It is a major challenge to effectively inhibit microbial pathogens in the treatment of infectious diseases. Research on the application of nanomaterials as antibacterial agents has evidenced their great potential for the remedy of infectious disease. Among these nanomaterials, carbon quantum dots (CQDs) have attracted much attention owing to their unique optical properties and high biosafety. In this work, P-doped CQDs were prepared by simple hydrothermal treatment of m-aminophenol and phosphoric acid with fluorescence emission at 501 nm when excited at 429 nm. The P-doped CQDs showed effective antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). The minimal inhibitory concentrations (MICs) of P-doped CQD were 1.23 mg/mL for E. coli and 1.44 mg/mL for S. aureus. Furthermore, the morphologies of E. coli cells were damaged and S. aureus became irregular when treated with the P-doped CQDs. The results of zeta potential analysis demonstrated that the P-doped CQDs inhibit antibacterial activity and destroy the structure of bacteria by electronic interaction. In combination, the results of this study indicate that the as-prepared P-doped CQDs can be a promising candidate for the treatment of bacterial infections.

With the in-depth advancement of the fifth generation (5G) mobile communication technology, the technical requirements for filters are also constantly improving. Surface acoustic wave (SAW) filters are widely used in home TV, mobile communications, radio frequency filters and radar due to their simple structure, few mask layers, easy miniaturization, and low cost. Through the continuous improvement of communication technology, SAW has developed into various high-performance acoustic filters from bulk SAW with the support of some new architectures, new materials and advanced modeling techniques. This paper analyzes and reviews the research situation of SAW filter technology.

With the rapid progress of artificial intelligence, humans are moving toward the era of the intelligent connection of all things. Therefore, the demand for sensors is drastically increasing with developing intelligent social applications. Traditional sensors must be triggered by an external power source and the energy consumption is high for equipment that is widely distributed and working intermittently, which is not conducive to developing sustainable green and healthy applications. However, self-powered sensors based on triboelectric nanogenerators (TENG) can autonomously harvest energy from the surrounding environment and convert this energy into electrical energy for storage. Sensors can also be self-powered without an external power supply, which is vital for smart cities, smart homes, smart transportation, environmental monitoring, wearable devices, and bio-medicine. This review mainly summarizes the working mechanism of TENG and the research progress of self-powered sensors based on TENG about the Internet of Things (IoT), robotics, human–computer interaction, and intelligent medical fields in recent years.

Sensors are an important part of the organization required for robots to perceive the external environment. Self-powered sensors can be used to implement energy-saving strategies in robots and reduce their power consumption, owing to their low-power consumption characteristics. The triboelectric nanogenerator (TENG) and piezoelectric transducer (PE) are important implementations of self-powered sensors. Hybrid sensors combine the advantages of the PE and TENG to achieve higher sensitivity, wider measurement range, and better output characteristics. This paper summarizes the principles and research status of pressure sensors, displacement sensors, and three-dimensional (3D) acceleration sensors based on the self-powered TENG, PE, and hybrid sensors. Additionally, the basic working principles of the PE and TENG are introduced, and the challenges and problems in the development of PE, TENG, and hybrid sensors in the robotics field are discussed with regard to the principles of the self-powered pressure sensors, displacement sensors, and 3D acceleration sensors applied to robots.

Computational modeling is a crucial approach in material-related research for discovering new materials with superior properties. However, the high design flexibility in materials, especially in the realm of metamaterials where the sub-wavelength structure provides an additional degree of freedom in design, poses a formidable computational cost in various real-world applications. With the advent of big data, deep learning (DL) brings revolutionary breakthroughs in many conventional machine learning and pattern recognition tasks such as image classification. The accompanied data-driven modeling paradigm also provides transformative methodology shift in materials science, from trial-and-error routine to intelligent material discovery and analysis. This review systematically summarize the application of DL in material science, based on a model selection perspective for both natural materials and metamaterials. The review aims to uncover the logic behind data-model relation with emphasis on suitable data structures for different scenarios in the material study and the corresponding problem-solving DL model architectures.

The Role of Artificial Intelligence Algorithms in Marine Scientific Research