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CFNCs with a high-curvature 3D hollow structure and rich interfaces enable activation of –CF and –CF2 groups, which results in cathode capacity of 1056 mAh g−1 and energy density up to 2487 Wh kg−1 beyond the theoretical limits of the Li-CFx system.

To evaluate the safety and efficacy of Neuroform EZ stent placement for patients with symptomatic atherosclerotic stenosis of the middle cerebral artery (MCA).We retrospectively reviewed the clinical data of 70 patients (36 males and 34 females; mean age: 62.5 ± 1.25 years) with symptomatic atherosclerotic stenosis of the MCA who underwent Neuroform EZ stent insertion between January 2018 and June 2020. We reviewed the clinical data of each patient so that we could evaluate outcomes and angiographic findings at follow-up.The technical success rate for the 70 patients was 100%. The mean rate of stenosis improved from 82.6 ± 6.5% (pre-stenting) to 21.11 ± 2.6% (post-stenting). During the post-stenting follow-up period, the 1-year frequencies of stroke, transient ischemic attack, and death, were all 0%. 42 patients were reviewed in hospital by DSA or CTA. Of the patients undergoing DSA or CTA review, four patients were found to have in-stent restenosis.Our analysis indicates that the Neuroform EZ stent represents a valuable endovascular treatment option for patients with severe stenosis of the MCA. Additional studies are now needed to evaluate the long-term outcomes arising from the use of this technique.

Sodium-ion batteries (SIBs) with fast-charge capability and long lifespan could be applied in various sustainable energy storage systems, from personal devices to grid storage. Inspired by the disordered Rubik's cube, here, we report that the high-entropy (HE) concept can lead to a very substantial improvement in the sodium storage properties of hexacyanoferrate (HCF). An example of HE-HCF has been synthesized as a proof of concept, which has achieved impressive cycling stability over 50 000 cycles and an outstanding fast-charging capability up to 75 C. Remarkable air stability and all-climate performance are observed. Its quasi-zero-strain reaction mechanism and high sodium diffusion coefficient have been measured and analyzed by multiple in situ techniques and density functional theory calculations. This strategy provides new insights into the development of advanced electrodes and provides the opportunity to tune electrochemical performance by tailoring the atomic composition.

Despite decades of intensive effort, the current solutions for efficiently searching high-dimensional data spaces are not entirely satisfactory. This paper proposes a more efficient global low-density locality sensitive hashing search algorithm (GLDH) based on the minimal cut hyperplane and ensemble learning. The innovation is that a novel global low-density hyperplane candidate set is constructed by the graph cut method, the minimum information gain method and random maximum entropy method are used to greedily select the hyperplane, and the ensemble learning method is used to query the global approximate nearest-neighbors data. This paper proves that the GLDH algorithm produces a low error hyperplane partition. The results of extensive experiments show that the proposed GLDH method performs better than the latest methods when using the same hash coding length for datasets from different fields.

In targeting the most important energy and environmental issues in current society, the development of low-cost, bifunctional electrocatalysts for urea-assisted electrocatalytic hydrogen (H2) production is an urgent and challenging task. In this work, interlaced rosette-like MoS2/Ni3S2/NiFe-layered double hydroxide/nickel foam (LDH/NF) is successfully synthesized by a two-step hydrothermal reaction. Due to its unique interlaced heterostructure, MoS2/Ni3S2/NiFe-LDH/NF exhibits excellent bifunctional catalytic activity towards the urea oxidation reaction (UOR) and the hydrogen evolution reaction (HER) in 1.0 M KOH with 0.5 M urea. In a concurrent two-electrode electrolyser (MoS2/Ni3S2/NiFe-LDH/NF(+,-)), only voltage of 1.343 V is required to reach 50 mA cm−2, which is 216 mV lower than for pure water splitting. Furthermore, after 16 h of urea electrolysis in 1.0 M KOH with 0.5 M urea, the current density remains at 98% of the original value. Thus, the catalyst is not only favorable for H2 production, but also has great significance for the problem of urea-rich wastewater treatment.

Prussian blue analogues (PBAs) have attracted wide attention for their application in the energy storage and conversion field due to their low cost, facile synthesis, and appreciable electrochemical performance. At the present stage, most research on PBAs is focused on their material-level optimization, whereas their properties in practical battery systems are seldom considered. This review aims to first provide an overview of the history and parameters of PBA materials and analyze the fundamental principles toward rational design of PBAs, and then evaluate the prospects and challenges for PBAs for practical sodium-ion batteries, hoping to bridge the gap between laboratory research and commercial reality.

Developing an excellent bifunctional catalyst is essential for the commercial application of Li-O

To reach a closed-loop material system and meet the urgent requirement of sustainable energy storage technologies, it is essential to incorporate efficient waste management into designing new energy storage materials. Here, a “two birds with one stone” strategy to transform rusty iron products into Prussian blue as high-performance cathode materials, and recover the rusty iron products to their original status, is reported. Owing to the high crystalline and Na+ content, the rusty iron derived Prussian blue shows a high specific capacity of 145 mAh g−1 and excellent cycling stability over 3500 cycles. Through the in situ X-ray diffraction and in situ Raman spectra, it is found that the impressive ion storage capability and stability are strongly related to the suppressed structure distortion during the charge/discharge process. The ion migration mechanism and the possibility to serve as a universal host for other kinds of ions are further illuminated by density functional theory calculations. This work provides a new strategy for recycling wasted materials into high value-added materials for sustainable battery systems, and is adaptable in the nanomedicine, catalysis, sensors, and gas storage applications.

The investigation of novel growth mechanisms for electrodes and the understanding of their in situ energy storage mechanisms remains major challenges in rechargeable lithium‐ion batteries. Herein, a novel mechanism for the growth of high‐purity diversified Li3VO4 nanostructures (including hollow nanospheres, uniform nanoflowers, dispersed hollow nanocubes, and ultrafine nanowires) has been developed via a microwave irradiation strategy. In situ synchrotron X‐ray diffraction and in situ transmission electron microscope observations are applied to gain deep insight into the intermediate Li3+ x VO4 and Li3+ y VO4 phases during the lithiation/delithiation mechanism. The first‐principle calculations show that lithium ions migrate into the nanosphere wall rapidly along the (100) plane. Furthermore, the Li3VO4 hollow nanospheres deliver an outstanding reversible capacity (299.6 mAh g−1 after 100 cycles) and excellent cycling stability (a capacity retention of 99.0% after 500 cycles) at 200 mA g−1. The unique nanostructure offers a high specific surface area and short diffusion path, leading to fast thermal/kinetic reaction behavior, and preventing undesirable volume expansion during long‐term cycling., In‐situ transmission electron microscope (TEM) and in situ synchrotron X‐ray diffraction (SXRD) of Li3VO4 hollow nanospheres observation were applied to gain deep insight into the intermediate Li3+ x VO4 and Li3+ y VO4 phases during the lithiation/delithiation mechanism.

Background Anti-N-methyl-d-aspartate (NMDA) receptor encephalitis is an autoimmune disorder characterized by complex neuropsychiatric syndromes during disease onset. Although this disease has been well documented in the last decade, clinical characteristics of anti-NMDA receptor encephalitis in patients with long-term diagnostic history of mental disorders remain unclear. Methods Here, we reviewed and analyzed series of anti-NMDA receptor encephalitis patients with a long-term medical history of psychiatric disorders through a review of literature using PubMed, web of science and Embase database. In addition, we described a patient of anti-NMDA receptor encephalitis with a long-term history of major depressive disorder. Results A total of 14 patients with anti-NMDA receptor encephalitis and a long-term history of mental disorders were included in our study. We found that most patients were adult (92.9%) and female (78.6%). These patients often first visited a psychiatric department (71.43%). The mean disease course of psychiatric disorders was more than 9 years. Speech impairment (71.4%), abnormal behaviors (64.3%), and catatonia (64.3%) were the most common clinical symptoms. Most patients (85.7%) had a satisfactory prognosis after immunotherapy. Conclusion Anti-NMDA receptor encephalitis in individuals with mental disorders is an underestimated condition, yet it presents complex clinical symptoms. Mental and behavioral impairments are more frequently observed in newly diagnosed anti-NMDA receptor encephalitis patients with a long-term history of mental disorders than those without mental illness. A diagnosis of anti-NMDA receptor encephalitis should be considered when patients with mental illness show sudden fluctuations in psychiatric symptoms.

Growth of dendrites, the low plating/stripping efficiency of Zn anodes, and the high freezing point of aqueous electrolytes hinder the practical application of aqueous Zn-ion batteries. Here, a zwitterionic osmolyte-based molecular crowding electrolyte is presented, by adding betaine (Bet, a by-product from beet plant) to the aqueous electrolyte, to solve the abovementioned problems. Substantive verification tests, density functional theory calculations, and ab initio molecular dynamics simulations consistently reveal that side reactions and growth of Zn dendrites are restrained because Bet can break Zn

With the increasing penetration of renewables, the modern power system is facing emerging stability issues. A prime example of such issues is the subsynchronous oscillation (SSO) phenomenon observed in large-scale wind farms. It has been studied that hydrogen production equipment (HPE) has the potential to smooth the fluctuation in generated power. However, it is still unclear whether the HPE can be used to mitigate dynamic stability problems induced by wind generators. This study is aimed at filling the gap by exploring the capability of HPE to damp the emerging SSO. Firstly, the overall configuration of an MW-scale HPE is presented. Then its electromagnetic transient model is established for system dynamics studies. Next, a supplementary subsynchronous damping control (SSDC) of HPE is proposed to modulate its power absorption at the subsynchronous frequency, which can improve system damping and stabilise SSO. Finally, the effectiveness of the proposed SSO mitigation strategy is verified on the simulation model of a practical wind power system that suffered actual SSO incidents. The results demonstrated that the HPE with properly designed SSDC, along with suitable capacity and location, can efficiently mitigate the SSO, thus offers a new option to improve the dynamic stability of renewable power systems.

Recently, emerging oscillation issues have been caused by subsynchronous interaction (SSI) between voltage source converters (VSCs) based renewable generators and ac/dc grids. To investigate the dynamics of SSI, several impedance models (IMs) of VSC have been developed in previous studies. They are established in different reference frames and consider the entire or just parts of the control systems. However, it is still an unanswered question whether or not the existing IMs are suitable for SSI analysis and to what extent the IMs with different modeling details affect subsynchronous dynamics. To fill in this gap, this paper, first, reviews the existing IMs of VSC and clarifies their relationship. Then, their frequency characteristics are examined so as to select the proper models for the studies of sub-/supersynchronous and high-frequency dynamics. Next, both zero-pole and IM-based SSI analyses are carried out on a simplified system and a real-world wind-farm system that suffered actual SSI incidents. The results indicate that the outer-loop control has a significant impact on the subsynchronous dynamics, and only those IMs taking full-scale controls and frequency-coupling effects into account can reflect SSI accurately. Finally, both modal analysis and electromagnetic transient simulations have been conducted to verify the theoretical results.

Recently, emerging sub-synchronous oscillation (SSO) issues caused by the interaction between power electronic converters and weak AC grids have provoked serious stability concerns. Impedance-based modelling approaches have been widely employed for stability analysis of such interactions. In this study, a sequence-domain frequency-coupled impedance model (FCIM) and the corresponding stability criterion are proposed. First, a fast identification method of the FCIM is proposed, in which the impedance-frequency curves of the FCIMs at multiple frequencies are measured, followed by identification of their transfer function models. Next, the proposed method is applied to model a practical system, where a SSO event once occurred due to the interactions between direct-drive wind turbines and a weak AC grid. The individual FCIMs of wind turbine generators, steam turbine generators and static var generator in this system are derived. After that, an aggregated FCIM is formed by combining the FCIMs without extra coordinate transformation. Then, the aggregated FCIM-based stability analysis is carried out to investigate the interaction between the wind farms and weak AC grids. In the end, the results of FCIM-based stability analysis are verified by time-domain simulations.

Wireless multimedia sensor networks (WMSNs) have been widely used in environmental monitoring, intelligent transportation, and other scenarios; however, their data have high-dimensional, large-scale, and multitype properties, as well as other characteristics. It is technically difficult to construct an appropriate index structure and query strategy while we perform a highly accurate search. This paper proposes a novel locality-sensitive hashing (LSH) fast Johnson–Lindenstrauss transform (FJLT)-fly locality-sensitive hashing (FLSH) algorithm for WMSN Internet of Things search. In this method, the projection method of FJLT and the winner-takes-all feature selection strategy in the fruity FLSH are considered. The method provides a new solution for the nearest neighbor search of high-dimensional data. We also discuss the distance-keeping property of our algorithm, and prove theoretically that the method proposed in this paper has better distance keeping performance than the traditional dimensionality reduction method. The experimental results show that the proposed algorithm has better generalization, accuracy of the search results, and time efficiency when using the Drosophila olfactory nerve to simulate the LSH process. This method effectively solves the problem of the approximate neighbor query of high-dimensional big data and can be effectively applied to search application on WMSN system.

The increasing penetration of renewables into modern power systems is bringing new oscillatory stability concerns. It is of significance to find all oscillatory modes concerned and evaluate components’ participation into each mode, especially the dominant one. To address this issue, a frequency-domain modal analysis method is developed in this paper. It represents the target system with a networked impedance model and works out the oscillatory modes by locating the zeros of the determinant of the loop impedance matrix or nodal admittance matrix. Loop/nodal participation factors, branch observability, and component sensitivity are derived to give insight into the oscillation, including its origin, oscillation path, and contributive components. The proposed method has been conducted on a simple passive circuit and a practical wind power system that experienced actual subsynchronous resonance incidents. Theoretical results as well as electromagnetic transient simulations have verified its effectiveness. The proposed method has the potential to analyze very large power systems with high penetration of renewables.

With the increasing penetration of renewable power generation (RPG) in modern power systems, subsynchronous oscillation (SSO) induced by the interaction between RPG and the grid is posing a great threat to the system stability and equipment safety. To address this issue, this paper proposes a wide-area monitoring and early-warning system of SSO. It incorporates the locally deployed subsynchronous phasor measurement units (S-PMUs) and a centralized data center & subsynchronous dynamics analyzer (DC&SDA). Several key functions have been developed, including: (1) the subsynchronous state estimation (SSE) to form global view or observability of SSO dynamics, (2) the establishment of subsynchronous power & current flow to locate oscillation source/path, and (3) the real-time evaluation of subsynchronous stability using two complementary criteria, one based on the identification of oscillation damping and the other based on the frequency characteristics of the impedance models. The practicality and performance of the proposed system have been demonstrated with electromagnetic transient (EMT) simulations on a practical system that suffered SSO incidents.

The electric vehicle, if accessed in the distribution network disorderly and extensively, will influence the safe, stable, and economic operation of the network, due to the characteristics of its randomness, intermittency, and duality with source and load. Here, the power demand models of different electric vehicles were established by analysing the behaviour of electric vehicle loads attributed to different users, and the spatial-temporal distribution pattern of various electric vehicle loads in the distribution network was predicted with the Monte–Carlo method. Based on this regular pattern, an evaluation method for the adaptability of the distribution network to electric vehicles was proposed. Finally, the influence of electric vehicle loads under different access modes on the static voltage stability margin was tested with the IEEE33 node distribution network.

The lack of suitable cathodes is one of the key reasons that impede the development of aqueous zinc-ion batteries. Because of the inherently unsuitable structure and inferior physicochemical properties, the low-valent V