Hunan University of Technology

Surface functionalized magnetic iron oxide nanoparticles (NPs) are a kind of novel functional materials, which have been widely used in the biotechnology and catalysis. This review focuses on the recent development and various strategies in preparation, structure, and magnetic properties of naked and surface functionalized iron oxide NPs and their corresponding application briefly. In order to implement the practical application, the particles must have combined properties of high magnetic saturation, stability, biocompatibility, and interactive functions at the surface. Moreover, the surface of iron oxide NPs could be modified by organic materials or inorganic materials, such as polymers, biomolecules, silica, metals, etc. The problems and major challenges, along with the directions for the synthesis and surface functionalization of iron oxide NPs, are considered. Finally, some future trends and prospective in these research areas are also discussed.

Tissue engineering has become a promising strategy for repairing damaged cartilage and bone tissue. Among the scaffolds for tissue-engineering applications, injectable hydrogels have demonstrated great potential for use as three-dimensional cell culture scaffolds in cartilage and bone tissue engineering, owing to their high water content, similarity to the natural extracellular matrix (ECM), porous framework for cell transplantation and proliferation, minimal invasive properties, and ability to match irregular defects. In this review, we describe the selection of appropriate biomaterials and fabrication methods to prepare novel injectable hydrogels for cartilage and bone tissue engineering. In addition, the biology of cartilage and the bony ECM is also summarized. Finally, future perspectives for injectable hydrogels in cartilage and bone tissue engineering are discussed.

Graphene exhibits unique 2-D structure and exceptional phyiscal and chemical properties that lead to many potential applications. Among various applications, biomedical applications of graphene have attracted ever-increasing interests over the last three years. In this review, we present an overview of current advances in applications of graphene in biomedicine with focus on drug delivery, cancer therapy and biological imaging, together with a brief discussion on the challenges and perspectives for future research in this field.

The free-weighting matrix and integral-inequality methods are widely used to derive delay-dependent criteria for the stability analysis of time-varying-delay systems because they avoid both the use of a model transformation and the technique of bounding cross terms. This technical note presents a new integral inequality, called a free-matrix-based integral inequality, that further reduces the conservativeness in those methods. It includes well-known integral inequalities as special cases. Using it to investigate the stability of systems with time-varying delays yields less conservative delay-dependent stability criteria, which are given in terms of linear matrix inequalities. Two numerical examples demonstrate the effectiveness and superiority of the method.

Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied in a variety of fields. In particular, the Internet of Healthcare Things (IoHT) is becoming increasingly important in human activity recognition (HAR) due to the rapid development of wearable and mobile devices. In this article, we focus on the deep-learning-enhanced HAR in IoHT environments. A semisupervised deep learning framework is designed and built for more accurate HAR, which efficiently uses and analyzes the weakly labeled sensor data to train the classifier learning model. To better solve the problem of the inadequately labeled sample, an intelligent autolabeling scheme based on deep Q-network (DQN) is developed with a newly designed distance-based reward rule which can improve the learning efficiency in IoT environments. A multisensor based data fusion mechanism is then developed to seamlessly integrate the on-body sensor data, context sensor data, and personal profile data together, and a long short-term memory (LSTM)-based classification method is proposed to identify fine-grained patterns according to the high-level features contextually extracted from the sequential motion data. Finally, experiments and evaluations are conducted to demonstrate the usefulness and effectiveness of the proposed method using real-world data.

Abstract The development of low-cost and long-lasting all-climate cathode materials for the sodium ion battery has been one of the key issues for the success of large-scale energy storage. One option is the utilization of earth-abundant elements such as iron. Here, we synthesize a NASICON-type tuneable Na 4 Fe 3 (PO 4 ) 2 (P 2 O 7 )/C nanocomposite which shows both excellent rate performance and outstanding cycling stability over more than 4400 cycles. Its air stability and all-climate properties are investigated, and its potential as the sodium host in full cells has been studied. A remarkably low volume change of 4.0% is observed. Its high sodium diffusion coefficient has been measured and analysed via first-principles calculations, and its three-dimensional sodium ion diffusion pathways are identified. Our results indicate that this low-cost and environmentally friendly Na 4 Fe 3 (PO 4 ) 2 (P 2 O 7 )/C nanocomposite could be a competitive candidate material for sodium ion batteries.

Abstract Constructing photocatalytically active and stable covalent organic frameworks containing both oxidative and reductive reaction centers remain a challenge. In this study, benzotrithiophene-based covalent organic frameworks with spatially separated redox centers are rationally designed for the photocatalytic production of hydrogen peroxide from water and oxygen without sacrificial agents. The triazine-containing framework demonstrates high selectivity for H 2 O 2 photogeneration, with a yield rate of 2111 μM h −1 (21.11 μmol h −1 and 1407 μmol g −1 h −1 ) and a solar-to-chemical conversion efficiency of 0.296%. Codirectional charge transfer and large energetic differences between linkages and linkers are verified in the double donor-acceptor structures of periodic frameworks. The active sites are mainly concentrated on the electron-acceptor fragments near the imine bond, which regulate the electron distribution of adjacent carbon atoms to optimally reduce the Gibbs free energy of O 2 * and OOH* intermediates during the formation of H 2 O 2 .

Abstract The design and development of advanced energy storage systems with both high energy/power densities and long cycling life have long been a research hotspot. Zinc‐ion hybrid capacitors (ZICs) are regarded as emerging and highly promising candidates, which originates from the combined advantages of zinc‐ion batteries (ZIBs) with large energy density and supercapacitors (SCs) with exceptional power density and cycle stability. This critical review comprehensively and systematically summarizes the fundamentals and recent advances of ZICs, including their compositions, two types of energy storage mechanisms, advantages and disadvantages of ZICs as well as their electrode materials, electrolytes and new types of devices. Moreover, the present challenges and future research directions of ZICs are proposed, which need further research. This review is expected to provide good guidance for the design and exploitation of high‐performance ZICs to realize their potential practical applications.

With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly discussed topic in digital and intelligent industry field. The security problem existing in the signal processing on large scale of data stream is still a challenge issue in industrial internet of things, especially when dealing with the high-dimensional anomaly detection for intelligent industrial application. In this article, to mitigate the inconsistency between dimensionality reduction and feature retention in imbalanced IBD, we propose a variational long short-term memory (VLSTM) learning model for intelligent anomaly detection based on reconstructed feature representation. An encoder-decoder neural network associated with a variational reparameterization scheme is designed to learn the low-dimensional feature representation from high-dimensional raw data. Three loss functions are defined and quantified to constrain the reconstructed hidden variable into a more explicit and meaningful form. A lightweight estimation network is then fed with the refined feature representation to identify anomalies in IBD. Experiments using a public IBD dataset named UNSW-NB15 demonstrate that the proposed VLSTM model can efficiently cope with imbalance and high-dimensional issues, and significantly improve the accuracy and reduce the false rate in anomaly detection for IBD according to F1, area under curve (AUC), and false alarm rate (FAR).

Abstract A V 4+ -V 2 O 5 cathode with mixed vanadium valences was prepared via a novel synthetic method using VOOH as the precursor, and its zinc-ion storage performance was evaluated. The products are hollow spheres consisting of nanoflakes. The V 4+ -V 2 O 5 cathode exhibits a prominent cycling performance, with a specific capacity of 140 mAh g −1 after 1000 cycles at 10 A g −1 , and an excellent rate capability. The good electrochemical performance is attributed to the presence of V 4+ , which leads to higher electrochemical activity, lower polarization, faster ion diffusion, and higher electrical conductivity than V 2 O 5 without V 4+ . This engineering strategy of valence state manipulation may pave the way for designing high-performance cathodes for elucidating advanced battery chemistry.

Gene transfer methods are promising in the field of gene therapy. Current methods for gene transfer include three major groups: viral, physical and chemical methods. This review mainly summarizes development of several types of chemical methods for gene transfer in vitro and in vivo by means of nano-carriers like; calcium phosphates, lipids, and cationic polymers including chitosan, polyethylenimine, polyamidoamine dendrimers, and poly(lactide-co-glycolide). This review also briefly introduces applications of these chemical methods for gene delivery.

The primary developing trends in flexible and stretchable electronics involve the innovation of material synthesis, mechanical design, and fabrication strategies that employ soft substrates. The biggest challenge is that the entire electronic system must allow not only bending but also stretching. Therefore, stretchable conductors become a crucial construction unit for the connection of working circuits of various stretchable devices. Owing to the success of stretchable conductors, various stretchable electronic devices are fabricated with the help of multiple manufacturing strategies, including stretchable heaters, stretchable energy conversion and storage devices, stretchable transistors, sensors and artificial skin. The continuous development of stretchable electronics has led to the new functionality of transparency, and the fabrication of transparent stretchable electronic devices has gained a lot of interest due to the potential of wearable electronic systems. This review presents technology developments in the preparation of related materials, fabrication strategies and various applications of stretchable electronics. It focuses on the fundamental structural design, mechanisms, and tactics, as well as on challenges and opportunities in the manufacture of stretchable electronic devices and their various applications.

With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial cyber-physical systems (CPS). Intelligent anomaly detection for identifying cyber-physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this article, we propose a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FSL-SCNN can significantly improve false alarm rate (FAR) and F1 scores when detecting intrusion signals for industrial CPS security protection.

Abstract Although some strategies have been triggered to address the intrinsic drawbacks of zinc (Zn) anodes in aqueous Zn‐ion batteries (ZIBs), the larger issue of Zn anodes unable to cycle at a high current density with large areal capacity is neglected. Herein, the zinc phosphorus solid solution alloy (ZnP) coated on Zn foil (Zn@ZnP) prepared via a high‐efficiency electrodeposition method as a novel strategy is proposed. The phosphorus (P) atoms in the coating layer are beneficial to fast ion transfer and reducing the electrochemical activation energy during Zn stripping/plating processes. Besides, a lower energy barrier of Zn 2+ transferring into the coating can be attained due to the additional P. The results show that the as‐prepared Zn@ZnP anode in the symmetric cell can be cycled at a current density of 15 mA cm −2 with an areal capacity of 48 mAh cm −2 (depth of discharge, DOD ≈ 82%) and even at an ultrahigh current density of 20 mA cm −2 and DOD ≈ 51%. Importantly, a discharge capacity of 154.4 mAh g −1 in the Zn/MnO 2 full cell can be attained after 1000 cycles at 1 A g −1 . The remarkable effect achieved by the developed strategy confirms its prospect in the large‐scale application of ZIBs for high‐power devices.

Along with the rapid development of cloud computing, IoT, and AI technologies, cloud video surveillance (CVS) has become a hotly discussed topic, especially when facing the requirement of real-time analysis in smart applications. Object detection usually plays an important role for environment monitoring and activity tracking in surveillance system. The emerging edge-cloud computing paradigm provides us an opportunity to deal with the continuously generated huge amount of surveillance data in an on-site manner across IoT systems. However, the detection performance is still far away from satisfactions due to the complex surveilling environment. In this study, we focus on the multitarget detection for real-time surveillance in smart IoT systems. A newly designed deep neural network model called A-YONet, which is constructed by combining the advantages of YOLO and MTCNN, is proposed to be deployed in an end-edge-cloud surveillance system, in order to realize the lightweight training and feature learning with limited computing sources. An intelligent detection algorithm is then developed based on a preadjusting scheme of anchor box and a multilevel feature fusion mechanism. Experiments and evaluations using two data sets, including one public data set and one homemade data set obtained in a real surveillance system, demonstrate the effectiveness of our proposed method in enhancing training efficiency and detection precision, especially for multitarget detection in smart IoT application developments.

The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.

The rational lifetime-tuning strategy of ultralong organic phosphorescence is extraordinarily important but seldom reported. Herein, a series of multi-host/guest ultralong organic phosphorescence materials with dynamic lifetime-tuning properties were reported. By doping a non-room-temperature phosphorescence emitter into various solid host matrices with continuously reduced triplet energy levels, a wide-range lifetime (from 3.9 ms gradually to 376.9 ms) phosphorescence with unchangeable afterglow colors were realized. Further studies revealed that the host matrices were employed to afford rigid environment and proper energy levels to generate and stabilize the long-live triplet excitons. Meanwhile, these multi-host/guest ultralong organic phosphorescence materials also exhibited excitation-dependent phosphorescence and temperature-controlled afterglow on/off switching properties, according to the virtue of various photophysical and thermal properties of the host matrices. This work provides a guiding strategy to realize lifetime-tuning ultralong organic phosphorescence with lifetime-order encoding characteristic towards widespread applications in time-resolved information displaying, higher-level security protection, and dynamic multi-dimensional anti-counterfeiting.

Abstract Emerging evidence indicates that exosomes derived from gastric cancer cells enhance tumor migration and invasion through the modulation of the tumor microenvironment. However, it remains a major problem to detect cancer‐specific exosomes due to technical and biological challenges. Most of the methods reported could not achieve efficient detection of tumor‐derived exosomes in the background of normal exosomes. Herein, a label‐free electrochemical aptasensor is presented for specific detection of gastric cancer exosomes. This platform contains an anti‐CD63 antibody modified gold electrode and a gastric cancer exosome specific aptamer. The aptamer is linked to a primer sequence that is complementary to a G‐quadruplex circular template. The presence of target exosomes could trigger rolling circle amplification and produce multiple G‐quadruplex units. This horseradish peroxidase mimicking DNAzyme could catalyze the reduction of H 2 O 2 and generate electrochemical signals. This aptasensor exhibits high selectivity and sensitivity toward gastric cancer exosomes with a detection limit of 9.54 × 10 2 mL −1 and a linear response range from 4.8 × 10 3 to 4.8 × 10 6 exosomes per milliliter. Therefore, this electrochemical aptasensor is expected to become a useful tool for the early diagnosis of gastric cancer.

Recently, along with several technological advancements in cyber-physical systems, the revolution of Industry 4.0 has brought in an emerging concept named digital twin (DT), which shows its potential to break the barrier between the physical and cyber space in smart manufacturing. However, it is still difficult to analyze and estimate the real-time structural and environmental parameters in terms of their dynamic changes in digital twinning, especially when facing detection tasks of multiple small objects from a large-scale scene with complex contexts in modern manufacturing environments. In this article, we focus on a small object detection model for DT, aiming to realize the dynamic synchronization between a physical manufacturing system and its virtual representation. Three significant elements, including equipment, product, and operator, are considered as the basic environmental parameters to represent and estimate the dynamic characteristics and real-time changes in building a generic DT system of smart manufacturing workshop. A hybrid deep neural network model, based on the integration of MobileNetv2, YOLOv4, and Openpose, is constructed to identify the real-time status from physical manufacturing environment to virtual space. A learning algorithm is then developed to realize the efficient multitype small object detection based on the feature integration and fusion from both shallow and deep layers, in order to facilitate the modeling, monitoring, and optimizing of the whole manufacturing process in the DT system. Experiments and evaluations conducted in three different use cases demonstrate the effectiveness and usefulness of our proposed method, which can achieve a higher detection accuracy for DT in smart manufacturing.

The blood-brain barrier (BBB) is a critical biological structure that prevents damage to the brain and maintains its bathing microenvironment. However, this barrier is also the obstacle to deliver beneficial drugs to treat CNS (central nervous system) diseases. Many efforts have been made for improvement of delivering drugs across the BBB in recent years to treat CNS diseases. In this review, the anatomical and functional structure of the BBB is comprehensively discussed. The mechanisms of BBB penetration are summarized, and the methods and effects on increasing BBB permeability are investigated in detail. It also elaborates on the physical, chemical, biological and nanocarrier aspects to improve drug delivery penetration to the brain and introduces some specific drug delivery effects on BBB permeability.