Harbin University of Science and Technology

The reactions between peroxymonosulfate (PMS) and quinones were investigated for the first time in this work, where benzoquinone (BQ) was selected as a model quinone. It was demonstrated that BQ could efficiently activate PMS for the degradation of sulfamethoxazole (SMX; a frequently detected antibiotic in the environments), and the degradation rate increased with solution pH from 7 to 10. Interestingly, quenching studies suggested that neither hydroxyl radical (•OH) nor sulfate radical (SO4•-) was produced therein. Instead, the generation of singlet oxygen (1O2) was proved by using two chemical probes (i.e., 2,2,6,6-tetramethyl-4-piperidinol and 9,10-diphenylanthracene) with the appearance of 1O2 indicative products detected by electron paramagnetic resonance spectrometry and liquid chromatography mass spectrometry, respectively. A catalytic mechanism was proposed involving the formation of a dioxirane intermediate between PMS and BQ and the subsequent decomposition of this intermediate into 1O2. Accordingly, a kinetic model was developed, and it well described the experimental observation that the pH-dependent decomposition rate of PMS was first-order with respect to BQ. These findings have important implications for the development of novel nonradical oxidation processes based on PMS, because 1O2 as a moderately reactive electrophile may suffer less interference from background organic matters compared with nonselective •OH and SO4•-.

Trifluorophenyl-functionalized multi-walled-carbon-nanotube/poly(vinylidene fluoride) (TFP-MWNT/PVDF) nanocomposites are fabricated by employing a wet-chemistry route. The modified MWNTs are observed to form a well-dispersed, structurally random nanophase within the polymer matrix (see figure). The TFP-MWNT/PVDF nanocomposite exhibits enhanced dielectric permittivity when the content of TFP-MWNT is close to the percolation threshold.

With the ever-increasing adaption of large-scale energy storage systems and electric devices, the energy storage capability of batteries and supercapacitors has faced increased demand and challenges. The electrodes of these devices have experienced radical change with the introduction of nano-scale materials. As new generation materials, heterostructure materials have attracted increasing attention due to their unique interfaces, robust architectures, and synergistic effects, and thus, the ability to enhance the energy/power outputs as well as the lifespan of batteries. In this review, the recent progress in heterostructure from energy storage fields is summarized. Specifically, the fundamental natures of heterostructures, including charge redistribution, built-in electric field, and associated energy storage mechanisms, are summarized and discussed in detail. Furthermore, various synthesis routes for heterostructures in energy storage fields are roundly reviewed, and their advantages and drawbacks are analyzed. The superiorities and current achievements of heterostructure materials in lithium-ion batteries (LIBs), sodium-ion batteries (SIBs), lithium-sulfur batteries (Li-S batteries), supercapacitors, and other energy storage devices are discussed. Finally, the authors conclude with the current challenges and perspectives of the heterostructure materials for the fields of energy storage.

Optical trapping describes the interaction between light and matter to manipulate micro-objects through momentum transfer. In the case of 3D trapping with a single beam, this is termed optical tweezers. Optical tweezers are a powerful and noninvasive tool for manipulating small objects, and have become indispensable in many fields, including physics, biology, soft condensed matter, among others. In the early days, optical trapping was typically accomplished with a single Gaussian beam. In recent years, we have witnessed rapid progress in the use of structured light beams with customized phase, amplitude, and polarization in optical trapping. Unusual beam properties, such as phase singularities on-axis and propagation invariant nature, have opened up novel capabilities to the study of micromanipulation in liquid, air, and vacuum. We summarize the recent advances in the field of optical trapping using structured light beams.

Abstract Crystalline and porous covalent organic frameworks (COFs) and metal‐organic frameworks (MOFs) materials have attracted enormous attention in the field of photocatalytic H 2 evolution due to their long‐range order structures, large surface areas, outstanding visible light absorbance, and tunable band gaps. In this work, we successfully integrated two‐dimensional (2D) COF with stable MOF. By covalently anchoring NH 2 ‐UiO‐66 onto the surface of TpPa‐1‐COF, a new type of MOF/COF hybrid materials with high surface area, porous framework, and high crystallinity was synthesized. The resulting hierarchical porous hybrid materials show efficient photocatalytic H 2 evolution under visible light irradiation. Especially, NH 2 ‐UiO‐66/TpPa‐1‐COF (4:6) exhibits the maximum photocatalytic H 2 evolution rate of 23.41 mmol g −1 h −1 (with the TOF of 402.36 h −1 ), which is approximately 20 times higher than that of the parent TpPa‐1‐COF and the best performance photocatalyst for H 2 evolution among various MOF‐ and COF‐based photocatalysts.

Asymmetric supercapacitors with high energy density are fabricated using a self‐assembled reduced graphene oxide (RGO)/MnO 2 (GrMnO 2 ) composite as a positive electrode and a RGO/MoO 3 (GrMoO 3 ) composite as a negative electrode in safe aqueous Na 2 SO 4 electrolyte. The operation voltage is maximized by choosing two metal oxides with the largest work function difference. Because of the synergistic effects of highly conductive graphene and highly pseudocapacitive metal oxides, the hybrid nanostructure electrodes exhibit better charge transport and cycling stability. The operation voltage is expanded to 2.0 V in spite of the use of aqueous electrolyte, revealing a high energy density of 42.6 Wh kg −1 at a power density of 276 W kg −1 and a maximum specific capacitance of 307 F g −1 , consequently giving rise to an excellent Ragone plot. In addition, the GrMnO 2 //GrMoO 3 supercapacitor exhibits improved capacitance with cycling up to 1000 cycles, which is explained by the development of micropore structures during the repetition of ion transfer. This strategy for the choice of metal oxides provides a promising route for next‐generation supercapacitors with high energy and high power densities.

Abstract In response to the change of energy landscape, sodium‐ion batteries (SIBs) are becoming one of the most promising power sources for the post‐lithium‐ion battery (LIB) era due to the cheap and abundant nature of sodium, and similar electrochemical properties to LIBs. The electrochemical performance of electrode materials for SIBs is closely bound up with their crystal structures and intrinsic electronic/ionic states. Apart from nanoscale design and conductive composite strategies, heteroatom doping is another effective way to enhance the intrinsic transfer characteristics of sodium ions and electrons in crystal structures to accelerate reaction kinetics and thereby achieve high performance. In this review, the recent advancements in heteroatom doping for sodium ion storage of electrode materials are reviewed. Specifically, different doping strategies including nonmetal element doping (e.g., nitrogen, sulfur, phosphorous, boron, fluorine), metal element doping (magnesium, titanium, iron, aluminum, nickel, copper, etc.), and dual/triple doping (such as N–S, N–P, N–S–P) are reviewed and summarized in detail. Furthermore, various doping methods are introduced and their advantages and disadvantages are discussed. The doping effect on crystal structure and intrinsic electronic/ionic state are illustrated and the relationship with capacity and energy/power density is interrogated. Finally, future development trends in doping strategies for advanced SIBs electrodes are analyzed.

To keep pace with the increasing pursuit of portable and wearable electronics, it is urgent to develop advanced flexible power supplies. In this context, Zn-ion batteries (ZIBs) have garnered increasing attention as favorable energy storage devices for flexible electronics, owing to the high capacity, low cost, abundant resources, high safety, and eco-friendliness. Extensive efforts have been devoted to developing flexible ZIBs in the last few years. This work summarizes the recent achievements in the design, fabrication, and characterization of flexible ZIBs. Representative structures, such as sandwich and cable type, are particularly highlighted. Special emphasis is put on the novel design of electrolyte and electrode, which aims to endow reliable flexibility to the fabricated ZIBs. Moreover, current challenges and future opportunities for the development of high-performance flexible ZIBs are also outlined.

Metamaterials are artificial media structured on a size scale smaller than wavelength of external stimuli, and they can exhibit a strong localization and enhancement of fields, which may provide novel tools to significantly enhance the sensitivity and resolution of sensors, and open new degrees of freedom in sensing design aspect. This paper mainly presents the recent progress concerning metamaterials-based sensing, and detailedly reviews the principle, detecting process and sensitivity of three distinct types of sensors based on metamaterials, as well as their challenges and prospects. Moreover, the design guidelines for each sensor and its performance are compared and summarized.

Imidazole molecules were frequently incorporated into porous materials to improve their proton conductivity. To investigate how different arrangements of imidazoles in metal–organic frameworks (MOFs) affect the overall proton conduction, we designed and prepared a MOF-based model system. It includes an Fe–MOF as the blank, an imidazole@Fe–MOF (Im@Fe–MOF) with physically adsorbed imidazole, and an imidazole–Fe–MOF (Im–Fe–MOF), which contains chemically coordinated imidazole molecules. The parent Fe–MOF, synthesized from the exchange of carboxylates in the preformed [Fe3(μ3–O)](carboxylate)6 clusters and multitopic carboxylate ligands, serves as a control. The Im@Fe–MOF was prepared by encapsulating free imidazole molecules into the pores of the Fe–MOF, whereas the Im–Fe–MOF was obtained in situ, in which imidazole ligands coordinate to the metal nodes of the framework. Proton-conductivity analyses revealed that the proton conductivity of Im–Fe–MOF was approximately two orders of magnitude greater than those of Fe–MOF and Im@Fe–MOF at room temperature. The high proton conductivity of 1.21 × 10–2 S cm–1 at 60 °C for Im–Fe–MOF ranks among the highest performing MOFs ever reported. The results of the density functional theory calculations suggest that coordinated imidazole molecules in Im–Fe–MOF provide a greater concentration of protons for proton transportation than do coordinated water molecules in Fe–MOF alone. Besides, Im–Fe–MOF exhibits steadier performance than Im@Fe–MOF does after being washed with water. Our investigation using the above ideal crystalline model system demonstrates that compared to disorderly arranged imidazole molecules in pores, the immobilized imidazole molecules by coordination bonds in the framework are more prone to form proton–conduction pathways and thus perform better and steadier in water-mediated proton conduction.

Modeling human behaviors and activity patterns for recognition or detection of special event has attracted significant research interest in recent years. Diverse methods that are abound for building intelligent vision systems aimed at scene understanding and making correct semantic inference from the observed dynamics of moving targets. Most applications are in surveillance, video content retrieval, and human-computer interfaces. This paper presents not only an update extending previous related surveys, but also a focus on contextual abnormal human behavior detection especially in video surveillance applications. The main purpose of this survey is to extensively identify existing methods and characterize the literature in a manner that brings key challenges to attention.

Intrusion detection system (IDS) plays an important role in network security by discovering and preventing malicious activities. Due to the complex and time-varying network environment, the network intrusion samples are submerged into a large number of normal samples, which leads to insufficient samples for model training and detection results with a high false detection rate. According to the problem of data imbalance, we propose a network intrusion detection algorithm combined hybrid sampling with deep hierarchical network. Firstly, we use the one-side selection (OSS) to reduce the noise samples in majority category, and then increase the minority samples by Synthetic Minority Over-sampling Technique (SMOTE). In this way, a balanced dataset can be established to make the model fully learn the features of minority samples and greatly reduce the model training time. Secondly, we use convolution neural network (CNN) to extract spatial features and Bi-directional long short-term memory (BiLSTM) to extract temporal features, which forms a deep hierarchical network model. The proposed network intrusion detection algorithm was verified by experiments on the NSL-KDD and UNSW-NB15 dataset, and the classification accuracy can achieve 83.58% and 77.16%, respectively.

This study systematically analyzes the factors that affect consumers' green purchase intention. Through a comprehensive literature review, the influencing factors of consumers' green purchase intention are organized into three categories: cognitive factors, consumer individual characteristics, and social factors. Next, a meta-analysis of 54 empirical papers was conducted using Comprehensive Meta-Analysis 3.0 software to quantitatively assess these relationships. The results revealed that green perceived value, attitude, and green trust have a significant positive influence on green purchase intention. Perceived behavior control, perceived consumer effectiveness, and subjective norm also has a strong positive impact on green purchase intention. Collectivism has a positive effect on green purchase intention. Green perceived risk has a significant negative impact on green purchase intention. The study's findings provide references for enterprises engaged in green product diffusion and organizations responsible for environmental protection.

Abstract Recently, the need for miniaturization and high integration have steered a strong technical wave in developing (micro‐)electronic devices. However, excessive amounts of heat may be generated during operation/charging, severely affecting device performance and leading to life/property loss. Benefiting from their low density, easy processing and low manufacturing cost, thermally conductive polymer composites have become a research hotspot to mitigate the disadvantage of excessive heat, with potential applications in 5G communication, electronic packaging and energy transmission. By far, the reported thermal conductivity coefficient (λ) of thermally conductive polymer composite is far from expectation. Deeper understanding of heat transfer mechanism is desired for developing next generation thermally conductive composites. This review holistically scopes current advances in this field, while giving special attention to critical factors that affect thermal conductivity in polymer composites as well as the thermal conduction mechanisms on how to enhance the λ value. This review covers critical factors such as interfacial thermal resistance, chain structure of polymer, intrinsic λ value of different thermally conductive fillers, orientation/configuration of nanoparticles, 3D interconnected networks, processing technology, etc. The applications of thermally conductive polymer composites in electronic devices are summarized. The existing problems are also discussed, new challenges and opportunities are prospected.

Since the positive influences of defects on the performance of electroceramics were discovered, investigations concerning on defects and aliovalent doping routes have grown rapidly in the fields of inorganic chemistry and condensed matter physics. In this article, we summarized the types of defects in electroceramics as well as characterization tools of defects and highlighted the effects of intrinsic and extrinsic defects on the material performances with the emphasis on dielectric, ferroelectric, and piezoelectric properties. We mainly introduced defect related theoretical simulation and experimental results in several typical incipient ferroelectrics, ferroelectrics, and antiferroelectrics. Hence, the influences of defects on the crystal lattice were summed up, and then the main physical mechanisms were highlighted. Particularly, the performance enhancements of aliovalently doped electroceramics were also evaluated and reviewed. Finally, the outlook and challenges were discussed on the basis of their current developments. This article covers not only an overview of the state-of-the-art advances of defects and aliovalent doping routes in electroceramics but also the future prospects that may open another window to tune the electrical performance of electroceramics via intentionally introducing certain defects.

Abstract This article is concerned with the recursive finite-horizon filtering problem for a class of nonlinear time-varying systems subject to multiplicative noises, missing measurements and quantisation effects. The missing measurements are modelled by a series of mutually independent random variables obeying Bernoulli distributions with possibly different occurrence probabilities. The quantisation phenomenon is described by using the logarithmic function and the multiplicative noises are considered to account for the stochastic disturbances on the system states. Attention is focused on the design of a recursive filter such that, for all multiplicative noises, missing measurements as well as quantisation effects, an upper bound for the filtering error covariance is guaranteed and such an upper bound is subsequently minimised by properly designing the filter parameters at each sampling instant. The desired filter parameters are obtained by solving two Riccati-like difference equations that are of a recursive form suitable for online applications. Finally, two simulation examples are exploited to demonstrate the effectiveness and applicability of the proposed filter design scheme. Keywords: nonlinear systemstime-varying systemsmissing measurementsquantisation effectsmultiplicative noisesriccati-like difference equation Acknowledgements This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61273201, 61104125 and 11271103, National 973 Project under Grant 2009CB320600, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK and the Alexander von Humboldt Foundation of Germany.

Nanodielectrics, which are concentrated in polymer matrix incorporating nanofillers, have received considerable attention due to their potential benefits as dielectrics. In this paper, short-term breakdown and long-term failure properties of nanodielectrics have been reviewed. The characteristics of polymer matrix, types of nanoparticle and its content, and waveforms of the applied voltage are fully evaluated. In order to effectively comment on the published experimental data, a ratio k has been proposed to compare the electric properties of the nanodielectrics with the matrix and assess the effect for nanoparticles doping. There is evidence that the short-term breakdown properties of nanodielectrics show a strong dependence on the applied voltage waveforms. The polarity and the cohesive energy density (CED) of polymer matrix have a dramatic influence on the properties of nanodielectrics. Nanoparticle doped composites show a positive effect on the long-term failure properties, such as ageing resistance and partial discharge (PD) properties of nanocomposites are superior than microcomposites and the matrix. The larger the dielectric constant and CED of the matrix become, the more significant improvements in long-term performance appear. Based on the reported experimental results, we also present our understandings and propose some suggestions for further work.

Chatter is a self-excited vibration of parts in machining systems. It is widely present across a range of cutting processes, and has an impact upon both efficiency and quality in production processing. A great deal of research has been dedicated to the development of technologies that are able to predict and detect chatter. The purpose of these technologies is to facilitate the avoidance of chatter during cutting processes, which leads to better surface precision, higher productivity, and longer tool life. This paper summarizes the current state of the art in research regarding the problems of how to arrive at stable chatter prediction, chatter identification, and chatter control/suppression, with a focus on milling processes. Particular focus is placed on the theoretical relationship between cutting chatter and process damping, tool runout, and gyroscopic effect, as well as the importance of this for chatter prediction. The paper concludes with some reflections regarding possible directions for future research in this field.

We report that vertically aligned ZnO nanowire arrays (ZnO NWAs) were fabricated on 3D graphene foam (GF) and used to selectively detect uric acid (UA), dopamine (DA), and ascorbic acid (AA) by a differential pulse voltammetry method. The optimized ZnO NWA/GF electrode provided a high surface area and high selectivity with a detection limit of 1 nM for UA and DA. The high selectivity in the oxidation potential was explained by the gap difference between the lowest unoccupied and highest occupied molecular orbitals of a biomolecule for a set of given electrodes. This method was further used to detect UA levels in the serum of patients with Parkinson's disease (PD). The UA level was 25% lower in PD patients than in healthy individuals. This finding strongly implies that UA can be used as a biomarker for PD.

Abstract Molybdenum trioxide (MoO 3 ) has recently aroused intensive interest as a renowned conversion‐type anode of Li‐ion batteries (LIBs). Nevertheless, the inferior rate capability, sluggish reaction kinetics, and fast capacity decay during a long‐term charge/discharge process seriously inhibits large‐scale commercial application. Herein, abundant oxygen vacancies and MXene nanosheets are elaborately introduced into MoO 3 nanobelts through hydrazine reduction and electrostatic assembly to accelerate the ionic and electronic diffusion/transport kinetics for LIBs. Benefitting from the accelerated ion diffusion kinetics, enhanced electrical conductivity, and additional active sites induced by oxygen vacancies as well as the robust interfacial contact, the prepared MoO 3− x /MXene heterostructure exhibits excellent lithium‐ion storage performances. First‐principles calculations indicate that the adsorption of Li + ion and the electrical conductivity are significantly enhanced for the MoO 3− x /MXene heterostructure. Thus, the composite exhibits high reversible capacity of 1258 mAh g −1 at 0.1 A g −1 for Li‐ion storage and retains 474 mAh g −1 at 10 A g −1 , remarkably higher than those of the previously reported MoO 3 ‐based anode materials. More importantly, the composite is fabricated with commercial LiFePO 4 into a full LIB, which displays an unparalleled energy density of 330 Wh kg −1 .