Recent advances on emerging nanomaterials for diagnosis and treatment of inflammatory bowel disease.

Inflammatory bowel disease (IBD) is a chronic idiopathic inflammatory disorder that affects the entire gastrointestinal tract and is associated with an increased risk of colorectal cancer. Mainstream clinical testing methods are time-consuming, painful for patients, and insufficiently sensitive to detect early symptoms. Currently, there is no definitive cure for IBD, and frequent doses of medications with potentially severe side effects may affect patient response. In recent years, nanomaterials have demonstrated considerable potential for IBD management due to their diverse structures, composition, and physical and chemical properties. In this review, we provide an overview of the advances in nanomaterial-based diagnosis and treatment of IBD in recent five years. Multi-functional bio-nano platforms, including contrast agents, near-infrared (NIR) fluorescent probes, and bioactive substance detection agents have been developed for IBD diagnosis. Based on a series of pathogenic characteristics of IBD, the therapeutic strategies of antioxidant, anti-inflammatory, and intestinal microbiome regulation of IBD based on nanomaterials are systematically introduced. Finally, the future challenges and prospects in this field are presented to facilitate the development of diagnosis and treatment of IBD.

Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm.

Traditional fingerprint localization algorithms need help with low localization accuracy, large data volumes, and device dependence. This paper proposes a LoRa-based improved fingerprint localization algorithm-particle swarm optimization-random forest-fingerprint localization for indoor localization. The first improvement step involves creating a new exceptional fingerprint value (referred to as RSSI-RANGE) by adding the Time of Flight ranging value (referred to as RANGE) to the Received Signal Strength Indication (RSSI) value and weighting them together. The second improvement step involves preprocessing the fingerprint data to eliminate gross errors using Gaussian and median filtering. After noise reduction, the particle swarm optimization algorithm is used to optimize the hyper parameters of the random forest algorithm, and the best RSSI-RANGE value is obtained using the random forest algorithm. The Kriging method is then used for interpolation to establish an offline fingerprint database, and the final online recognition and localization are performed. Experimental results demonstrate that the first improvement step improves localization accuracy by 53-57% in different experimental scenarios, while the second improves localization accuracy by 25-31%. When both steps are combined, the localization accuracy is improved by 58-63%. The effectiveness of this method is demonstrated through experiments.

Recent advances on nanomaterials for antibacterial treatment of oral diseases.

An imbalance of bacteria in oral environment can lead to a variety of oral diseases, such as periodontal disease, dental caries, and peri-implant inflammation. In the long term, in view of the increasing bacterial resistance, finding suitable alternatives to traditional antibacterial methods is an important research today. With the development of nanotechnology, antibacterial agents based on nanomaterials have attracted much attention in dental field due to their low cost, stable structures, excellent antibacterial properties and broad antibacterial spectrum. Multifunctional nanomaterials can break through the limitations of single therapy and have the functions of remineralization and osteogenesis on the basis of antibacterial, which has made significant progress in the long-term prevention and treatment of oral diseases. In this review, we have summarized the applications of metal and their oxides, organic and composite nanomaterials in oral field in recent five years. These nanomaterials can not only inactivate oral bacteria, but also achieve more efficient treatment and prevention of oral diseases by improving the properties of the materials themselves, enhancing the precision of targeted delivery of drugs and imparting richer functions. Finally, future challenges and untapped potential are elaborated to demonstrate the future prospects of antibacterial nanomaterials in oral field.

Influence of the Reference Electrode on the Performance of Single-Electrode Triboelectric Nanogenerators and the Optimization Strategies.

Owing to their unique advantages, single-electrode triboelectric nanogenerators (SETENGs) have gained wide attention and have been applied in myriad areas, especially in the burgeoning flexible/wearable electronics. However, there is still a lack of a clear understanding of SETENGs. For example, previous simulation models generally put the reference electrode perpendicularly below the working part, but in practice, the reference electrode is designed in various scenarios and noticeable differences in outputs often occur when the reference electrode changes. With SETENGs developing towards wearability and portability, its reference electrode is often required to be constructed inside the device. Consequently, to achieve optimum performance, it is essential to understand the reference electrode's influence on the outputs. Here, the influence of the reference electrode on the performance of SETENGs is systematically investigated and the targeted optimization strategies are thoroughly revealed. First, theoretical simulations are conducted to investigate the reference electrode's effect on the performance of SETENGs with different structures and in various working modes. Secondly, the theoretical results are certified through corresponding experiments. Based on the results, the targeted optimization strategies for SETENGs are comprehensively demonstrated. This work provides fundamental guidance for the development of TENGs and the design and fabrication of new electronic devices.

Wireless Sensing Technology Combined with Facial Expression to Realize Multimodal Emotion Recognition.

Emotions significantly impact human physical and mental health, and, therefore, emotion recognition has been a popular research area in neuroscience, psychology, and medicine. In this paper, we preprocess the raw signals acquired by millimeter-wave radar to obtain high-quality heartbeat and respiration signals. Then, we propose a deep learning model incorporating a convolutional neural network and gated recurrent unit neural network in combination with human face expression images. The model achieves a recognition accuracy of 84.5% in person-dependent experiments and 74.25% in person-independent experiments. The experiments show that it outperforms a single deep learning model compared to traditional machine learning algorithms.

Flexible temperature sensors based on carbon nanomaterials.

Flexible temperature sensors can be attached to the surface of human skin or curved surfaces directly for continuous and stable data measurements, and have attracted extensive attention in myriad areas. Carbon nanomaterials possess great potential for temperature sensing, and flexible temperature sensors based on carbon nanomaterials have demonstrated unique advantages such as high sensitivity, fast response, good mechanical adaptability, low-cost fabrication processes, high cycling stability and reliability. In this review, the working mechanisms, device structures, material compositions, fabrication technologies, temperature sensing properties, the crucial roles of carbon nanomaterials, specific advantages and existing limitations of different types of flexible temperature sensors based on carbon nanomaterials are comprehensively elaborated and discussed. Based on recent advances, conclusions are made and challenges as well as future perspectives are systematically outlined and discussed.

Recent Progress in Self-Powered Skin Sensors.

Self-powered skin sensors have attracted significant attention in recent years due to their great potential in medical care, robotics, prosthetics, and sports. More importantly, self-powered skin sensors do not need any energy-supply components like batteries, which allows them to work sustainably and saves them the trouble of replacement of batteries. The self-powered skin sensors are mainly based on energy harvesters, with the device itself generating electrical signals when triggered by the detected stimulus or analyte, such as body motion, touch/pressure, acoustic sound, and chemicals in sweat. Herein, the recent research achievements of self-powered skin sensors are comprehensively and systematically reviewed. According to the different monitoring signals, the self-powered skin sensors are summarized and discussed with a focus on the working mechanism, device structure, and the sensing principle. Based on the recent progress, the key challenges that exist and the opportunities that lie ahead are also discussed.