Effects of conductive agent type on lithium extraction from salt lake brine with LiFePO_(4) electrodes

Electrochemical lithium extraction from salt lakes is an effective strategy for obtaining lithium at a low cost.Nevertheless,the elevated Mg:Li ratio and the presence of numerous coexisting ions in salt lake brines give rise to challenges,such as prolonged lithium extraction periods,diminished lithium extraction efficiency,and considerable environmental pollution.In this work,Li FePO4(LFP)served as the electrode material for electrochemical lithium extraction.The conductive network in the LFP electrode was optimized by adjusting the type of conductive agent.This approach resulted in high lithium extraction efficiency and extended cycle life.When the single conductive agent of acetylene black(AB)or multiwalled carbon nanotubes(MWCNTs)was replaced with the mixed conductive agent of AB/MWCNTs,the average diffusion coefficient of Li+in the electrode increased from 2.35×10^(-9)or 1.77×10^(-9)to 4.21×10^(-9)cm^(2)·s^(-1).At the current density of 20 mA·g^(-1),the average lithium extraction capacity per gram of LFP electrode increased from 30.36 mg with the single conductive agent(AB)to 35.62 mg with the mixed conductive agent(AB/MWCNTs).When the mixed conductive agent was used,the capacity retention of the electrode after 30 cycles reached 82.9%,which was considerably higher than the capacity retention of 65.8%obtained when the single AB was utilized.Meanwhile,the electrode with mixed conductive agent of AB/MWCNTs provided good cycling performance.When the conductive agent content decreased or the loading capacity increased,the electrode containing the mixed conductive agent continued to show excellent electrochemical performance.Furthermore,a self-designed,highly efficient,continuous lithium extraction device was constructed.The electrode utilizing the AB/MWCNT mixed conductive agent maintained excellent adsorption capacity and cycling performance in this device.This work provides a new perspective for the electrochemical extraction of lithium using LFP electrodes.

MSCANet: multiscale context information aggregation network for Tibetan Plateau lake extraction from remote sensing images

Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.

Lithium Ion Extraction from the High Ration Mg/Li Salt Lake Brine with Ionic Liquid in Triisobutyl Phosphate and Kerosene

1 Introduction As the lightest metal with the unique properties of energy production and storage,lithium is regarded as the new century energy metal.Lithium and its compounds were widely used in various industrial fields,especially in

Distinguishing different subclasses of water bodies for long-term and large-scale statistics of lakes:a case study of the Yangtze River basin from 2008 to 2018

Long-term and large-scale lake statistics are meaningful for the study of environment change,but many of the existing methods are labourintensive and time-consuming.To overcome this problem,a novel method for long-term and large-scale lake extraction by shape-factorsand machine-learning-based water body classification is proposed.An experiment was conducted to extract the lakes in the Yangtze River basin(YRB)from 2008 to 2018 with the Joint Research Centre’s Global Surface Water Dataset(JRC GSW)data and OSM data.The results show:1)The proposed method is automatically and successfully executed.2)The number of river–lake complexes is between 3008 and 4697,representing 3.56%–5.70%of the total water bodies.3)The areas of the lakes and rivers in the YRB were obtained,and the accuracy of water classification in each year was stable between 90.2%and 93.6%.Comparing the back propagation neural network,random forest,and support vector machine models,we found that the three machine learning models have similar classification accuracy for the scenario.4)Fragmented and incomplete small rivers in the JRC GSW data,unchecked training samples,and overlapped shape factors are the three error sources.Future work will focus on addressing these three error sources.