Soap Film Transfer Printing for Ultrathin Electronics.

Flexible and stretchable electronics have attractive applications inaccessible to conventional rigid electronics. However, the mainstream transfer printing techniques have challenges for electronic films in terms of thickness and size and limitations for target substrates in terms of curvature, depth, and interfacial adhesion. Here a facile, damage-free, and contamination-free soap film transfer printing technique is reported that enables the wrinkle-free transfer of ultrathin electronic films, precise alignment in a transparent manner, and conformal and adhesion-independent printing onto various substrates, including those too topographically and adhesively challenging by existing methods. In principle, not only the pattern, resolution, and thickness of transferred films, but also the curvature, depth, and adhesion of target substrates are unlimited, while the size of transferred films can be as high as meter-scale. To demonstrate the capabilities of soap film transfer printing, pre-fabricated ultrathin electronics with multiple patterns, single micron resolution, sub-micron thickness, and centimeter size are conformably integrated onto the ultrathin web, ultra-soft cotton, DVD-R disk with the minimum radius of curvature of 131 nm, interior cavity of Klein bottle and dandelion with ultralow adhesion. The printed ultrathin sensors show superior conformabilities and robust adhesion, leading to engineering opportunities including electrocardiogram (ECG) signal acquisition and temperature measurement in aqueous environments.

The development of robotics courses for young children under vector space model.

Robotics education is important in training children's thinking, practical, and innovation abilities. It is significant to stimulate children's interest in learning and improve their learning quality. The existing research has not paid attention to the application of robotics education in children. It is necessary to stimulate children's interest in learning. This paper will take senior kindergarten students as the research object. It analyzes the application of the Vector Space Model (VSM) in robotics course development. The research and development of children's robotics courses incorporating Artificial Intelligence technology are based on the survey results of robotics courses offered by 38 kindergartens in Baoji City. An automatic document classification system based on VSM is designed to assist in compiling robotics teaching textbooks. Finally, the system performance is tested. The results show that about 24% of kindergartens offer robotics courses, and 76% do not. Besides, 70.14% of teachers support the establishment of children's robotics courses. The classification effect of the VSM system is better than that of Chinese documents. This system performs better than the automatic document classification system based on Term Frequency-Inverse Document Frequency. Its classification accuracy, recall, and F1 value are all above 85%. The development of the robotics course provides a better teaching environment for teaching young children about AI and robots. The robotics education discussed in this paper is a hot spot in the current curriculum reform and is of great significance to the development and innovation in early childhood education.

A drug molecular classification model based on graph structure generation.

Molecular property prediction based on artificial intelligence technology has significant prospects in speeding up drug discovery and reducing drug discovery costs. Among them, molecular property prediction based on graph neural networks (GNNs) has received extensive attention in recent years. However, the existing graph neural networks still face the following challenges in node representation learning. First, the number of nodes increases exponentially with the expansion of the perception field, which limits the exploration ability of the model in the depth direction. Secondly, the large number of nodes in the perception field brings noise, which is not conducive to the model's representation learning of the key structures. Therefore, a graph neural network model based on structure generation is proposed in this paper. The model adopts the depth-first strategy to generate the key structures of the graph, to solve the problem of insufficient exploration ability of the graph neural network in the depth direction. A tendentious node selection method is designed to gradually select nodes and edges to generate the key structures of the graph, to solve the noise problem caused by the excessive number of nodes. In addition, the model skillfully realizes forward propagation and iterative optimization of structure generation by using an attention mechanism and random bias. Experimental results on public data sets show that the proposed model achieves better classification results than the existing best models.

A nanonewton-scale biomimetic mechanosensor.

Biomimetic mechanosensors have profound implications for various areas, including health care, prosthetics, human‒machine interfaces, and robotics. As one of the most important parameters, the sensitivity of mechanosensors is intrinsically determined by the detection resolution to mechanical force. In this manuscript, we expand the force detection resolution of current biomimetic mechanosensors from the micronewton to nanonewton scale. We develop a nanocrack-based electronic whisker-type mechanosensor that has a detection resolution of 72.2 nN. We achieve the perception of subtle mechanical stimuli, such as tiny objects and airflow, and the recognition of surface morphology down to a 30 nm height, which is the finest resolution ever reported in biomimetic mechanosensors. More importantly, we explore the use of this mechanosensor in wearable devices for sensing gravity field orientation with respect to the body, which has not been previously achieved by these types of sensors. We develop a wearable smart system for sensing the body's posture and movements, which can be used for remote monitoring of falls in elderly people. In summary, the proposed device offers great advantages for not only improving sensing ability but also expanding functions and thus can be used in many fields not currently served by mechanosensors.

Nanoengineering Ultrathin Flexible Pressure Sensor with Superior Sensitivity and Perfect Conformability.

Flexible pressure sensors play an increasingly important role in a wide range of applications such as human health monitoring, soft robotics, and human-machine interfaces. To achieve a high sensitivity, a conventional approach is introducing microstructures to engineer the internal geometry of the sensor. However, this microengineering strategy requires the sensor's thickness to be typically at hundreds to thousands of microns level, impairing the sensor's conformability on surfaces with microscale roughness like human skin. In this manuscript, a nanoengineering strategy is pioneered that paves a path to resolve the conflicts between sensitivity and conformability. A dual-sacrificial-layer method is initiated that facilitates ease of fabrication and precise assembly of two functional nanomembranes to manufacture the thinnest resistive pressure sensor with a total thickness of ≈850 nm that achieves perfectly conformable contact to human skin. For the first time, the superior deformability of the nanothin electrode layer on a carbon nanotube conductive layer is utilized by the authors to achieve a superior sensitivity (92.11 kPa-1 ) and an ultralow detection limit (<0.8 Pa). This work offers a new strategy that is able to overcome a key bottleneck for current pressure sensors, therefore is of potential to inspire the research community for a new wave of breakthroughs.