Lithium metal, the ideal anode material for rechargeable batteries, suffers from the inherent limitations of sensitivity to the humid atmosphere and dendrite growth. Herein, low-cost fabrication of a ...metallic-lithium anode that is stable in air and plated dendrite-free from an organic-liquid electrolyte solves four key problems that have plagued the development of large-scale Li-ion batteries for storage of electric power. Replacing the low-capacity carbon anode with a safe, dendrite-free lithium anode provides a fast charge while reducing the cost of fabrication of a lithium battery, and increasing the cycle life of a rechargeable cell by eliminating the liquid-electrolyte ethylene-carbonate additive used to form a solid-electrolyte interphase passivation layer on the anode that is unstable during cycling. This solution is accomplished by formation of a hydrophobic solid-electrolyte interphase on a metallic-lithium anode that allows for handling of the treated lithium anode membrane in a standard dry room during cell fabrication.
A coordinated operation of smart grid (SG) and intelligent transportation system (ITS) provides electric vehicle (EV) owners with a myriad of power and transportation network data for EV charging ...navigation. However, the optimal charging navigation would be a challenging task owing to the randomness of traffic conditions, charging prices and waiting time at EV charging station (EVCS). In this paper, we propose a deep reinforcement learning (DRL)-based EV charging navigation, aiming at minimizing the total travel time and the charging cost at EVCS. First, we utilize the deterministic shortest charging route model (DSCRM) to extract feature states out of collected stochastic data and then formulate EV charging navigation as a Markov Decision Process (MDP) with an unknown transition probability. The proposed DRL-based approach will approximate the solution, which can adaptively learn the optimal strategy without any prior knowledge of uncertainties. Case studies are carried out within a practical zone in Xi'an city, China. Numerous experimental results verity the effectiveness of the proposed approach and illustrate its adaptation to EV driver preferences. The coordination effect of SG and ITS on reducing the waiting time and the charging cost in EV charging navigations is also analyzed.
The electroreduction of nitrogen to ammonia offers a promising alternative to the energy-intensive Haber-Bosch process. Unfortunately, the reaction suffers from low activity and selectivity, owing to ...competing hydrogen evolution and the poor accessibility of nitrogen to the electrocatalyst. Here, we report that deliberately triggering a salting-out effect in a highly concentrated electrolyte can simultaneously tackle the above challenges and achieve highly efficient ammonia synthesis. The solute ions exhibit strong affinity for the surrounding H
O molecules, forming a hydration shell and limiting their efficacy as both proton sources and solvents. This not only effectively suppresses hydrogen evolution but also ensures considerable nitrogen flux at the reaction interface via heterogeneous nucleation of the precipitate, thus facilitating the subsequent reduction process in terms of both selectivity and activity. As expected, even when assembled with a metal-free electrocatalyst, a high Faradaic efficiency of 71 ± 1.9% is achieved with this proof-of-concept system.
Ambient electrochemical N
reduction is emerging as a highly promising alternative to the Haber-Bosch process but is typically hampered by a high reaction barrier and competing hydrogen evolution, ...leading to an extremely low Faradaic efficiency. Here, we demonstrate that under ambient conditions, a single-atom catalyst, iron on nitrogen-doped carbon, could positively shift the ammonia synthesis process to an onset potential of 0.193 V, enabling a dramatically enhanced Faradaic efficiency of 56.55%. The only doublet coupling representing
NH
in an isotopic labeling experiment confirms reliable NH
production data. Molecular dynamics simulations suggest efficient N
access to the single-atom iron with only a small energy barrier, which benefits preferential N
adsorption instead of H adsorption via a strong exothermic process, as further confirmed by first-principle calculations. The released energy helps promote the following process and the reaction bottleneck, which is widely considered to be the first hydrogenation step, is successfully overcome.
A linear operator in a Hilbert space defined through inner product against a kernel function naturally introduces a reproducing kernel Hilbert space structure over the range space. Such formulation, ...called
H
-
H
K
formulation in this paper, possesses a built-in mechanism to solve some basic type problems in the formulation by using the basis method, that include identification of the range space, the inversion problem, and the Moore–Penrose pseudo-(generalized) inversion problem. After a quick survey of the existing theory, the aim of the article is to establish connection between this formulation with sparse series representation, and in particular with one called pre-orthogonal adaptive Fourier decomposition (POAFD), the latter being one, most recent and well developed, with great efficiency and wide and deep connections with traditional analysis. Within the matching pursuit methodology the optimality of POAFD is theoretically guaranteed. In practice POAFD offers fast converging numerical solutions.
The 2019 COVID-19 pandemic poses a challenge to adolescent psychological health. The aim of this study was to survey junior high and high school students in China to better understand the ...psychological consequences, such as anxiety, depression, and stress, of the COVID-19 pandemic.
A cross-sectional online survey using structural questionnaires was conducted from April 7, 2020, to April 24, 2020. Demographic information and general information related to the pandemic were collected. Psychological consequences were assessed by the Impact of Event Scale-Revised and the Depression, Anxiety and Stress Scale. Influencing factors were assessed by the Brief Resilience Scale and Coping Style Questionnaire.
Our sample comprised 493 junior high school students (male = 239, mean age = 13.93 years) and 532 high school students (male = 289, mean age = 17.08 years). Resilience and positive coping were protective factors for the occurrence of depressive, anxiety, and stress symptoms in junior high and high school students (p < .05). Positive coping was a protective factor for trauma-related distress in junior high school students (p < .05). Negative coping is a risk factor for depression, anxiety, stress symptoms, and trauma-related distress in junior high and high school students (p < .05).
During the COVID-19 pandemic in China, more than one fifth of junior high and high school students' mental health was affected. Our findings suggested that resilience and positive coping lead to better psychological and mental health status among students. In contrast, negative coping is a risk factor for mental health.
Covalent organic frameworks with abundant active sites are potential metal-free catalysts for the nitrogen reduction reaction. However, the utilization ratio of active sites is restricted in an ...actual reaction process due to the limited nitrogen transport. Here, we demonstrate that facilitating the N
accessibility to boron-rich covalent organic frameworks through electrochemical excitation can achieve highly efficient nitrogen reduction activity. Simulations show that the boron sites are bonded with nitrogenous species under electrochemical condition and the resultant amorphous phase of covalent organic frameworks has much stronger affinity toward N
to enhance the molecule collision. Combined with experimental results, the excitation process is confirmed to be a virtuous cycle of more excited sites and stronger N
affinity, which continuously proceed until the whole system reaches the optimum reaction status. As expected, the electrochemically excited catalyst delivers significantly enhanced reaction activity, with a high Faradaic efficiency of 45.43%.
Electric power and transportation networks become increasingly coupled through electric vehicles (EV) charging station (EVCS) as the penetration of EVs continues to grow. In this paper, we propose a ...holistic framework to enhance the operation of coordinated electric power distribution network (PDN) and urban transportation network (UTN) via EV charging services. Under this framework, a bi-level model is formulated to optimally determine EVCS charging service fees (CSF) for guiding EV charging behaviors and minimizing the total social cost. At the upper level, PDN with wind power generation is formulated as a second-order cone problem (SOCP) where CSF is determined. Given the settings calculated at the upper level, the lower level problem is described as a traffic assignment problem (TAP) which is subject to the user equilibrium (UE) principle and captures the individual rationality of single EV owners in UTN. The uncertainties in wind power output and origin-destination (O-D) traffic demands are considered in the proposed model and a deep reinforcement learning (DRL)-based solution framework is developed to decouple and approximately solve the stochastic bi-level problem. Both gradient-based and gradient-free training algorithms are implemented in this paper and the respective results are compared. The case studies on a 5-node system, 24-node Sioux-Falls system and real-world Xi'an city in China are conducted to verify the effectiveness of the proposed model, which demonstrates the enhanced operation of coordinated PDN and UTN networks by reducing the traffic congestion and improving the integration of renewable energy.
This study explores plasmon-induced electrochemical reactions on single nanoparticles using electrogenerated chemiluminescence microscopy (ECLM). Under laser irradiation, real-time screening showed ...lower plasmon-induced reaction efficiency for bimetallic Au@Pt nanoparticles compared to monometallic Au nanoparticles. ECLM offers a high-throughput imaging and precise quantitative approach for analyzing photo-electrochemical conversion at single nanoparticle level, valuable for both theoretical exploration and optimization of plasmonic nanocatalysts.
Plasmon-induced electrochemical reactions at single plasmonic nanocatalysts were explored.
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