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Li‐CO2 batteries could skillfully combine the reduction of “greenhouse effect” with energy storage systems. However, Li‐CO2 batteries still suffer from unsatisfactory electrochemical performances and their rechargeability is challenged. Here, it is reported that a composite of Ni nanoparticles highly dispersed on N‐doped graphene (Ni‐NG) with 3D porous structure, exhibits a superior discharge capacity of 17 625 mA h g−1, as the air cathode for Li‐CO2 batteries. The batteries with these highly efficient cathodes could sustain 100 cycles at a cutoff capacity of 1000 mA h g−1 with low overpotentials at the current density of 100 mA g−1. Particularly, the Ni‐NG cathodes allow to observe the appearance/disappearance of agglomerated Li2CO3 particles and carbon thin films directly upon discharge/charge processes. In addition, the recycle of CO2 is detected through in situ differential electrochemical mass spectrometry. This is a critical step to verify the electrochemical rechargeability of Li‐CO2 batteries. Also, first‐principles computations further prove that Ni nanoparticles are active sites for the reaction of Li and CO2, which could guide to design more advantageous catalysts for rechargeable Li‐CO2 batteries. A composite of Ni nanoparticles highly dispersed on N‐doped graphene is prepared as the air cathode for Li‐CO2 batteries, with high discharge capacity and excellent cyclic stability. The cathode allows to observe the morphological evolution of discharge products directly and reversible consumption and evolution of CO2, and then the reversibility of electrochemical reactions could well be understood in Li‐CO2 batteries.

Surface‐enhanced Raman spectroscopy (SERS), as a nondestructive and ultra‐sensitive single molecular level characterization technique, is a powerful tool to deeply understand the interfacial electrochemistry reaction mechanism involved in energy conversion and storage, especially for oxygen electrochemistry in Li‐O2 batteries with unrivaled theoretical energy density. SERS can provide precise spectroscopic identification of the reactants, intermediates and products at the electrode|electrolyte interfaces, independent of their physical states (solid and/or liquid) and crystallinity level. Furthermore, SERS's power to resolve different isotopes can be exploited to identify the mass transport limitation and reactive sites of the passivated interface. In this review, the application of in situ SERS in studying the oxygen electrochemistry, specifically in aprotic Li‐O2 batteries, is summarized. The ideas and concepts covered in this review are also extended to the perspectives of the spectroelectrochemistry in general aprotic metal‐gas batteries. In situ surface‐enhanced Raman spectroscopy (SERS), as a nondestructive and ultra‐sensitive single molecular level characterization technique, has become one of the favorite tools for the characterization of the interfaces in diverse electrochemical energy storage and conversion systems, notably the Li‐O2 batteries. In this review, we summarized what SERS has accomplished on the understanding of the reaction pathways, reactive sites, and parasitic reaction mechanism essential to the oxygen electrochemistry (OER and ORR) in aprotic Li‐O2 batteries. Both the latest understanding of the reaction mechanism and the rationales underlying the experiment design are given the equal amount of emphasis.

Utilization of LiFePO 4 as a cathode material for Li-ion batteries often requires size nanonization coupled with calcination-based carbon coating to improve its electrochemical performance, which, however, is usually at the expense of tap density and may be environmentally problematic. Here we report the utilization of micron-sized LiFePO 4 , which has a higher tap density than its nano-sized siblings, by forming a conducting polymer coating on its surface with a greener diazonium chemistry. Specifically, micron-sized LiFePO 4 particles have been uniformly coated with a thin polyphenylene film via the spontaneous reaction between LiFePO 4 and an aromatic diazonium salt of benzenediazonium tetrafluoroborate. The coated micron-sized LiFePO 4 , compared with its pristine counterpart, has shown improved electrical conductivity, high rate capability and excellent cyclability when used as a ‘carbon additive free’ cathode material for rechargeable Li-ion batteries. The bonding mechanism of polyphenylene to LiFePO 4 /FePO 4 has been understood with density functional theory calculations. Nanonization of battery electrode particles is a usual way to enhance their conductivity, but the decreased tap density is detrimental to battery performance. Here, the authors coat micron-sized lithium iron phosphate with a conducting polymer layer and demonstrate some excellent electrochemical properties.

Li‐CO 2 batteries could skillfully combine the reduction of “greenhouse effect” with energy storage systems. However, Li‐CO 2 batteries still suffer from unsatisfactory electrochemical performances and their rechargeability is challenged. Here, it is reported that a composite of Ni nanoparticles highly dispersed on N‐doped graphene (Ni‐NG) with 3D porous structure, exhibits a superior discharge capacity of 17 625 mA h g −1 , as the air cathode for Li‐CO 2 batteries. The batteries with these highly efficient cathodes could sustain 100 cycles at a cutoff capacity of 1000 mA h g −1 with low overpotentials at the current density of 100 mA g −1 . Particularly, the Ni‐NG cathodes allow to observe the appearance/disappearance of agglomerated Li 2 CO 3 particles and carbon thin films directly upon discharge/charge processes. In addition, the recycle of CO 2 is detected through in situ differential electrochemical mass spectrometry. This is a critical step to verify the electrochemical rechargeability of Li‐CO 2 batteries. Also, first‐principles computations further prove that Ni nanoparticles are active sites for the reaction of Li and CO 2 , which could guide to design more advantageous catalysts for rechargeable Li‐CO 2 batteries.

Due to the real applications, optimal consensus reinforcement learning with switching topology is still challenging due to the complexity of topological changes. This paper investigates the optimal consensus control problem for discrete multi-agent systems under Markov switching topologies. The goal is to design an appropriate algorithm to find the optimal control policies that minimize the performance index while achieving consensus among the agents. The concept of mean-square consensus is introduced, and the relationship between consensus error and tracking error to achieve mean-square consensus is studied. A performance function for each agent under switching topologies is established and a policy iteration algorithm using system data is proposed based on the Bellman optimality principle. The theoretical analysis shows that the consensus error realizes mean-square consensus and the performance function is optimized. The efficacy of the suggested approach is confirmed by numerical simulation using an actor–critic neural network. As a result, the value function is the optimum and the mean-square consensus can be reached using this technique.

Background Myopia and obesity in children and adolescents have become serious public health problems that endanger public health, especially in China. Unhealthy lifestyle behaviors are environmental drivers of both myopia and obesity. This protocol describes a study to evaluate the effectiveness of “22510SS”, that is 2 h of daytime outdoor activities (‘2’); Limit screen time to no more than 2 h per day (‘2’); Consume at least 5 servings of fruits and vegetables daily (‘5’); Attain 1 h of physical activity daily (‘1’); Consume 0 sugar-sweetened beverages (‘0’); Reasonable sleep duration (‘S’); Regular supervision (‘S’). A school-based, multifaceted intervention strategy for myopia and obesity prevention, and to assess and explore the implementation of “22510SS” with regards to acceptability, feasibility, adoption, usage and maintenance. Methods and analysis This study aims to develop a comprehensive intervention strategy \"22510SS\" based on the socio-ecological model, and A two-arm cluster randomized trial with a parallel-group of a 1:1 allocation ratio in 36 primary and secondary schools to test its evidence-based intervention programs on the effects and implementation of myopia and obesity epidemics in children and adolescents in grades 4 and 7. The primary outcomes will include differences in visual acuity, body mass index, outdoor activity indicators, screen time, fruit and vegetable intake, high-quality protein intake, sugar-sweetened beverage intake, sleep duration, and level of monitoring among children and adolescents. Secondary outcomes will assess the acceptability, feasibility, uptake, use, and maintenance of the intervention. Effects on the primary and secondary outcomes will be analyzed using linear and logistic regression analyses, as well as difference-in-difference analysis, taking into account cluster effects and possible confounding factors. Process assessments will also be conducted through quantitative and qualitative analyses, including acceptability, feasibility, gender, adoption, implementation, and sustainability. Discussion This study will evaluate the effectiveness of “22510SS” and examine its implementation in the school-based network nesting family and clinic. Following this intervention study, the integrated intervention program focused on myopia and obesity among children and adolescents have great potential to be implemented in China to promote and support healthy lifestyle behavior change and reduce the risk of myopia and obesity in children and adolescents. Trial registration NCT05275959. Registered 23 Mach 2022.

This article studies the consensusability of multi-agent systems with general linear continuous-time dynamics. It is assumed that each agent can only obtain the information of other agents at sampling instants. Some sufficient and necessary conditions for consensusability in the case of state feedback are established, and it is shown that multi-agent systems with periodic sampling are consensusable if and only if the system associated with each agent is stabilisable and the topology graph has a spanning tree. Furthermore, some methods are provided to seek controller gain and sampling period which ensure consensus. Simulations are performed to validate the theoretical results.

While deep learning technologies are now capable of generating realistic images confusing humans, the research efforts are turning to the synthesis of images for more concrete and application-specific purposes. Facial image generation based on vocal characteristics from speech is one of such important yet challenging tasks. It is the key enabler to influential use cases of image generation, especially for business in public security and entertainment. Existing solutions to the problem of speech2face renders limited image quality and fails to preserve facial similarity due to the lack of quality dataset for training and appropriate integration of vocal features. In this paper, we investigate these key technical challenges and propose Speech Fusion to Face, or SF2F in short, attempting to address the issue of facial image quality and the poor connection between vocal feature domain and modern image generation models. By adopting new strategies on data model and training, we demonstrate dramatic performance boost over state-of-the-art solution, by doubling the recall of individual identity, and lifting the quality score from 15 to 19 based on the mutual information score with VGGFace classifier.