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Single-wall carbon nanotubes (SWCNTs) have a high aspect ratio, large surface area, good stability and unique metallic or semiconducting electrical conductivity, they are therefore considered a promising candidate for the fabrication of flexible gas sensors that are expected to be used in the Internet of Things and various portable and wearable electronics. In this review, we first introduce the sensing mechanism of SWCNTs and the typical structure and key parameters of SWCNT-based gas sensors. We then summarize research progress on the design, fabrication, and performance of SWCNT-based gas sensors. Finally, the principles and possible approaches to further improving the performance of SWCNT-based gas sensors are discussed.

The rapidly changing and wide dynamic range of combustion temperature in scramjet engines presents a major challenge to existing test techniques. Tunable diode laser absorption spectroscopy (TDLAS) based temperature measurement has the advantages of high sensitivity, fast response, and compact structure. In this invited paper, a temperature measurement method based on the TDLAS technique with a single diode laser was demonstrated. A continuous-wave (CW), distributed feedback (DFB) diode laser with an emission wavelength near 1.4 ?m was used for temperature measurement, which could cover two water vapor (H2O) absorption lines located at 7153.749 cm?1 and 7154.354 cm?1 simultaneously. The output wavelength of the diode laser was calibrated according to the two absorption peaks in the time domain. Using this strategy, the TDLAS system has the advantageous of immunization to laser wavelength shift, simple system structure, reduced cost, and increased system robustness. The line intensity of the two target absorption lines under room temperature was about one-thousandth of that under high temperature, which avoided the measuring error caused by H2O in the environment. The system was tested on a McKenna flat flame burner and a scramjet model engine, respectively. It was found that, compared to the results measured by CARS technique and theoretical calculation, this TDLAS system had less than 4% temperature error when the McKenna flat flame burner was used. When a scramjet model engine was adopted, the measured results showed that such TDLAS system had an excellent dynamic range and fast response. The TDLAS system reported here could be used in real engine in the future.

HighlightsThis review introduces soft electronics for health monitoring assisted by machine learning, and discusses soft materials, physiological signals, and machine learning algorithms in sequence and their relationships.The principles of classic machine learning algorithms and neural network algorithms are summarized and explained by representative examples combining with soft electronics.The potential challenges of soft electronics assisted by machine learning especially in health monitoring field are outlined, and future research directions are outlooked.Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.

An inflammatory response is beneficial to the organism, while an excessive uncontrolled inflammatory response can lead to the nonspecific killing of tissue cells. Therefore, promoting the resolution of inflammation is an important mechanism for protecting an organism suffering from chronic inflammatory diseases. Resolvins are a series of endogenous lipid mediums and have the functions of inhibiting a leukocyte infiltration, increasing macrophagocyte phagocytosis, regulating cytokines, and alleviating inflammatory pain. By promoting the inflammation resolution, resolvins play an irreplaceable role throughout the pathological process of some joint inflammation, neuroinflammation, vascular inflammation, and tissue inflammation. Although a large number of experiments have been conducted to study different subtypes of resolvins in different directions, the differences in the action targets between the different subtypes are rarely compared. Hence, this paper reviews the generation of resolvins, the characteristics of resolvins, and the actions of resolvins under a chronic inflammatory response and clinical translation of resolvins for the treatment of chronic inflammatory diseases.

HighlightsThe developmental history of high-entropy materials and the conceptual origin of “high entropy” is comprehensively reviewed. The preparation methods of various high-entropy electrode materials are comprehensively reviewed. The application properties of various high-entropy electrode materials in electrocatalysis and energy storage are comprehensively reviewed, with a prospective outlook on the future development of such materials. High-entropy materials represent a new category of high-performance materials, first proposed in 2004 and extensively investigated by researchers over the past two decades. The definition of high-entropy materials has continuously evolved. In the last ten years, the discovery of an increasing number of high-entropy materials has led to significant advancements in their utilization in energy storage, electrocatalysis, and related domains, accompanied by a rise in techniques for fabricating high-entropy electrode materials. Recently, the research emphasis has shifted from solely improving the performance of high-entropy materials toward exploring their reaction mechanisms and adopting cleaner preparation approaches. However, the current definition of high-entropy materials remains relatively vague, and the preparation method of high-entropy materials is based on the preparation method of single metal/low- or medium-entropy materials. It should be noted that not all methods applicable to single metal/low- or medium-entropy materials can be directly applied to high-entropy materials. In this review, the definition and development of high-entropy materials are briefly reviewed. Subsequently, the classification of high-entropy electrode materials is presented, followed by a discussion of their applications in energy storage and catalysis from the perspective of synthesis methods. Finally, an evaluation of the advantages and disadvantages of various synthesis methods in the production process of different high-entropy materials is provided, along with a proposal for potential future development directions for high-entropy materials.

Trajectory tracking is a key technology for electrical-driven tracked unmanned vehicles (TUVs), while the control model has a significant impact on tracking performance. To improve trajectory tracking accuracy for a dual-motor-driven TUV, a data-driven model-based predictive control scheme is proposed in this article. First, a vehicle dynamics model based on the Long Short-Term Memory (LSTM) network is developed for a TUV. The vehicle’s motion states in a subsequent time step are predicted using a sequence of history states and control inputs, while the multi-body dynamics model in the TUV platform are utilized for training and validation. Then, a neural network-based model predictive control (NN-MPC) strategy is designed, employing the trained LSTM model as the prediction model within a receding horizon framework to compute the optimal motor torques for trajectory tracking. Unlike existing learning-based MPC approaches that mainly focus on wheeled vehicles, this work investigates a neural network-enhanced MPC for tracked unmanned vehicles with coupled longitudinal–lateral dynamics. The simulation results demonstrate that, compared to a physics-model based MPC strategy, the proposed NN-MPC reduces the root mean square (RMS) values of lateral error and heading error by 12.1% and 7.9% in a medium-speed scenario and by 80% and 14.0% in a high-speed scenario. The field experiment further verifies the practical feasibility of the proposed control scheme.

Periodontitis is a long-term inflammatory illness and a leading contributor to tooth loss in humans. Due to the influence of the anatomic parameters of teeth, such as root bifurcation lesions and the depth of the periodontal pocket, basic periodontal treatment on its own often does not completely obliterate flora microorganisms. As a consequence, topical medication has become a significant supplement in the treatment of chronic periodontitis. Berberine (BBR) has various pharmacological effects, such as hypoglycemic, antitumor, antiarrhythmic, anti-inflammatory, etc. The target of our project is to develop a safe and non-toxic carrier that can effectively release berberine, which can significantly reduce periodontal tissue inflammation, and to investigate whether berberine thermosensitive hydrogel can exert anti-inflammatory and osteogenic effects by modulating phosphatifylinositol-3-kinase/Protein Kinase B (PI3K/AKT) signaling pathway. Consequently, firstly berberine temperature-sensitive hydrogel was prepared, and its characterizations showed that the mixed solution gelated within 3 min under 37 °C with a hole diameter of 10–130 µm, and the accumulation of berberine release amounted to 89.99% at 21 days. CCK-8 and live-dead cell staining results indicated that this hydrogel was not biotoxic, and it is also presumed that the optimum concentration of berberine is 5 µM, which was selected for subsequent experiments. Real-time polymerase chain reaction (qRT-PCR) and Western blotting (WB)results demonstrated that inflammatory factors, as well as protein levels, were significantly reduced in the berberine-loaded hydrogel group, and LY294002 (PI3K inhibitor) could enhance this effect (p < 0.05). In the berberine-loaded hydrogel group, osteogenesis-related factor levels and protein profiles were visibly increased, along with an increase in alkaline phosphatase expression, which was inhibited by LY294002 (p < 0.05). Therefore, berberine thermosensitive hydrogel may be an effective treatment for periodontitis, and it may exert anti-inflammatory and osteogenic effects through the PI3K/AKT signaling pathway.

Penicillium species are ubiquitous in all kinds of environments, and they are of industrial, agricultural and clinical importance. In this study, soil fungal diversity in Southwestern China was investigated, and that of Penicillium turned out to be unexpectedly high. The survey included a total of 179 cultures of the genus isolated from 33 soil samples. Three-locus phylogenetic analyses and morphological comparisons were carried out. The examinations revealed that they belonged to two subgenera (Aspergilloides and Penicillium), 11 sections (Aspergilloides, Canescentia, Citrina, Exilicaulis, Fasciculata, Gracilenta, Lanata-Divaricata, Penicillium, Ramosum, Robsamsonia, and Sclerotiorum), 25 series, and 74 species. Forty-three species were discovered as new to science, and a new series, Simianshanica, was established in sect. Aspergilloides. Additionally, 11 species were recorded for the first time in China. Species isolation frequency and distribution of the group were also discussed.

Facial Expression Recognition (FER) is a research topic of great practical significance. However, existing FER methods still face numerous challenges, particularly in the interaction between spatial and global information, the distinction of subtle expression features, and the attention to key facial regions. This paper proposes a lightweight Global-Aware Spatial (GAS) Attention module, designed to improve the accuracy and robustness of FER. This module extracts global semantic information from the image via global average pooling and fuses it with local spatial features extracted by convolution, guiding the model to focus on regions highly relevant to facial expressions (such as the mouth and eyes). This effectively suppresses background noise and enhances the model’s ability to perceive subtle expression variations. In addition, we further introduce a Squeeze-and-Excitation (SE) Attention module into the dual-branch architecture to adaptively adjust the channel-wise weights of features, emphasizing critical region information and enhancing the model’s discriminative capacity. Based on these improvements, we develop the Ada-DF++ network model. Experimental results show that the improved model achieves test accuracies of 89.21%, 66.14%, and 63.75% on the RAF-DB, AffectNet (7cls), and AffectNet (8cls) datasets, respectively, outperforming current state-of-the-art methods across multiple benchmarks and demonstrating the effectiveness of the proposed approach for FER tasks.