Wuhan Institute of Technology

All forms of energy follow the law of conservation of energy, by which they can be neither created nor destroyed. Light-to-heat conversion as a traditional yet constantly evolving means of converting light into thermal energy has been of enduring appeal to researchers and the public. With the continuous development of advanced nanotechnologies, a variety of photothermal nanomaterials have been endowed with excellent light harvesting and photothermal conversion capabilities for exploring fascinating and prospective applications. Herein we review the latest progresses on photothermal nanomaterials, with a focus on their underlying mechanisms as powerful light-to-heat converters. We present an extensive catalogue of nanostructured photothermal materials, including metallic/semiconductor structures, carbon materials, organic polymers, and two-dimensional materials. The proper material selection and rational structural design for improving the photothermal performance are then discussed. We also provide a representative overview of the latest techniques for probing photothermally generated heat at the nanoscale. We finally review the recent significant developments of photothermal applications and give a brief outlook on the current challenges and future directions of photothermal nanomaterials.

Dissolution of cellulose with ionic liquids allows the comprehensive utilization of cellulose by combining two major green chemistry principles: using environmentally preferable solvents and bio-renewable feed-stocks. In this paper, the dissolution of cellulose with ionic liquids and its application were reviewed. Cellulose can be dissolved, without derivation, in some hydrophilic ionic liquids, such as 1-butyl-3-methylimidazolium chloride (BMIMCl) and 1-allyl-3-methylimidazolium chloride (AMIMCl). Microwave heating significantly accelerates the dissolution process. Cellulose can be easily regenerated from its ionic liquid solutions by addition of water, ethanol or acetone. After its regeneration, the ionic liquids can be recovered and reused. Fractionation of lignocellulosic materials and preparation of cellulose derivatives and composites are two of its typical applications. Although some basic studies, such as economical syntheses of ionic liquids and studies of ionic liquid toxicology, are still much needed, commercialization of these processes has made great progress in recent years.

Planar supercapacitors have recently attracted much attention owing to their unique and advantageous design for 2D nanomaterials based energy storage devices. However, improving the electrochemical performance of planar supercapacitors still remains a great challenge. Here we report for the first time a novel, high-performance in-plane supercapacitor based on hybrid nanostructures of quasi-2D ultrathin MnO2/graphene nanosheets. Specifically, the planar structures based on the δ-MnO2 nanosheets integrated on graphene sheets not only introduce more electrochemically active surfaces for absorption/desorption of electrolyte ions, but also bring additional interfaces at the hybridized interlayer areas to facilitate charge transport during charging/discharging processes. The unique structural design for planar supercapacitors enables great performance enhancements compared to graphene-only devices, exhibiting high specific capacitances of 267 F/g at current density of 0.2 A/g and 208 F/g at 10 A/g and excellent rate capability and cycling stability with capacitance retention of 92% after 7000 charge/discharge cycles. Moreover, the high planar malleability of planar supercapacitors makes possible superior flexibility and robust cyclability, yielding capacitance retention over 90% after 1000 times of folding/unfolding. Ultrathin 2D nanomaterials represent a promising material platform to realize highly flexible planar energy storage devices as the power back-ups for stretchable/flexible electronic devices.

A piezoresistive sensor based on ultralight and superelastic aerogel is reported to fabricate MXene/reduced graphene oxide (MX/rGO) hybrid 3D structures and utilize their pressure-sensitive characteristics. The MX/rGO aerogel not only combines the rGO’s large specific surface area and the MXene’s (Ti3C2Tx) high conductivity but also exhibits rich porous structure, which leads to performance better than that of single-component rGO or MXene in terms of the pressure sensor. The large nanosheets of rGO can prevent the poor oxidization of MXene by wrapping MXene inside the aerogel. More importantly, the piezoresistive sensor based on the MX/rGO aerogel shows extremely high sensitivity (22.56 kPa–1), fast response time (<200 ms), and good stability over 10 000 cycles. The piezoresistive sensor based on the MX/rGO hybrid 3D aerogel can easily capture the signal below 10 Pa, thus clearly testing the pulse of an adult at random. Based on its superior performance, it also demonstrates potential applications in measuring pressure distribution, distinguishing subtle strain, and monitoring healthy activity.

Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. However, the high proportion of small objects in UAV images and the limited platform resources lead to the low accuracy of most of the existing detection models embedded in UAVs, and it is difficult to strike a good balance between detection performance and resource consumption. To alleviate the above problems, we optimize YOLOv8 and propose an object detection model based on UAV aerial photography scenarios, called UAV-YOLOv8. Firstly, Wise-IoU (WIoU) v3 is used as a bounding box regression loss, and a wise gradient allocation strategy makes the model focus more on common-quality samples, thus improving the localization ability of the model. Secondly, an attention mechanism called BiFormer is introduced to optimize the backbone network, which improves the model's attention to critical information. Finally, we design a feature processing module named Focal FasterNet block (FFNB) and propose two new detection scales based on this module, which makes the shallow features and deep features fully integrated. The proposed multiscale feature fusion network substantially increased the detection performance of the model and reduces the missed detection rate of small objects. The experimental results show that our model has fewer parameters compared to the baseline model and has a mean detection accuracy higher than the baseline model by 7.7%. Compared with other mainstream models, the overall performance of our model is much better. The proposed method effectively improves the ability to detect small objects. There is room to optimize the detection effectiveness of our model for small and feature-less objects (such as bicycle-type vehicles), as we will address in subsequent research.

Since the successful synthesis of the first MXenes, application developments of this new family of two-dimensional materials on energy storage, electromagnetic interference shielding, transparent conductive electrodes and field-effect transistors, and other applications have been widely reported. However, no one has found or used the basic characteristics of greatly changed interlayer distances of MXene under an external pressure for a real application. Here we report a highly flexible and sensitive piezoresistive sensor based on this essential characteristics. An in situ transmission electron microscopy study directly illustrates the characteristics of greatly changed interlayer distances under an external pressure, supplying the basic working mechanism for the piezoresistive sensor. The resultant device also shows high sensitivity (Gauge Factor ~ 180.1), fast response (<30 ms) and extraordinarily reversible compressibility. The MXene-based piezoresistive sensor can detect human being's subtle bending-release activities and other weak pressure.

Nanocomposites of chitosan and graphene oxide are prepared by simple self-assembly of both components in aqueous media. It is observed that graphene oxide is dispersed on a molecular scale in the chitosan matrix and some interactions occur between chitosan matrix and graphene oxide sheets. These are responsible for efficient load transfer between the nanofiller graphene and chitosan matrix. Compared with the pure chitosan, the tensile strength, and Young's modulus of the graphene-based materials are significantly improved by about 122 and 64%, respectively, with incorporation of 1 wt % graphene oxide. At the same time, the elongation at the break point increases remarkably. The experimental results indicate that graphene oxide sheets prefer to disperse well within the nanocomposites.

Food safety is increasingly becoming an important public health issue, as foodborne diseases present a widespread and growing public health problem in both developed and developing countries. The rapid and precise monitoring and detection of foodborne pathogens are some of the most effective ways to control and prevent human foodborne infections. Traditional microbiological detection and identification methods for foodborne pathogens are well known to be time consuming and laborious as they are increasingly being perceived as insufficient to meet the demands of rapid food testing. Recently, various kinds of rapid detection, identification, and monitoring methods have been developed for foodborne pathogens, including nucleic-acid-based methods, immunological methods, and biosensor-based methods, etc. This article reviews the principles, characteristics, and applications of recent rapid detection methods for foodborne pathogens.

This study characterizes the demographic, epidemiologic, and clinical characteristics of hospitalized infants diagnosed with coronavirus disease 2019 infection between December 8, 2019, and February 6, 2020, in China.

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

Exceptional cycling performance of graphite anode in K‐ion batteries is demonstrated with a reversible capacity of 246 mAh g –1 and 89% retention of the initial capacity after 200 cycles. Although the graphite anode experiences huge volume change and worse kinetics during K intercalation/deintercalation, the cycling stability delivered in K‐ion batteries is comparable to that of Li‐ion batteries using the same graphite anode. The combination of excellent electrochemical performance, the abundance and wide availability of K in earth's crust, and the well‐developed technology of the graphite anode make the K‐ion battery very attractive for offering a low cost battery chemistry for large‐scale energy storage applications.

To date, the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has determined 399,600,607 cases and 5,757,562 deaths worldwide. COVID-19 is a serious threat to human health globally. The World Health Organization (WHO) has declared COVID-19 pandemic a major public health emergency. Vaccination is the most effective and economical intervention for controlling the spread of epidemics, and consequently saving lives and protecting the health of the population. Various techniques have been employed in the development of COVID-19 vaccines. Among these, the COVID-19 messenger RNA (mRNA) vaccine has been drawing increasing attention owing to its great application prospects and advantages, which include short development cycle, easy industrialization, simple production process, flexibility to respond to new variants, and the capacity to induce better immune response. This review summarizes current knowledge on the structural characteristics, antigen design strategies, delivery systems, industrialization potential, quality control, latest clinical trials and real-world data of COVID-19 mRNA vaccines as well as mRNA technology. Current challenges and future directions in the development of preventive mRNA vaccines for major infectious diseases are also discussed.

The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set, Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the-art SR approaches.

(1992). Nontrivial Solution of Semilinear Elliptic Equations with Critical Exponent in R. Communications in Partial Differential Equations: Vol. 17, No. 3-4, pp. 407-435.

Quorum sensing is a cell-to-cell communication system that exists widely in the microbiome and is related to cell density. The high-density colony population can generate a sufficient number of small molecule signals, activate a variety of downstream cellular processes including virulence and drug resistance mechanisms, tolerate antibiotics, and harm the host. This article gives a general introduction to the current research status of microbial quorum-sensing systems, focuses on the role of quorum-sensing systems in regulating microbial resistance mechanisms, such as drug efflux pump and microbial biofilm formation regulation, and discusses a new strategy for the treatment of drug-resistant bacteria proposed by using quorum quenching to prevent microbial resistance.

In large-scale applications of portable and wearable electronic devices, high-performance supercapacitors are important energy supply sources. However, since the reliability and stability of supercapacitors are generally destroyed by mechanical deformation and damage during practical applications, the stretchability and self-healability must be exploited for the supercapacitors. Preparing the highly stretchable and self-healable electrodes is still a challenge. Here, we report reduced graphene oxide fiber based springs as electrodes for stretchable and self-healable supercapacitors. The fiber springs (diameters of 295 μm) are thick enough to reconnect the broken electrodes accurately by visual inspection. By wrapping fiber springs with a self-healing polymer outer shell, a stretchable and self-healable supercapacitor is successfully realized. The supercapacitor has 82.4% capacitance retention after a large stretch (100%), and 54.2% capacitance retention after the third healing. This work gave an essential strategy for designing and fabricating stretchable and self-healable supercapacitors in next-generation multifunctional electronic devices.

Feature matching, which refers to establishing reliable correspondence between two sets of features (particularly point features), is a critical prerequisite in feature-based registration. In this paper, we propose a flexible and general algorithm, which is called locally linear transforming (LLT), for both rigid and nonrigid feature matching of remote sensing images. We start by creating a set of putative correspondences based on the feature similarity and then focus on removing outliers from the putative set and estimating the transformation as well. We formulate this as a maximum-likelihood estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. To ensure the well-posedness of the problem, we develop a local geometrical constraint that can preserve local structures among neighboring feature points, and it is also robust to a large number of outliers. The problem is solved by using the expectation-maximization algorithm (EM), and the closed-form solutions of both rigid and nonrigid transformations are derived in the maximization step. In the nonrigid case, we model the transformation between images in a reproducing kernel Hilbert space (RKHS), and a sparse approximation is applied to the transformation that reduces the method computation complexity to linearithmic. Extensive experiments on real remote sensing images demonstrate accurate results of LLT, which outperforms current state-of-the-art methods, particularly in the case of severe outliers (even up to 80%).

Hyaluronic acid has good biocompatibility, biodegradability, and nonimmunogenicity. In addition, it has the ability to recognize specific receptors that are overexpressed on the surface of tumor cells, and cancer drugs can be targeted to the tumor cells to better kill them. Therefore, hyaluronic acid has attracted much attention as drug delivery vehicle. Herein, the application of hyaluronic acid as carrier in drug delivery was analyzed and summarized in detail. It showed that hyaluronic acid would have broad prospects for drug delivery.

], A = Li, K, Na; T = Fe, Co, Ni, Mn, Cu, etc.; M = Fe, Mn, Co, etc.) are a large family of materials with an open framework structure. In recent years, they have been intensively investigated as active materials in the field of energy conversion and storage, such as for alkaline-ion batteries (lithium-ion, LIBs; sodium-ion, NIB; and potassium-ion, KIBs), and as electrochemical catalysts. Nevertheless, few review papers have focused on the intrinsic chemical and structural properties of Prussian blue (PB) and its analogues. In this Review, a comprehensive insight into the PBAs in terms of their structural and chemical properties, and the effects of these properties on their materials synthesis and corresponding performance is provided.

Recent advances in transition metal nitride-based catalysts for efficient electrochemical water splitting have been reviewed.