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Abstract Rechargeable aqueous zinc-metal batteries, considered as the possible post-lithium-ion battery technology for large-scale energy storage, face severe challenges such as dendrite growth and hydrogen evolution side reaction (HER) on Zn negative electrode. Herein, a three-dimensional Cu-In alloy interface is developed through a facile potential co-replacement route to realize uniform Zn nucleation and HER anticatalytic effect simultaneously. Both theoretical calculations and experimental results demonstrate that this bifunctional Cu-In alloy interface inherits the merits of low Zn-nucleation overpotential and high HER overpotential from individual copper and indium constituents, respectively. Moreover, the dynamical self-reconstruction during cycling leads to an HER-anticatalytic and zincophilic gradient hierarchical structure, enabling highly reversible Zn chemistry with dendrite-free Zn (002) deposition and inhibited HER. Moreover, the improved interface stability featured by negligible pH fluctuations in the diffusion layer and suppressed by-product formation is evidenced by in-situ scanning probe technology, Raman spectroscopy, and electrochemical gas chromatography. Consequently, the lifespan of the CuIn@Zn symmetric cell is extended to more than one year with a voltage hysteresis of 6 mV. Importantly, the CuIn@Zn negative electrode is also successfully coupled with high-loading iodine positive electrode to fabricate Ah-level (1.1 Ah) laminated pouch cell, which exhibits a capacity retention of 67.9% after 1700 cycles.

Abstract In rats with unilateral nephrectomy and cardiac dysfunction, renal function deteriorates at an accelerated rate, as evidenced by increased proteinuria. Whether myocardial infarct-induced heart failure (HF) exacerbates renal injury in hypertensive rats with mild renal injury has not been reported. Rats underwent either coronary ligation or sham surgery. Thirty spontaneously hypertensive rats (SHRs) aged 8 weeks were randomly divided into two groups. Group 1 was the sham group, in which the rats underwent thoracotomy without ligation of the coronary artery. Group 2 underwent coronary artery ligation. The rats in group 2 underwent coronary artery ligation on week 0. The experiment lasted 12 weeks. Urine was collected in metabolic cages over a 24-h period. Urine was collected from the rats 2 days before the end of the experiment, and the ratio of urinary protein to urinary creatinine was measured in the clinical laboratory. All rats were examined by echocardiogram one day before the end of the experiment. On the last day of the experiment, blood was collected and sent to the laboratory for analysis. Hematoxylin–eosin (HE) and periodic acid-Schiff (PAS) staining were performed on heart and kidney sections. The ejection fraction in group 2 was lower than that in group 1 (P

Dockless bike-sharing (DBS) is an effective solution to the “first and last mile” problem in urban transportation. It can be integrated with urban rail transit (URT) to provide passengers with more convenient travel services. This study focuses on the integrated use of DBS and URT in Shenzhen, utilising a multi-buffer zone approach to identify DBS data within URT station catchment areas. By employing ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models, the spatiotemporal heterogeneity of integrated use and its relationship with environmental factors surrounding URT stations were examined. The empirical findings highlight the superiority of the MGWR model in accurately explaining spatial relationships compared to the OLS and GWR models. Furthermore, the study reveals that the impact of built environment factors on integrated use varies during morning and evening peak periods, as well as in terms of access and egress. Specifically, factors such as catering, shopping, companies, residential buildings, bus stops, minor roads, transfer stations and population density were found to influence the integrated use of DBS and URT. These findings not only contribute to the promotion of the DBS-URT integration but also promote the overall development of urban transportation.

Highlights The mechanism of the change in lithium-ion transport behavior caused by the incorporation of inorganic fillers into the polymer matrix is reviewed. The intrinsic factors of inorganic fillers to enhance the ionic conductivity of composite polymer electrolyte (CPEs) are investigated in depth. The contribution of inorganic fillers to inhibit dendrite growth and side reactions in CPEs is summarized.

The safety concerns associated with power batteries have prompted significant interest in all−solid−state lithium batteries (ASSBs). However, the advancement of ASSBs has been significantly impeded due to their unsatisfactory electrochemical performance, which is attributed to the challenging interface between the solid−state electrolyte and the electrodes. In this work, an in situ polymerized composite solid−state electrolyte (LLZTO−PVC) consisting of poly(vinylene carbonate) (PVC) and Li6.4La3Zr1.4Ta0.6O12 (LLZTO) was successfully prepared by a γ−ray irradiation technique. The novel technique successfully solved the problem of rigidity at the interface between the electrode and electrolyte. The LLZTO−PVC electrolyte exhibited a notable ionic conductivity of 1.2 × 10−4 S cm−1 25 °C, along with good mechanical strength and flexibility and an electrochemical window exceeding 4.65 V. It was showed that the LiCoO2(LCO)/LLZTO−PVC/Li battery, which achieved in situ solidification via γ−ray irradiation, can steadily work at a current density of 0.2 C at 25 °C and maintain a retention rate of 92.4% over 100 cycles. The good interfacial compatibility between electrodes and LLZTO−PVC electrolyte designed via in situ γ−ray irradiation polymerization could be attributed to its excellent electrochemical performance. Therefore, the method of in situ γ−ray irradiation polymerization provides a vital reference for solving the interface problem.

To accurately estimate the effect of driving conditions on vehicle emissions, an on-road light-duty vehicle emission platform was established based on OEM-2100TM, and each second data of mass emission rate corresponding to the driving conditions were obtained through an on-road test. The mass emission rate was closely related to the velocity and acceleration in real-world driving. This study shows that a high velocity and acceleration led to high real-world emissions. The vehicle emissions were the minimum when the velocity ranged from 30 to 50 km/h and the acceleration was less than 0.5 m/s2. Microscopic emission models were established based the on-road test, and single regression models were constructed based on velocity and acceleration separately. Binary regression and neural network models were established based on the joint distribution of velocity and acceleration. Comparative analysis of the accuracy of prediction and evaluation under different emission models, total error, second-based error, related coefficient, and sum of squared error were considered as evaluation indexes to validate different models. The results show that the three established emission models can be used to make relatively accurate prediction of vehicle emission on actual roads. The velocity regression model can be easily combined with traffic simulation models because of its simple parameters. However, the application of neural network model is limited by a complex coefficient matrix. First published online 19 March 2020

Abstract Implementing fast‐charging lithium‐ion batteries (LIBs) is severely hindered by the issues of Li plating and poor rate capability for conventional graphite anode. Wadsley–Roth phase TiNb2O7 is regarded as a promising anode candidate to satisfy the requirements of fast‐charging LIBs. However, the unsatisfactory electrochemical kinetics resulting from sluggish ion and electron transfer still limit its wide applications. Herein, an effective strategy is proposed to synchronously improve the ion and electron transfer of TiNb2O7 by incorporation of oxygen vacancy and N‐doped graphene matrix (TNO−x@N‐G), which is designed by combination of solution‐combustion and electrostatic self‐assembly approach. Theoretical calculations demonstrate that Li+ intercalation gives rise to the semi‐metallic characteristics of lithiated phases (LiyTNO−x), leading to the self‐accelerated electron transport. Moreover, in situ X‐ray diffraction and Raman measurements reveal the highly reversible structural evolution of the TNO−x@N‐G during cycling. Consequently, the TNO−x@N‐G delivers a higher reversible capacity of 199.0 mAh g−1 and a higher capacity retention of 86.5% than those of pristine TNO (155.8 mAh g−1, 59.4%) at 10 C after 2000 cycles. Importantly, various electrochemical devices including lithium‐ion full battery and hybrid lithium‐ion capacitor by using the TNO−x@N‐G anode exhibit excellent rate capability and cycling stability, verifying its potential in practical applications.

Lithium-ion batteries (LIBs) have been widely used in mobile devices, energy storage power stations, medical equipment, and other fields, became an indispensable technological product in modern society. However, the capacity degradation of LIBs limits their long-term deployment, which is not conducive to saving resources. What is more, it will lead to safety problems when the capacity of the battery is degraded. Failure of the battery is a key issue in the research and application of LIBs. Faced with the problem of capacity degradation, various aspects of LIBs have been studied. This paper reviews the electrochemical degradation mechanism of LIBs’ life fade, detection technologies for battery failure, methods to regulate battery capacity degradation, and battery lifetime prognostics. Finally, the development trend and potential challenges of battery capacity degradation research are prospected. All the key insights from this review are expected to advance the research on capacity fading and lifetime prediction techniques for LIBs.

The efficient and accurate prediction of urban travel demand, which is a hot topic in intelligent transportation research, is challenging due to its complicated spatial-temporal dependencies, dynamic nature, and uneven distribution. Most existing forecasting methods merely considered the static spatial dependencies while ignoring the influence of the diversity of dynamic demand patterns and/or uneven distribution. In this paper, we propose a traffic demand forecasting framework of a hybrid dynamic graph convolutional network (HDGCN) model to deeply capture the characteristics of urban travel demand and improve prediction accuracy. In HDGCN, traffic flow similarity graphs are designed according to the dynamic nature of travel demand, and a dynamic graph sequence is generated according to time sequence. Then, the dynamic graph convolution module and the standard graph convolution module are introduced to extract the spatial features from dynamic graphs and static graphs, respectively. Finally, the spatial features of the two components are fused and combined with the gated recurrent unit (GRU) to learn the temporal features. The efficiency and accuracy of the HDGCN model in predicting urban taxi travel demand are verified by using the taxi data from Manhattan, New York City. The modeling and comparison results demonstrate that the HDGCN model can achieve stable and effective prediction for taxi travel demand compared with the state-of-the-art baseline models. The proposed model could be used for the real-time, accurate, and efficient travel demand prediction of urban taxi and other urban transportation systems.

Abstract Wadsley–Roth crystallographic shear structure niobium‐based oxides are of great interest in fast Li+ storage due to their unique 3D open tunnel structures that offer facile Li+ diffusion paths. Their moderate lithiation potential and reversible redox couples hold the great promise in the development of next‐generation lithium‐ion batteries (LIBs) that are characterized by high power density, long lifespan, and high safety. Despite these outstanding merits, there is still extensive advancement space for further enhancing their electrochemical kinetics. And the industrial feasibility of Wadsley–Roth crystallographic shear structure niobium‐based oxides as anode materials for LIBs requires more systematic research. In this review, recent progress in this field is summarized with the aim of realizing the practical applications of Wadsley–Roth phase anode materials in commercial LIBs. The review focuses on research toward the crystalline structure analyses, electrochemical reaction mechanisms, modification strategies, and full cell performance. In addition to highlighting the current research advances, the outlook and perspective on Wadsley–Roth anode materials is also concisely provided.

Summary: Although Li-metal anodes are extremely attractive owing to the ultrahigh theoretical specific capacity, the low Coulombic efficiency and severe safety hazards resulting from uncontrollable Li dendrites growth hinder their widespread implementation. Herein, we propose a novel design of Ni macropore arrays for the functional Li deposition host. Benefiting from the regulated electric field distribution, Li nucleation and growth can be well confined within conductive Ni macropores. Consequently, the Ni macropore array electrode exhibits stable Li deposition behavior, i.e., high Coulombic efficiency of above 97% over 400 cycles for 1.0 mAh cm−2. Most importantly, the LiFePO4 || Li-Ni macropore arrays full cell also shows greatly enhanced cycling stability (90.3 mAh g−1 at 1 C after 700 cycles), holding great promise for high-performance rechargeable Li metal batteries.

There are many challenges for the Static lithium polysulfide semiliquid battery in its commercial application, such as poor stability of the cathode material and further amplification of the lithium polysulfide shuttle effect. Therefore, this manuscript introduced a new type of Pt3Ni@C composite material as cathode working electrode based on the principle of volcanic catalytic curve. Through symmetric battery test, CV, polarization curves and impedance test, it was found that Pt3Ni@C composite material had good catalytic activity of lithium polysulfide to improve electrochemical kinetics. When the catholyte was Li2S8 and the charge-discharge voltage range was 1.8~2.6 V, the capacity maintained at approximately 550 mAh g−1, and the coulombic efficiency maintained at approximately 95% after 100 cycles at a current rate of 0.5 mA cm−2. The Pt3Ni@C composite material is a potential cathode material with the specific capacity and long cycling stability of the static lithium polysulfide semiliquid battery.

The lithium metal anode is one of the most attractive candidates for high-energy lithium rechargeable batteries because it has an ultrahigh theoretical specific capacity and the lowest electrode potential. Unfortunately, uncontrollable growth of dendritic Li leads to problems such as safety hazards and low cycling reversibility, which greatly hinder its commercial application. Here, a Li–Zn alloy layer is constructed in situ on Li metal foil by a simple chemical reaction of zinc trifluoromethanesulfonate with Li metal. The modified Li metal anode forms an interface with fast charge transfer kinetics and high chemical resistance to the electrolyte, which enables deposition of Li with a smooth, dense morphology without the growth of dendritic Li. In symmetrical cells, the Li metal anode with the Li–Zn alloy layer can reach a cycling lifetime of more than 500 h under a current density of 2 mA cm−2. This work provides a simple and effective strategy to suppress the formation of Li dendrites. Keywords: Li metal, Li–Zn alloy layer, Interface, Dendrite, Charge transfer kinetics

This paper proposes a visual servo scheme for hoisting positioning under disturbance conditions. In actual hoisting work, disturbances such as equipment and load vibration are inevitable, which brings challenges to the development of a visual servo for hoisting positioning. The main problems are as follows: (1) the correlation between visual error and disturbance is not considered or well resolved; (2) the disturbance has a great influence on the control stability, but it is difficult to model. At present, there is no detailed research on the above problems. In this paper, the visual error is defined by the image error of the feedback signal based on dynamic equations containing disturbances. An adaptive sliding mode control algorithm is employed to decrease the influence of external disturbance, and the coefficient of the slide surface is established based on the adaptive gain. In view of the belief that it is difficult to model disturbance terms, a nonlinear disturbance observer is introduced to obtain equivalent disturbance. On this basis, an adaptive control algorithm with disturbance compensation is proposed to improve the robustness of the visual servo system. We use Lyapunov’s method to analyze the stability conditions of the system. Compared with the other state-of-the-art methods, the simulation results show that our method has superior performance in convergence, accuracy, and restraining disturbance. Finally, the proposed algorithm is applied to the hoisting platform for experimental research, which proves the effectiveness of the controller.

Although urban rain transit (URT) is an attractive alternative mode of daily travel, barriers exist in URT development across the world, in particular, the high cost of construction and operation and relative low rates of URT ridership. Despite these barriers, URT has gained considerable popularity worldwide in recent years; much of this trend is driven by projects in China. Despite this public support and implementation of URT projects, the ridership, capital costs and cost-effectiveness of URT projects remain largely unstudied. This paper addresses this planning and policy issue by examining line-level ridership and investment data for 97 heavy rail transit (HRT) lines and 12 light rail transit (LRT) lines in 28 Chinese cities. Comparative analysis is conducted so as to evaluate the performance and cost-effectiveness of HRT and LRT. Multiple linear regression analysis is used to explain the variability of URT cost-effectiveness and how it varies depending on land use density, project design, system service, and multimodal transit integration. Findings indicate that land-use density, line length, number of transfer stations, operation time, and bus ridership significantly contribute to higher levels of URT ridership, while URT ridership decreases significantly with train headway and the station’s distance from the city center. It is cost-effective to develop URT in high-density cities in spite of high costs, and some, if not all, LRT lines are more cost-effective than HRT lines. As of this analysis, the overdevelopment of HRT in China has failed to plan for multimodal transport integration and operational optimization. However, these shortcomings are also opportunities for Chinese transportation and land-use planners to develop more cost-effective URT projects that also improve the level of service available to the public.

Ammonia gas sensors are very essential in many industries and everyday life. However, their complicated fabrication process, severe environmental fabrication requirements and desorption of residual ammonia molecules result in high cost and hinder their market acceptance. Here, laser direct writing is used to fabricate three parallel porous 3D graphene lines on a polyimide (PI) tape to simply construct an ammonia gas sensor. The middle one works as an ammonia sensing element and the other two on both sides work as heaters to improve the desorption performance of the sensing element to ammonia gas molecules. The graphene lines were characterized by scanning electron microscopy and Raman spectroscopy. The response and recovery time of the sensor without heating are 214 s and 222 s with a sensitivity of 0.087% ppm−1 for sensing 75 ppm ammonia gas, respectively. The experimental results prove that under the optimized heating temperature of about 70 °C the heaters successfully help implement complete desorption of residual NH3 showing a good sensitivity and cyclic stability.

Transportation is generally perceived as a catalyst for economic development. This has been highlighted in previous studies. However, less attention has been paid to examine the relationship between economy and transport demand by exploring spatially cross-sectional data, especially for countries with significant regional economic imbalance, like China. In this article, we assess the economic influence of intercity multimodal transport demand at the prefecture level in China. Spatial autoregressive regression models are used to examine the impact of transport demand on economy by deep analysis of transport modes (land, air, and water) and regions (eastern, central, and western). Through contrasting results from spatial lag model and spatial error model with those from the ordinary least square, this study finds that the estimation results can become more accurate by controlling for spatial autocorrelation, especially at the national level. Through rigorous analysis it is identified that except for water passenger traffic, all other intercity transport demand significantly contribute to a city’s economic development level in gross domestic product. In particular, air transport demands distribute more evenly and are estimated with the highest beta coefficients at both national and regional levels. In addition, the beta coefficients for land, air and water transportation are estimated with different magnitudes and significances at the national and regional levels. This study contributes to the ongoing discussion on the relationship between intercity multimodal transport demand and economic development level. Findings from this paper provide planning makers with valid and efficient strategies to better develop the economy by leveraging the special “⊣” cluster pattern of economic development and the benefits of air transportation.

Traffic fatalities and injuries on urban roads especially at urban intersections constitute a growing problem in China. This study aims at researching urban intersection crashes in China and drawing conclusions by using hierarchical structured data with reference to Bayesian network (BN). On the basis of 3584 recorded crashes collected from the urban intersections of Changshu, China, a BN topological structure is developed to reflect the hierarchical characteristic of crash variables. The parameter learning process is completed with Dirichlet prior distribution. Junction tree engine is used to make inference on crash types at urban intersections with two respective given evidences, i.e. human factor and vehicle type. Parameter learning results suggest the efficacy of BN approach in the prediction accuracy. The average learned probability of illegal driving is 40.83%, which is much higher than other learned probabilities of human factors. The inferred probabilities of frontal collision at urban intersection crashes involving bicycles and electric bikes are 43.16% and 40.44% respectively, which is higher than the probabilities involving small cars and heavy vehicles. However, heavy vehicles have a higher inferred probability in side collision than light vehicles, whose inferred side collision probability is 41.02%. This study has a good potential in traffic safety discipline to reveal the correlation exists in traffic risk factors. By means of BN, researchers can make an intensive study on the hierarchical traffic crash data, determine the key risk factors and then propose corresponding and appropriate improvement measures. First published online: 16 Oct 2013

To relax the strong assumption associated with User Equilibrium (UE) in the previous research of network reserve capacity conducted by Gao and Song (2002), this paper assumes that the drivers all make route choices based on Stochastic User Equilibrium (SUE) principle. Similarly, two bi-level programs are formulated to study the network reserve capacity with SUE problem. The first bi-level program is developed to maximize the network reserve capacity by optimizing signal settings while the traffic demands are reassigned by SUE model. The second program extends the research with Continuous Network Design (CND) problem to find the maximum possible increase in reserve capacity through optimizing allocation of network investment. Two methods, i.e. the sensitivity analysis-based method and Genetic Algorithm (GA), are detailed formulated to solve the bi-level reserve capacity problem. Application of the proposed model and its solution algorithms on two numerical examples find that the network reserve capacity does not always increase with improved quality of drivers’ information. Besides, CND can not only help to increase network reserve capacity, but also can help to make more use of physical capacity of road network at Deterministic User Equilibrium (DUE) condition, thus reduces the difference of reserve capacity between the assumptions of SUE and DUE.

This paper aims at developing a fuzzy multiobjective decision support model for urban rail transit projects in China under the background that China is presently experiencing an unprecedented construction of urban rail transit. In this study, an appropriate model using multilevel fuzzy comprehensive evaluation is developed. To do so, detailed description and definition of multilevel fuzzy comprehensive evaluation is displayed. In the decision process, the followings are considered as the influential objectives: traveller attraction, environment protection, project feasibility and operation. In addition, consistent matrix analysis method is used to determine the weights between objectives and the factor weights between factors, which reduce the work caused by repeated establishment of the decision matrix on the basis of ensuring the consistency of decision matrix. An application study is provided as an example to demonstrate the feasibility of this model. The results show that this model is reliable and applicable for decisionmaking in urban rail transit projects. Finally, a decision support system is developed to benefit decisionmakers in optimizing the process of identifying superior urban rail transit projects.

For urban rail transit (URT) operating companies, short-time passenger flow forecasting is a complex and critical task that determines the formulation and arrangement of operation plans and timetables. The automatic fare collection (AFC) system of URT provides detailed passenger flow data, which supports short-time passenger flow forecasting. This study proposes a combined model of Chaotic Sparrow Search Algorithm and Long Short-term memory artificial neural network (CSSA-LSTM) based on station classification. The model classifies stations based on various indicators such as point of interest (POI) data in the radiation area of Hangzhou, utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to denoise and smooth passenger flow data. It also conducts short-time passenger flow prediction and cross-validation for different stations by using the proposed model. To demonstrate the accuracy of the model, evaluation metrics such as R-squared and RMSE are introduced, and the results of the CSSA-LSTM model are compared with those of the LSTM, PSO-LSTM, and SSA-LSTM models. The experimental results show that the CSSA-LSTM model can effectively improve prediction accuracy, with R-squared increasing by 14.80%, 8.60%, and 6.82% compared to the other three algorithms, respectively. In addition, the cross-validation results of different stations prove the wide applicability of the CSSA-LSTM model, and this study have practical significance for URT planning and management. [ABSTRACT FROM AUTHOR]