Construction of Dynamic Alloy Interfaces for Uniform Li Deposition in Li-Metal Batteries

It is well accepted that a lithiophilic interface can effectively regulate Li deposition behaviors,but the influence of the lithiophilic interface is gradually diminished upon continuous Li deposition that completely isolates Li from the lithiophilic metals.Herein,we perform in-depth studies on the creation of dynamic alloy interfaces upon Li deposition,arising from the exceptionally high diffusion coefficient of Hg in the amalgam solid solution.As a comparison,other metals such as Au,Ag,and Zn have typical diffusion coefficients of 10-20 orders of magnitude lower than that of Hg in the similar solid solution phases.This difference induces compact Li deposition pattern with an amalgam substrate even with a high areal capacity of 55 mAh cm^(-2).This finding provides new insight into the rational design of Li anode substrate for the stable cycling of Li metal batteries.

Dual-site lattice co-doping strategy regulated crystal-structure and microstructure for enhanced cycling stability of Co-free Ni-rich layered cathode

Affected by cobalt(Co)supply bottlenecks and high costs,Co-free Ni-rich layered cathodes are considered the most promising option for economical and sustainable development of lithium-ion batteries(LIBs).Low-cost LiNi_(x)Al_(1-x)O_(2)(x≥0.9)cathode are rarely reported due to their chemo-mechanical instabilities and poor cycle life.Herein,we employ a strategy of Mg/W Li/Ni dualsite co-doping LiNi_(0.9)Al_(0.1)O_(2)(named as LNA90)cathodes to enhance cycling stability by modifying the crystal structure and forming a center radially aligned microstructure.The Mg/W co-doped LiNi_(0.9)Al_(0.1)O_(2)cathode(named as LNAMW)exhibits high capacity retention of 94.9%at 1 C and 3.0-4.5 V after 100 cycles with 22.0%increase over the pristine cathode LNA90 and maintains the intact particle morphology.Meanwhile,the cycling performance of LNAMW cathode exceeds that of most reported Ni-rich cathodes(Ni mol%>80%).Our work offers a straightforward,efficient,and scalable strategy for the future design of Cofree Ni-rich cathodes to facilitate the development of economical lithium-ion batteries.

Geospatial big data for urban planning and urban management

The recent ten years witnessed the great achievements on rich applications of Geospatial Big Data across a variety of disciplines.For example,a huge number of Landsat images are utilized in mapping high-resolution global forest cover and the global forest changes in the twenty-first century are explored(Hansen et al.2013),which is impossible without the support of geospatial big data and the related automatic processing techniques.Based on the huge enterprise registration data in China,the economic and social development situations and trends are revealed by the non-statistic data and novel approaches(Li et al.2018).City-wide fine-grained urban population distribution at building level is achieved by integrating and fusing multisource geospatial big data(Yao et al.2017),which is usually not desired in traditional research.Geospatial Big Data provides a new transforming paradigm of scientific research especially at the crossroads of broad disciplines,including but not limited to the humanities,the physical sciences,engineering,and so on.

A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

Earth observation(EO)data,such as high-resolution satellite imagery or LiDAR,has become one primary source for forests Aboveground Biomass(AGB)mapping and estimation.However,managing and analyzing the large amount of globally or locally available EO data remains a great challenge.The Google Earth Engine(GEE),which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data,has appeared as an inestimable tool to address this challenge.In this paper,we present a scalable cyberinfrastructure for on-the-fly AGB estimation,statistics,and visualization over a large spatial extent.This cyberinfrastructure integrates state-of-the-art cloud computing applications,including GEE,Fusion Tables,and the Google Cloud Platform(GCP),to establish a scalable,highly extendable,and highperformance analysis environment.Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows.In addition,a web portal was developed to integrate the cyberinfrastructure with some visualization tools(e.g.Google Maps,Highcharts)to provide a Graphical User Interfaces(GUI)and online visualization for both general public and geospatial researchers.

Text GCN-SW-KNN:a novel collaborative training multi-label classification method for WMS application themes by considering geographic semantics

Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.