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In this work, we combined red phosphorus and hierarchically porous carbon networks by the evaporation–condensation method to obtain a red phosphorus/porous carbon composite with outstanding performance for potassium storage.

Network slicing (NS) management devotes to providing various services to meet distinct requirements over the same physical communication infrastructure and allocating resources on demands. Considering a dense cellular network scenario that contains several NS over multiple base stations (BSs), it remains challenging to design a proper real-time inter-slice resource management strategy, so as to cope with frequent BS handover and satisfy the fluctuations of distinct service requirements. In this paper, we propose to formulate this challenge as a multi-agent reinforcement learning (MARL) problem in which each BS represents an agent. Then, we leverage graph attention network (GAT) to strengthen the temporal and spatial cooperation between agents. Furthermore, we incorporate GAT into deep reinforcement learning (DRL) and correspondingly design an intelligent real-time inter-slice resource management strategy. More specially, we testify the universal effectiveness of GAT for advancing DRL in the multi-agent system, by applying GAT on the top of both the value-based method deep Q-network (DQN) and a combination of policy-based and value-based method advantage actor-critic (A2C). Finally, we verify the superiority of the GAT-based MARL algorithms through extensive simulations.

Transition metal nitrides (TMNs) are predicted to be highly promising electrocatalysts for the nitrogen reduction reaction (NRR) by theoretical calculations. TMNs follow a special Mars-van Krevelen (MvK) mechanism during the NRR, involving surface lattice nitrogen exchange and regeneration. It is hence essential to identify the source of nitrogen, especially taking the stability of lattice nitrogen into consideration. Herein, we investigated the deactivation process of a benchmark CrN nanoparticle catalyst (CrN NPs) with a phase-pure cubic rocksalt (RS) structure for the electrochemical NRR. The current work identifies two possible deactivation pathways for CrN: (i) potential-induced structural collapse of the catalyst including both lattice N leaching and metal dissolution and (ii) ammonia poisoning due to the accumulation and strong chemical bonding of the produced ammonia on the CrN surface. This work provides new perspectives for understanding the structural evolution of nitrogen-containing electrocatalysts for NRR research.

Porous carbon materials doped with atomically dispersed metal sites (ADMSs) are promising electrocatalysts for oxygen reduction reaction (ORR) electrocatalysis. In this work, we fabricated hierarchical porous nitrogen-doped carbon nanofibers with atomically dispersed Fe-N4 sites by carbonization of electrospinning iron-based metal-organic frameworks (MOFs)/polyacrylonitrile nanofibers for ORR electrocatalysis. Remarkably, the resultant carbon nanofibers with atomically dispersed Fe-N4 sites exhibit extraordinary electrochemical performance with an onset potential of 0.994 V and a halfwave potential of 0.876 V in alkaline electrolyte, comparable to the benchmark commercial Pt/C catalyst. The high catalytic performance is originated from the unique hierarchically porous 1D carbon structure and abundant highly active atomically dispersed Fe-N4 sites.

Antiperovskite Co3 InC0.7 N0.3 nanomaterials with highly enhanced oxygen reduction reaction (ORR) performance were prepared by tuning nitrogen contents through a metal-organic framework (MOF)-derived strategy. The nanomaterial surpasses all reported noble-metal-free antiperovskites and even most perovskites in terms of onset potential (0.957 V at J=0.1 mA cm-2 ) and half-wave potential (0.854 V). The OER and zinc-air battery performance demonstrate its multifunctional oxygen catalytic activities. DFT calculation was performed and for the first time, a 4 e- dissociative ORR pathway on (200) facets of antiperovskite was revealed. Free energy studies showed that nitrogen substitution could strengthen the OH desorption as well as hydrogenation that accounts for the enhanced ORR performance. This work expands the scope for material design via tailoring the nitrogen contents for optimal reaction free energy and hence performance of the antiperovskite system.

Solid-state batteries with metallic anodes have attracted great attention due to their high energy density and safety. As an indispensable part of these batteries, solid-state electrolytes (SSEs) with excellent mechanical strength and non-flammability play a significant role in suppressing the growth of dendrites and eliminating the risk of short circuits, whose development could greatly promote the overall battery performance. Recently, metal–organic frameworks (MOFs), a type of porous crystalline inorganic–organic material, have shown potential for the fabrication of high-performance SSEs, which have become an emerging research direction. Benefiting from the rich porosity, controllable functionality and modularity, MOFs not only offer great opportunities for manipulating the physicochemical and electrochemical properties of SSEs, but also provide ideal platforms for investigating the underlying mechanisms of ion conduction and the structure–property relationships. In this perspective, the development of MOF-based SSEs, which include MOF-incorporated polymer hybrids, ionic liquid-laden MOF hybrids, and neat MOFs as SSEs, is outlined. By discussing the pioneering works, both the opportunities and challenges in each SSE category are presented. Additionally, some design principles for MOFs and MOF-based SSEs, as well as the future directions for further development are provided.

Transition-metal-based phosphides (TMPs) have been considered as attractive electrocatalysts for water splitting due to their earth-abundance and remarkable catalytic activity. As a representative type of precursors, metal-organic frameworks (MOFs) provide ideal plateaus for the design of nanostructured TMPs. In this work, the hierarchically structured iron phosphide nanobundles (FeP-500) were fabricated by one-step phosphorization of an iron-based MOF (MET(Fe)) precursor. The derived FeP-500 nanobundles were constructed by quasi-paralleled one-dimensional nanorods with uneven surface, which provided channels for electrolyte penetration, mass transport, and effective exposure of active sites during the water-splitting process. With the addition of conductive Super P, the obtained FeP-500-S exhibited a good electrocatalytic performance towards the hydrogen evolution reaction in alkaline electrolyte (1 mol L-1 KOH). Furthermore, to investigate the influence of secondary metal doping, a series of isoreticular MOF precursors and bimetallic TMPs were fabricated. The results indicated that the catalytic performance is structure dominated.

Nitrogen-doped carbon materials with abundant defects and strong potassium adsorption ability have been recognized as potential anodes for potassium ion batteries (PIBs). However, the limited content and uncontrolled type of nitrogen-doped sites hinder the further performance improvement of PIBs. Herein, this work proposes nitrogen phosphorous co-doped hollow carbon nanofibers (PNCNFs) derived from high-energy metal-organic frameworks (MOFs) with an ultra-high nitrogen content of 19.52 wt% and a high portion of more reactive pyridinic N sites. Furthermore, the highly open architecture exploded by released gases from high-energy MOFs provides sufficient edge sites to settle the N atoms and further form pyridinic N sites induced by phosphorous dopants. The resulting PNCNFs achieve excellent potassium ion storage performance with high reversible capacity (466.2 mAh g

An electronic "smart" contact lens device with high gas permeability and optical transparency, as well as mechanical compliance and robustness, offers daily wear capability in eye interfacing and can have broad applications ranging from ocular diagnosis to augmented reality. Most existing contact lens electronics utilize gas-impermeable substrates, electronic components, and interfacial adhesion layers, which impedes them from applications requiring continuous daily wear. Here we report on the design and fabrication of an eye interfacing device with a commercial ocular contact lens as the substrate, metal-coated nanofiber mesh as the conductor, and

Transition metal and heteroatom doping porous carbon exhibits a promising perspective as non-noble metal electrocatalyst. In this work, we develop a novel and scalable strategy to synthesize hierarchically porous nitrogen-doped carbon nanofibers embedded with abundant Fe3C nanoparticles from continuous electrospun of iron-based metal organic frameworks (MOFs) and polyacrylonitrile nanofibrous precursors. The as-prepared Fe3C/NCNFs (NCNFs for nitrogen-doped carbon nanofibers) exhibit high graphitization degree, high specific surface area, hierarchically meso-microporous structures and well-dispersion of carbon-shielded ultrafine Fe3C nanoparticles. The Fe3C/NCNFs exhibit superior electrocatalytic performance towards oxygen reduction reaction with an onset potential of 1.012 V and a half-wave potential of 0.873 V in alkaline media, exceeding the benchmark commercial Pt/C catalyst by ca. 13 and 46 mV, respectively. Moreover, the onset and half-wave potentials of Fe3C/NCNF are 0.832 and 0.664 V in acid media, respectively, which are also comparable to commercial Pt/C catalyst. The dramatically improved electrocatalytic performance is attributed to the continuous one-dimensional (1D) structure of Fe3C/NCNFs enhancing electro-conductivity compared with simple MOF-derived carbon. Our study demonstrates the Fe3C/NCNF nanofibers a promising candidate for efficient, robust electrocatalyst for economic platinum-free fuel cells.

3D carbon-based materials with multiscale hierarchy are promising electrode materials for electrochemical energy storage and conversion applications, but the synthesis in an efficient and large-scale way is still a great challenge. Herein, a carbon nanorod-assembled 3D superstructure is facilely fabricated by morphology-preserving conversion of a metal-organic framework (MOF) nanorod-assembled superstructure. The MOF superstructure can be fabricated in one-pot synthesis with high reproducibility and high yield by precise control of the MOF nucleation and growth. Its derived carbon inherits the nanorod-assembled superstructure and possesses abundant micropores and nitrogen doping, which can serve as a high-performance anode material for fast potassium storage. The superiority of the superstructure and the synergism of micropore capturing and nitrogen anchoring are verified both experimentally and theoretically.

A personalized wireless wearable system for measuring environmental information requires small size and light weight for portability. Body energy harvesting (e.g., kinetics, heat) is a promising solution to make the system work without battery, thus increasing the portability. In this paper, a personalized wireless sensing system powered by human body heat via thermal electric energy harvesting prototype is presented. Challenges caused by dynamic voltage supply, dynamic charging rates, and low-energy supply systems are identified and addressed. The experimental results show that this system can power a microcontroller and sensors while enabling wireless transmission of data. The thermoelectric generator with a heat sink is able to provide a steady charging voltage for 30–120 min.

The greater availability of sodium with large storage capacity motivates the fabrication of advanced electrodes materials for sodium ion batteries (SIBs) to promote in potential market of smart appliances. Multiple metals atomic dispersion into carbon framework can enhance the adsorption property of sodium for high performance SIBs. In this work, rationalized atomic/clusters of Fe/Se/Al are dispersed on N-doped amorphous carbon fibers through electrospinning and direct selenization (DS) processes. The developed product of DS-Fe/Se/Al@NC-650 nanofibers are employed as anode materials of SIBs, exhibitinga high specific capacity of 432 mAh g−1 at 100 mA g−1 a desirable cycling stability with a capacity retention of 99 % after 700 cycles. Such an excellent storage capacity initiates through synergistic effects of the uniformly distributed Fe/Se/Al and the interconnected nitrogen-doped carbon network. For the apprehension of capacity and stability of DS-Fe/Se/Al@NC-650 nanofibers for SIBs, the theoretical calculations were carried out and provide strong evidences which proves the electron/ion transport in experimental results.

Gender contains a wide range of information regarding to the characteristics difference between male and female. Successful gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviors. First, this paper introduces the challenge and application for gender classification research. Then, the development and framework of gender classification are described. Besides, we compare these state-of-the-art approaches, including vision-based methods, biological information-based method, and social network information-based method, to provide a comprehensive review in the area of gender classification. In mean time, we highlight the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for the future work., Comment: This paper has been withdrawn by the author due to several literature mistakes

Video surveillance plays an important role in Industrial Internet of Things (IIOT). The entire system is demanded to output useful surveillance information in a timely and accurate manner, thus having high requirements of enough computation and communication resources. In this work, we propose a mobile edge computing (MEC) based video surveillance network for face recognition applications, consisting of multiple camera sensors, relays, and the mobile edge server. Considering the limited computation and communication resources, we make joint decision for task offloading, wireless channel allocation, and image compression rate selection to obtain high average recognition accuracy and low average process delay. We adopt different image recognition algorithms for both camera sensors and the MEC server according to their own computation ability, and utilize the deep Q-network (DQN) algorithm as the decision-making module. Besides, we propose a two-layer hierarchical learning framework, i.e., DQN and layers based on back propagation neural network (DQN+NN) algorithm, to reduce the curse of dimensionality. Experimental results show that both the DQN and DQN+NN algorithms can efficiently handle multiple computation tasks with limited computation and communication resources in intelligence video surveillance scenarios, and the DQN+NN algorithm performs better in terms of the convergence and training efficiency.

Three Co-based isostructural MOF-74-III materials with expanded pores are synthesized, with varied extent of fused benzene rings onto sidechain of same-length ligands to finely tune the pore sizes to 2.6, 2.4, and 2.2 nm. Gas sorption results for these highly mesoporous materials show that alternately arranged fused benzene rings on one side of the ligand could serve as extra anchoring sites for CO2 molecules with π-π interactions, conspicuously enhancing CO2 uptake and CO2 /CH4 and CO2 /N2 selectivity; while more steric hindrance effect towards open CoII sites were imposed by ligands flanked with fused benzene rings on both sides, compromising such extra-sites enhancement. In the catalytic conversion of CO2 with propylene oxide to form propylene carbonate, the as-synthesized MOF-74-III(Co) with desired properties of highly exposed and accessible open CoII centers, large mesopore apertures and multi-interactive sites, demonstrated higher catalytic activity compared with other two MOFs, with benzene rings fused to ligands hampering the functionality of CoII centers as Lewis acid sites. Our results highlight the viability of finely tuning the expanded pores of MOF-74 isostructure and the effect of fused benzene rings as functional groups onto selective CO2 capture and conversion.

Gait analysis is an important process to gauge human motion. Recently, longitudinal gait analysis received much attention from the medical and healthcare domains. The challenge in studies over extended time periods is the battery life. Due to the continuous sensing and computing, wearable gait devices cannot fulfill a full-day work schedule. In this paper, we present an energy-efficient adaptive sensing framework to address this problem. Through presampling for content understanding, a selective sensing and sparsity-based signal reconstruction method is proposed. In particular, we develop and implement the new sensing scheme in a smart insole system to reduce the number of samples, while still preserving the information integrity of gait parameters. Experimental results show the effectiveness of our method in data point reduction. Our proposed method improves the battery life to 10.47 h, while normalized mean square error is within 10%.

Rational design of metal compounds in terms of the structure/morphology and chemical composition is essential to achieve desirable electrochemical performances for fast energy storage because of the synergistic effect between different elements and the structure effect. Here, an approach is presented to facilely fabricate mixed‐metal compounds including hydroxides, phosphides, sulfides, oxides, and selenides with well‐defined hollow nanocage structure using metal–organic framework nanocrystals as sacrificial precursors. Among the as‐synthesized samples, the porous nanocage structure, synergistic effect of mixed metals, and unique phosphide composition endow nickel cobalt bimetallic phosphide (NiCo‐P) nanocages with outstanding performance as a battery‐type Faradaic electrode material for fast energy storage, with ultrahigh specific capacity of 894 C g−1 at 1 A g−1 and excellent rate capability, surpassing most of the reported metal compounds. Control experiments and theoretical calculations based on density functional theory reveal that the synergistic effect between Ni and Co in NiCo‐P can greatly increase the OH− adsorption energy, while the hollow porous structure facilitates the fast mass/electron transport. The presented work not only provides a promising electrode material for fast energy storage, but also opens a new route toward structural and compositional design of electrode materials for energy storage and conversion.

Cognitive radio is a promising technology that deals with the scarcity of radio spectrum. In this paper, we propose a new multi-channel transmission strategy for dynamic spectrum access in cognitive radio network. Starting with the model of spectrum activities, we present the multi-channel transmission strategy which has primary user’s protection mechanism to improve the spectrum efficiency and study its performance under perfect and imperfect sensing. Numerical example results reveal that the performance of the proposed strategy has the superiority with respect to capacity performance of cognitive users and the protection of primary users.

Heterometallic metal–organic frameworks (MOFs) are constructed from two or more kinds of metal ions, while still remaining their original topologies. Due to distinct reaction kinetics during MOF formation, partial distribution of different metals within a single MOF crystal can lead to sophisticated heterogeneous nanostructures. Here, this study reports an investigation of reaction kinetics for different metal ions in a bimetallic MOF system, the ZIF-8/67 (M(2-mIM)2, M = Zn for ZIF-8, and Co for ZIF-67, 2-mIM = 2-methylimidazole), by in situ optical method. Distinct kinetics of the two metals forming single-component MOFs are revealed, and when both Co and Zn ions are present in the starting solution, homogeneous distributions of the two metals are only achieved at high Co/Zn ratio, while at low Co/Zn ratio concentration gradient from Co-rich cores to Zn-rich shells is observed. Further, by adding the two metals in sequence, more sophisticated structures are achieved. Specifically, when Co2+ is added first, ZIF-67@ZIF-8/67 core–shell nanocrystals are achieved with tunable core/shell thickness ratio depending on the time intervals; while when Zn2+ is added first, only agglomerates of irregular shape form due to the weak nucleation ability of Zn2+.

Spectrum sharing offers the opportunity for promising efficiency gains, and it will be a valuable tool in the era of future radios. To address this issue, we develop a multi-user cognitive radio sharing network based on opportunistic spectrum access and propose a new spectrum sharing strategy. The objective of our interest is to obtain the number of secondary users who can coexist peacefully with primary users to improve the utility of licensed spectrum, at the same time maximize the total goodput under the interference temperature and SINR constraints. Through analysis and simulation, the new strategy of spectrum sharing does improve the goodput performance as well as guarantee the Quality of Service (QoS) of primary and secondary users.

Remote monitoring of heart disease provides the means to keep patients under continuous supervision. In this paper, we introduce the design and implementation of a remote monitoring medical system for heart failure prediction and management. The three-part system includes a patient-end for data collection, a medical data center as data storage and analysis, and a doctor-end to diagnosis and intervention. The main objective of the system is to prognose the occurrence risk of heart failure (HF) confirmed by the level of N-terminal prohormone of brain natriuretic peptide (NT-proBNP) based on the changes of the patients’ (systolic and diastolic) blood pressure and body weight that are measured noninvasively in a home environment. The prediction of HF and non-HF patients was achieved by a structured support vector machine (SVM) classification algorithm. With the present system, we also proposed a scoring method to interpret the long-term risk of HF. We demonstrated the efficiency of the system with a pilot clinical study of 34 samples, where the NT-proBNP test was used to help train the prediction model as well as check the prediction results for our system. Results showed an accuracy of 79.4% for predicting HF on day 7 based on daily body weight and blood pressure data acquired over 30 days.

In this paper, we develop a multi-user cognitive radio sharing network based on opportunistic spectrum access, where primary and secondary users can peacefully coexist in the same system to share the spectrum and improve the utility of licensed spectrum. Under this scenario, we introduce two constraints to guarantee their successful transmission, interference temperature limit at the primary users and SINR limit at the secondary users. The objective of our interest is to obtain the number of secondary users who can coexist peacefully with primary users, at the same time maximize the total goodput under the two constraints. Through analysis and simulation, the new strategy of spectrum sharing does improve the goodput performance as well as guarantee the QoS of primary and secondary users.