The Impact of Li Grain Size on Coulombic Efficiency in Li Batteries

One of the most promising means to increase the energy density of state-of-the-art lithium Li-ion batteries is to replace the graphite anode with a Li metal anode. While the direct use of Li metal may be highly advantageous, at present its practical application is limited by issues related to dendrite growth and low Coulombic efficiency, CE. Here operando electrochemical scanning transmission electron microscopy (STEM) is used to directly image the deposition/stripping of Li at the anode-electrolyte interface in a Li-based battery. A non-aqueous electrolyte containing small amounts of H2O as an additive results in remarkably different deposition/stripping properties as compared to the “dry” electrolyte when operated under identical electrochemical conditions. The electrolyte with the additive deposits more Li during the first cycle, with the grain sizes of the Li deposits being significantly larger and more variable. The stripping of the Li upon discharge is also more complete, i.e., there is a higher cycling CE. This suggests that larger grain sizes are indicative of better performance by leading to more uniform Li deposition and an overall decrease in the formation of Li dendrites and side reactions with electrolyte components, thus potentially paving the way for the direct use of Li metal in battery technologies

A high-throughput phenotyping assay for precisely determining stalk crushing strength in large-scale sugarcane germplasm

Sugarcane is a major industrial crop around the world. Lodging due to weak mechanical strength is one of the main problems leading to huge yield losses in sugarcane. However, due to the lack of high efficiency phenotyping methods for stalk mechanical strength characterization, genetic approaches for lodging-resistant improvement are severely restricted. This study attempted to apply near-infrared spectroscopy high-throughput assays for the first time to estimate the crushing strength of sugarcane stalks. A total of 335 sugarcane samples with huge variation in stalk crushing strength were collected for online NIRS modeling. A comprehensive analysis demonstrated that the calibration and validation sets were comparable. By applying a modified partial least squares method, we obtained high-performance equations that had large coefficients of determination (R2 > 0.80) and high ratio performance deviations (RPD > 2.4). Particularly, when the calibration and external validation sets combined for an integrative modeling, we obtained the final equation with a coefficient of determination (R2) and ratio performance deviation (RPD) above 0.9 and 3.0, respectively, demonstrating excellent prediction capacity. Additionally, the obtained model was applied for characterization of stalk crushing strength in large-scale sugarcane germplasm. In a three-year study, the genetic characteristics of stalk crushing strength were found to remain stable, and the optimal sugarcane genotypes were screened out consistently. In conclusion, this study offers a feasible option for a high-throughput analysis of sugarcane mechanical strength, which can be used for the breeding of lodging resistant sugarcane and beyond

Spatial Disparity and Influencing Factors of Coupling Coordination Development of Economy–Environment–Tourism–Traffic: A Case Study in the Middle Reaches of Yangtze River Urban Agglomerations

In the process of rapid development of economic globalization and regional integration, the importance of urban agglomeration has become increasingly prominent. It is not only the main carrier for countries and regions to participate in international competition, but also the main place to promote regional coordination and sustainable development. Coordinated economic, environmental, tourism and traffic development is very necessary for sustainable regional development. However, the existing literature lacks research on coupling coordination of the Economy–Environment–Tourism–Traffic (EETT) system in urban agglomeration. In this study, in order to fill this gap, we establish the index system from four dimensions of economy, environment, tourism and traffic, and select the influencing factors from the natural and human perspectives to exam the spatio-temporal changes and influencing factors in the coupling coordination of the EETT system using an integrated method in the Middle Reaches of Yangtze River Urban Agglomerations (MRYRUA), China. The results indicate that the coupling coordination degree of the EETT system transitioned from the uncoordinated period to the coordinated period, while it showed an increasing trend on the whole from 1995 to 2017. The spatial agglomeration effect has been positive since 2010, while “High–High” and “Low–High” agglomeration regions were transferred from the east to the south. Land used for urban construction as a percentage of the urban area and vegetation index has a great impact on the coupling coordination degree. These results provide important guidance for the formulation of integration and coordinated development policy in the MRYRUA, and then increase China’s international competitiveness by improving the contribution of urban agglomerations to GDP

Impact of connected vehicle guidance information on network-wide average travel time

With the emergence of connected vehicle technologies, the potential positive impact of connected vehicle guidance on mobility has become a research hotspot by data exchange among vehicles, infrastructure, and mobile devices. This study is focused on micro-modeling and quantitatively evaluating the impact of connected vehicle guidance on network-wide travel time by introducing various affecting factors. To evaluate the benefits of connected vehicle guidance, a simulation architecture based on one engine is proposed representing the connected vehicle–enabled virtual world, and connected vehicle route guidance scenario is established through the development of communication agent and intelligent transportation systems agents using connected vehicle application programming interface considering the communication properties, such as path loss and transmission power. The impact of connected vehicle guidance on network-wide travel time is analyzed by comparing with non-connected vehicle guidance in response to different market penetration rate, following rate, and congestion level. The simulation results explore that average network-wide travel time in connected vehicle guidance shows a significant reduction versus that in non–connected vehicle guidance. Average network-wide travel time in connected vehicle guidance have an increase of 42.23% comparing to that in non-connected vehicle guidance, and average travel time variability (represented by the coefficient of variance) increases as the travel time increases. Other vital findings include that higher penetration rate and following rate generate bigger savings of average network-wide travel time. The savings of average network-wide travel time increase from 17% to 38% according to different congestion levels, and savings of average travel time in more serious congestion have a more obvious improvement for the same penetration rate or following rate

Spatial co-location pattern discovery without thresholds

Spatial co-location pattern mining discovers the subsets of features whose events are frequently located together in geographic space. The current research on this topic adopts a threshold-based approach that requires users to specify in advance the thresholds of distance and prevalence. However, in practice, it is not easy to specify suitable thresholds. In this article, we propose a novel iterative mining framework that discovers spatial co-location patterns without predefined thresholds. With the absolute and relative prevalence of spatial co-locations, our method allows users to iteratively select informative edges to construct the neighborhood relationship graph until every significant co-location has enough confidence and eventually to discover all spatial co-location patterns. The experimental results on real world data sets indicate that our framework is effective for prevalent co-locations discovery

A comparative study on urban land use eco-efficiency of Yangtze and Yellow rivers in China: From the perspective of spatiotemporal heterogeneity, spatial transition and driving factors

Increasing urban land use eco-efficiency (ULUEE) is conducive to alleviating the human-land conflict in cities, especially in densely populated and ecologically sensitive areas. ULUEE research in the past has been less focused on different river basins from a comparative perspective, and largely ignored the regional heterogeneity in driving factors. Thus, this study aims to compare the spatiotemporal dynamics and driving factors of ULUEE in the Yangtze River Economic Belt (YREB) and Yellow River Basin (YRB) in China. The ULUEE was first evaluated using the Slack Based Model-Undesirable, and then the Theil Index and spatial Markov Chain were used to reveal the differential changes and shifting direction of the ULUEE, followed by an analysis of the driving factors of different segments of different river basins and sized cities using the Tobit model. Results show that: (1) in both YREB and YRB, the ULUEE shows the characteristic of “lower reaches > middle reaches > upper reaches”, however, notable differences in Theil index values are evident among basins and basin segments. (2) ULUEE exhibits spillovers and convergence phenomena during its spatial variations. (3) There is a significant U-shaped relationship between ULUEE and GDP in the whole basin and different segments of YREB, but only in the middle and lower reaches of YRB. Additionally, urban spatial form and environmental regulations have heterogeneous impacts on ULUEE in different-sized cities. In order to maintain sustainability in urban land resource management and coordinate city development of varying sizes, governments must take a systematic approach that accounts for the complexity of spatial characteristics and drivers of ULUEE