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The current preparation methods of carbon quantum dots (CDs) involve many reaction parameters, which leads to many possibilities in the synthesis processes and high uncertainty of the resultant production performance. Recently, machine learning (ML) methods have shown great potential in correlating the selected features in many applications, which can help understand the relevant structure–function relationships of CDs and discover better synthesis recipes as well. In this work, we employ the ML approach to guide the blue CD synthesis in microwave systems. After optimizing the synthesis parameters and conditions, the quantum yield (QY) increases to about 200% higher than the average value of the prepared samples without ML guidance. The obtained CDs are applied as fluorescent probes to monitor hydrogen peroxide (H 2 O 2 ) in human teeth. The CD probe exhibits a linear relationship with the concentration of H 2 O 2 ranging from 0 to 1.1 M with a lower detection limit of 0.12 M, which can effectively detect the residual H 2 O 2 after bleaching teeth. This work shows that the adopted ML methods have considerable advantages in guiding the synthesis of high-quality CDs, which could accelerate the development of other novel functional materials in energy, biomedical, and environmental remediation applications. 

Compared to traditional rigid-bodied robots, soft robots are constructed using physically flexible/elastic bodies and electronics to mimic nature and enable novel applications in industry, healthcare, aviation, military, etc. Recently, the fabrication of robots on soft matter with great flexibility and compliance has enabled smooth and sophisticated ‘multi-degree-of-freedom’ 3D actuation to seamlessly interact with humans, other organisms and non-idealized environments in a highly complex and controllable manner. Herein, we summarize the fabrication approaches, driving strategies, novel applications, and future trends of soft robots. Firstly, we introduce the different fabrication approaches to prepare soft robots and compare and systematically discuss their advantages and disadvantages. Then, we present the actuator-based and material-based driving strategies of soft robotics and their characteristics. The representative applications of soft robotics in artificial intelligence, medicine, sensors, and engineering are summarized. Also, some remaining challenges and future perspectives in soft robotics are provided. This work highlights the recent advances of soft robotics in terms of functional material selection, structure design, control strategies and biomimicry, providing useful insights into the development of next-generation functional soft robotics. 

Abstract The environmental problems of global warming and fossil fuel depletion are increasingly severe, and the demand for energy conversion and storage is increasing. Ecological issues such as global warming and fossil fuel depletion are increasingly stringent, increasing energy conversion and storage needs. The rapid development of clean energy, such as solar energy, wind energy and hydrogen energy, is expected to be the key to solve the energy problem. Several excellent literature works have highlighted quantum dots in supercapacitors, lithium-sulfur batteries, and photocatalytic hydrogen production. Here, we outline the latest achievements of quantum dots and their composites materials in those energy storage applications. Moreover, we rationally analyze the shortcomings of quantum dots in energy storage and conversion, and predict the future development trend, challenges, and opportunities of quantum dots research.