Guangdong University of Technology

Significance Exceedingly high levels of fine particulate matter (PM) occur frequently in China, but the mechanism of severe haze formation remains unclear. From atmospheric measurements in two Chinese megacities and laboratory experiments, we show that the oxidation of SO 2 by NO 2 occurs efficiently in aqueous media under two polluted conditions: first, during the formation of the 1952 London Fog via in-cloud oxidation; and second, on fine PM with NH 3 neutralization during severe haze in China. We suggest that effective haze mitigation is achievable by intervening in the sulfate formation process with NH 3 and NO 2 emission control measures. Hence, our results explain the outstanding sulfur problem during the historic London Fog formation and elucidate the chemical mechanism of severe haze in China.

Development of efficient and robust electrocatalysts is critical for practical fuel cells. We report one-dimensional bunched platinum-nickel (Pt-Ni) alloy nanocages with a Pt-skin structure for the oxygen reduction reaction that display high mass activity (3.52 amperes per milligram platinum) and specific activity (5.16 milliamperes per square centimeter platinum), or nearly 17 and 14 times higher as compared with a commercial platinum on carbon (Pt/C) catalyst. The catalyst exhibits high stability with negligible activity decay after 50,000 cycles. Both the experimental results and theoretical calculations reveal the existence of fewer strongly bonded platinum-oxygen (Pt-O) sites induced by the strain and ligand effects. Moreover, the fuel cell assembled by this catalyst delivers a current density of 1.5 amperes per square centimeter at 0.6 volts and can operate steadily for at least 180 hours.

We propose a localized peer-to-peer (P2P) electricity trading model for locally buying and selling electricity among plug-in hybrid electric vehicles (PHEVs) in smart grids. Unlike traditional schemes, which transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests. However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party. A localized P2P Electricity Trading system with COnsortium blockchaiN (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading. Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading. Security analysis shows that our proposed PETCON improves transaction security and privacy protection. Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs.

This review summarizes recent progresses in material and structural designs of zinc anodes for high-performance aqueous zinc-ion batteries.

) and occur with extensive temporal (on a daily, weekly, to monthly timescale) and spatial (over a million square kilometers) coverage. Although significant advances have been made in field measurements, model simulations, and laboratory experiments for fine PM over recent years, the causes for severe haze formation have not yet to be systematically/comprehensively evaluated. This review provides a synthetic synopsis of recent advances in understanding the fundamental mechanisms of severe haze formation in northern China, focusing on emission sources, chemical formation and transformation, and meteorological and climatic conditions. In particular, we highlight the synergetic effects from the interactions between anthropogenic emissions and atmospheric processes. Current challenges and future research directions to improve the understanding of severe haze pollution as well as plausible regulatory implications on a scientific basis are also discussed.

Ti-based alloys are finding ever-increasing applications in biomaterials due to their excellent mechanical, physical and biological performance. Nowdays, low modulus β-type Ti-based alloys are still being developed. Meanwhile, porous Ti-based alloys are being developed as an alternative orthopedic implant material, as they can provide good biological fixation through bone tissue ingrowth into the porous network. This paper focuses on recent developments of biomedical Ti-based alloys. It can be divided into four main sections. The first section focuses on the fundamental requirements titanium biomaterial should fulfill and its market and application prospects. This section is followed by discussing basic phases, alloying elements and mechanical properties of low modulus β-type Ti-based alloys. Thermal treatment, grain size, texture and properties in Ti-based alloys and their limitations are dicussed in the third section. Finally, the fourth section reviews the influence of microstructural configurations on mechanical properties of porous Ti-based alloys and all known methods for fabricating porous Ti-based alloys. This section also reviews prospects and challenges of porous Ti-based alloys, emphasizing their current status, future opportunities and obstacles for expanded applications. Overall, efforts have been made to reveal the latest scenario of bulk and porous Ti-based materials for biomedical applications.

Metal atoms dispersed on the oxide supports constitute a large category of single-atom catalysts. In this review, oxide supported single-atom catalysts are discussed about their synthetic procedures, characterizations, and reaction mechanism in thermocatalysis, such as water-gas shift reaction, selective oxidation/hydrogenation, and coupling reactions. Some typical oxide materials, including ferric oxide, cerium oxide, titanium dioxide, aluminum oxide, and so on, are intentionally mentioned for the unique roles as supports in anchoring metal atoms and taking part in the catalytic reactions. The interactions between metal atoms and oxide supports are summarized to give a picture on how to stabilize the atomic metal centers, and rationally tune the geometric structures and electronic states of single atoms. Furthermore, several directions in fabricating single-atom catalysts with improved performance are proposed on the basis of state-of-the-art understanding in metal-oxide interactions.

Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things era to provide massive low-power wireless devices with enhanced computation capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multiantenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time-division multiple access protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamforming at the AP, the central processing unit frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP's total energy consumption subject to the users' individual computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semiclosed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.

Federated learning is an emerging machine learning technique that enables distributed model training using local datasets from large-scale nodes, e.g., mobile devices, but shares only model updates without uploading the raw training data. This technique provides a promising privacy preservation for mobile devices while simultaneously ensuring high learning performance. The majority of existing work has focused on designing advanced learning algorithms with an aim to achieve better learning performance. However, the challenges, such as incentive mechanisms for participating in training and worker (i.e., mobile devices) selection schemes for reliable federated learning, have not been explored yet. These challenges have hindered the widespread adoption of federated learning. To address the above challenges, in this article, we first introduce reputation as the metric to measure the reliability and trustworthiness of the mobile devices. We then design a reputation-based worker selection scheme for reliable federated learning by using a multiweight subjective logic model. We also leverage the blockchain to achieve secure reputation management for workers with nonrepudiation and tamper-resistance properties in a decentralized manner. Moreover, we propose an effective incentive mechanism combining reputation with contract theory to motivate high-reputation mobile devices with high-quality data to participate in model learning. Numerical results clearly indicate that the proposed schemes are efficient for reliable federated learning in terms of significantly improving the learning accuracy.

An intelligent reflecting surface (IRS) can adaptively adjust the phase shifts of its reflecting units to strengthen the desired signal and/or suppress the undesired signal. In this letter, we investigate an IRS-aided secure wireless communication system where a multi-antenna access point (AP) sends confidential messages to a single-antenna user in the presence of a single-antenna eavesdropper. In particular, we consider the challenging scenario where the eavesdropping channel is stronger than the legitimate communication channel and they are also highly correlated in space. We maximize the secrecy rate of the legitimate communication link by jointly designing the AP's transmit beamforming and the IRS's reflect beamforming. While the resultant optimization problem is difficult to solve, we propose an efficient algorithm to obtain high-quality suboptimal solution for it by applying the alternating optimization, and semidefinite relaxation methods. Simulation results show that the proposed design significantly improves the secrecy communication rate for the considered setup over the case without using the IRS, and outperforms a heuristic scheme.

Abstract Piezoelectric‐based catalysis that relies on the charge energy or separation efficiency of charge carriers has attracted significant attention. The piezo‐potential induced by strain or stress can induce a giant electric field, which has been demonstrated to be an effective means for charge energy shifting or transferring electrons and holes. In recent years, intense efforts have been made in this subject, and the research has mainly focussed on two aspects: i) Alteration of surface charge energy by piezo‐potential in piezocatalysis; ii) the separation of photo‐generated charge carriers and the catalytic activity enhancement of an integrated piezoelectric semiconductor or coupled system composed of piezoelectrics and semiconductors. Systematically summarizing the advances of the above two aspects is helpful in the context of deepening understanding of the relevant issues and developing new ideas for piezoelectric‐based catalysis. In this review, a comprehensive summary on piezocatalysis and piezo‐photocatalysis is provided. The charge transfer behaviors and catalytic mechanisms over a large variety of piezocatalysts and piezo‐photocatalysts are systematically analyzed. In addition, the types of mechanical energy, strategies for enhancing piezocatalysis, and the advanced applications of piezocatalysis and piezo‐photocatalysis are discussed. Finally, the promising development directions of piezocatalysis and piezo‐photocatalysis, such as materials, assembly forms, and applications in the future are proposed.

Tremendous efforts have been devoted to the development of electrode materials, electrolytes, and separators of energy-storage devices to address the fundamental needs of emerging technologies such as electric vehicles, artificial intelligence, and virtual reality. However, binders, as an important component of energy-storage devices, are yet to receive similar attention. Polyvinylidene fluoride (PVDF) has been the dominant binder in the battery industry for decades despite several well-recognized drawbacks, i.e., limited binding strength due to the lack of chemical bonds with electroactive materials, insufficient mechanical properties, and low electronic and lithium-ion conductivities. The limited binding function cannot meet inherent demands of emerging electrode materials with high capacities such as silicon anodes and sulfur cathodes. To address these concerns, in this review we divide the binding between active materials and binders into two major mechanisms: mechanical interlocking and interfacial binding forces. We review existing and emerging binders, binding technology used in energy-storage devices (including lithium-ion batteries, lithium-sulfur batteries, sodium-ion batteries, and supercapacitors), and state-of-the-art mechanical characterization and computational methods for binder research. Finally, we propose prospective next-generation binders for energy-storage devices from the molecular level to the macro level. Functional binders will play crucial roles in future high-performance energy-storage devices.

This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and effective imaging.

This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it proposes MOEA/D-M2M, a new version of multiobjective optimization evolutionary algorithm-based decomposition. This proposed algorithm solves these subproblems in a collaborative way. Each subproblem has its own population and receives computational effort at each generation. In such a way, population diversity can be maintained, which is critical for solving some MOPs. Experimental studies have been conducted to compare MOEA/D-M2M with classic MOEA/D and NSGA-II. This letter argues that population diversity is more important than convergence in multiobjective evolutionary algorithms for dealing with some MOPs. It also explains why MOEA/D-M2M performs better.

The drastically increasing volume and the growing trend on the types of data have brought in the possibility of realizing advanced applications such as enhanced driving safety, and have enriched existing vehicular services through data sharing among vehicles and data analysis. Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i.e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources. However, road side units that primarily presume the role of vehicular edge computing servers cannot be fully trusted, which may lead to serious security and privacy challenges for such integrated platforms despite their promising potential and benefits. We exploit consortium blockchain and smart contract technologies to achieve secure data storage and sharing in vehicular edge networks. These technologies efficiently prevent data sharing without authorization. In addition, we propose a reputation-based data sharing scheme to ensure high-quality data sharing among vehicles. A three-weight subjective logic model is utilized for precisely managing reputation of the vehicles. Numerical results based on a real dataset show that our schemes achieve reasonable efficiency and high-level of security for data sharing in VECONs.

Zn metal has been considered as a promising anode material for rechargeable aqueous metal-ion batteries. However, the propensity of dendrite growth during plating restricts its practical applications. Herein we propose an effective, low-cost, and nontoxic electrolyte additive, tetrabutylammonium sulfate (TBA2SO4), as the first example of a cationic surfactant-type electrolyte additive in Zn-ion batteries, which can induce the uniform Zn deposition in both electrode preparation and the battery charge/discharge process. Electrochemical characterizations, in situ optical microscopy observation, along with density functional theory (DFT) calculations reveal the unique zincophobic repulsion mechanism, which results in the minimum addition amount of 0.029 g L–1 compared with other reported additives (at least 1g L–1), demonstrating the great potential for practical application. Excellent cycling performance with dendrite-free morphology at different current densities and discharge depths is achieved for both the symmetric cell and the full cell (coupled to α-MnO2) using the as-prepared 3D Zn anode and the proposed additives.

Constructing heterostructures can endow materials with fascinating performance in high-speed electronics, optoelectronics, and other applications owing to the built-in charge-transfer driving force, which is of benefit to the specific charge-transfer kinetics. Rational design and controllable synthesis of nano-heterostructure anode materials with high-rate performance, however, still remains a great challenge. Herein, ultrafine SnS/SnO2 heterostructures were successfully fabricated and showed enhanced charge-transfer capability. The mobility enhancement is attributed to the interface effect of heterostructures, which induces an electric field within the nanocrystals, giving them much lower ion-diffusion resistance and facilitating interfacial electron transport.

Abstract A competitive complexation strategy has been developed to construct a novel electrocatalyst with Zn‐Co atomic pairs coordinated on N doped carbon support (Zn/CoN‐C). Such architecture offers enhanced binding ability of O 2 , significantly elongates the O−O length (from 1.23 Å to 1.42 Å), and thus facilitates the cleavage of O−O bond, showing a theoretical overpotential of 0.335 V during ORR process. As a result, the Zn/CoN‐C catalyst exhibits outstanding ORR performance in both alkaline and acid conditions with a half‐wave potential of 0.861 and 0.796 V respectively. The in situ XANES analysis suggests Co as the active center during the ORR. The assembled zinc–air battery with Zn/CoN‐C as cathode catalyst presents a maximum power density of 230 mW cm −2 along with excellent operation durability. The excellent catalytic activity in acid is also verified by H 2 /O 2 fuel cell tests (peak power density of 705 mW cm −2 ).

This article reviews the recent development of adaptive dynamic programming (ADP) with applications in control. First, its applications in optimal regulation are introduced, and some skilled and efficient algorithms are presented. Next, the use of ADP to solve game problems, mainly nonzero-sum game problems, is elaborated. It is followed by applications in large-scale systems. Note that although the functions presented in this article are based on continuous-time systems, various applications of ADP in discrete-time systems are also analyzed. Moreover, in each section, not only some existing techniques are discussed, but also possible directions for future work are pointed out. Finally, some overall prospects for the future are given, followed by conclusions of this article. Through a comprehensive and complete investigation of its applications in many existing fields, this article fully demonstrates that the ADP intelligent control method is promising in today's artificial intelligence era. Furthermore, it also plays a significant role in promoting economic and social development.

Unmanned aerial vehicle (UAV) communication is anticipated to be widely applied in the forthcoming fifth-generation wireless networks, due to its many advantages such as low cost, high mobility, and on-demand deployment. However, the broadcast and line-of-sight nature of air-to-ground wireless channels give rise to a new challenge on how to realize secure UAV communications with the destined nodes on the ground. This paper aims to tackle this challenge by applying the physical layer security technique. We consider both the downlink and uplink UAV communications with a ground node, namely, UAV-to-ground (U2G) and ground-to-UAV (G2U) communications, respectively, subject to a potential eavesdropper on the ground. In contrast to the existing literature on the wireless physical layer security only with the ground nodes at fixed or quasi-static locations, we exploit the high mobility of the UAV to proactively establish favorable and degraded channels for the legitimate and eavesdropping links, through its trajectory design. We formulate new problems to maximize the average secrecy rates of the U2G and G2U transmissions, by jointly optimizing the UAV's trajectory, and the transmit power of the legitimate transmitter over a given flight period of the UAV. Although the formulated problems are non-convex, we propose iterative algorithms to solve them efficiently by applying the block coordinate descent and successive convex optimization methods. Specifically, both the transmit power and UAV trajectory are optimized, with the other being fixed in an alternating manner, until the algorithms converge. The simulation results show that the proposed algorithms can improve the secrecy rates for both U2G and G2U communications, as compared to other benchmark schemes without power control and/or trajectory optimization.