perception systems is crucial for the advancement of soft robots. However, achieving both exteroceptive and proprioceptive capabilities in E-skins, particularly in terms of decoupling and classifying sensing signals, remains a challenge. This study presents an E-skin with mixed electronic and ionic conductivity that can simultaneously achieve exteroceptive and proprioceptive, based on the resistance response of conductive hydrogels. It is integrated with soft robots to enable state perception, with the sensed signals further decoded using the machine learning model of decision trees and random forest algorithms. The results demonstrate that the newly developed hydrogel sensing system can accurately predict attitude changes in soft robots when subjected to varying degrees of pressing, hot pressing, bending, twisting, and stretching. These findings that multifunctional hydrogels combine with machine learning to decode signals may serve as a basis for improving the sensing capabilities of intelligent soft robots in future advancements.

Acoustic emission-based intelligent identification of piston aero-engine ignition advance angle anomalies.

Ignition advance angle is one of the important factors affecting the performance of the engine, when it occurs abnormally will make the engine power and economy worse, and even cause serious damage to the engine. Therefore, it is very necessary to recognize the abnormal ignition advance angle of the engine. However, the engine system is closed and has a complex structure, which makes traditional diagnostic methods difficult. This paper proposes an intelligent identification method based on acoustic emission (AE) signals, which collects the AE signals from the engine surface and divides their spectra into equal parts, and selects the frequency bands with high contribution to the classification based on the minimum distance method to construct feature maps, which is used as the input to the convolutional neural network (CNN). The extracted frequency band features of this method can better characterize the AE signals, and the constructed feature maps make the fault information more obvious. Experiments show that the accuracy of this method for abnormal ignition advance angle under normal operating conditions of piston aero-engine is 100%, which is better than the traditional methods. In addition, the recognition accuracies under the other two operating conditions are 99.75% and 98.5%, respectively, indicating that the method has a certain universality.

Multifunctional Photothermal Phase-Change Superhydrophobic Film with Excellent Light-Thermal Conversion and Thermal-Energy Storage Capability for Anti-icing/De-icing Applications.

The accumulation of ice may cause serious safety problems in numerous fields. A photothermal superhydrophobic surface is considered to be useful for preventing ice formation because of its environmentally friendly, energy-saving, and excellent anti-icing/de-icing properties. However, it easily fails to work in the absence of sunlight. To improve its anti-icing property without sunlight irradiation, a multifunctional photothermal phase-change superhydrophobic film (MPPSF) consisting of phase-change microcapsules (PCMs) and carbon nanotubes (CNTs) was fabricated using a facile spraying method. Benefitting from the excellent light-thermal conversion effect of CNTs, the surface temperature could increase from -20 to 130.1 °C within 180 s under 808 nm near-infrared laser irradiation of 1 W/cm2, thus realizing high-efficiency de-icing. Meanwhile, a portion of the light-thermal energy was stored in the MPPSF because of the phase change of the PCMs. Without sunlight irradiation, the latent heat of the PCMs was released when the external temperature approached the phase-transition temperature. The synergistic effects of the phase-transition latent heat release and superhydrophobicity allowed the MPPSF to effectively hinder the formation of ice for 10.1 min at -20 °C. Therefore, this MPPSF with outstanding anti-icing and de-icing performances is expected to achieve ice prevention and removal in all-days.

Preparation of Novel Graphene/Silicone Rubber Nanocomposite Dielectric Foams.

In order to obtain high dielectric silicone rubber (SR)-based nanocomposites, graphene (Gr) was added by ultrasonication and mechanical mixing for the preparation of a microporous structure. It was discovered that the Gr content and the expansion rate had a great impact on the cellular structure. Based on the effects of the Gr content and the expansion rate on the dielectric property, hybrid materials were prepared and better properties appeared, as expected. For all samples, the dielectric constant increased with the Gr content until 3 wt% and then decreased. When the Gr content was 3 wt% and the expansion rate was 2, the dielectric constant reached 18.14 (1 kHz), which was 55% higher than that of the non-expansion sample (11.74) and several times that of the pure sample (3~6). Meanwhile, the dielectric loss was less than 0.01. This work proposed a method for producing high dielectric materials with important applications in the field of capacitors, sensors, and micro-resistors.

Multifunctional Slippery Polydimethylsiloxane/Carbon Nanotube Composite Strain Sensor with Excellent Liquid Repellence and Anti-Icing/Deicing Performance.

In this paper, a multifunctional slippery polydimethylsiloxane/carbon nanotube composite strain sensor (SPCCSS) is prepared using a facile template method. Benefitting from the slippery surface, the SPCCSS shows excellent liquid repellence properties, which can repel various liquids such as oil, cola, yogurt, hot water and some organic solvents. Meanwhile, the SPCCSS has a large strain sensing range (up to 100%), good sensitivity (GF = 3.3) and stable response with 500 cyclic stretches under 20% strain. Moreover, it is also demonstrated that the SPCCSS displays outstanding corrosion resistance (from pH = 1 to pH = 14) and anti-icing (8 min at -20 °C)/photothermal deicing (104 s with NIR power density of 1 W/cm2) properties, broadening its application in extreme acid, alkali and low-temperature conditions. Therefore, the multifunctional SPCCSS with the liquid repellence, anti-corrosion, and anti-icing/deicing properties has potential applications in wearable human motion monitoring tools under complex harsh environments.