Synergistic effect from coaxially integrated CNTs@MoS_(2)/MoO_(2) composite enables fast and stable lithium storage

Molybdenum oxide/sulfide materials are extensively evaluated as high-capacity anode candidates for lithium ion batteries.However,they suffer from rapid capacity decay and poor kinetics.Herein,we report on synergistic effect from structurally integrated coaxial CNTs@MoS_(2)/MoO_(2) composite material on lithium storage,in which MoS_(2)/MoO_(2) nanosheets are conformally decorated on carbon nanotubes(CNTs).In-situ synchrotron X-ray diffraction measurement is performed to elucidate synergistic effect among three MoS_(2),MoO_(2) and CNTs components for lithium storage.Reaction mechanism exploration reveals that the MoO_(2) component undergoes reversible Li^(+)intercalation via forming a stable Li_(0.98) MoO_(2) phase over a voltage range of 3.0 to 0.01 V vs.Li^(+)/Li,without experiencing the conversion reaction into metallic Mo,which contributes to long-term stability during charge/discharge cycles.Meanwhile,lithium storage of MoS_(2) is through lithium and sulfur reversible reaction after the initial conversion reaction of lithiated MoS_(2) forming Li_(2)S and Mo.The CNTs component enhances electronic conductivity and structural stability by minimizing volume change and reaction strains in the CNTs@MoS_(2)/MoO_(2) composite anode.A desired 68.2%capacity retention upon 2000 cycles at 10 A/g has been demonstrated for the CNTs@MoS_(2)/MoO_(2) anode,revealing prominent reaction kinetics and structural stability for fast and stable lithium storage,superior to various Mo-based anode materials previously reported.The findings from this study,with the unique insight into the role of structural integrity in combining MoS_(2)/MoO_(2) materials with the CNTs substrate,offers a strategy for designing composite anode materials for superior lithium storage performance.

Threshold of climate extremes that impact vegetation productivity over the Tibetan Plateau

Vegetation growth is adversely impacted by multiple climate extremes related to the water and thermal stress over the Tibetan Plateau(TP).However,it remains unknown at which stress level these climate extremes can trigger the abrupt shifts of vegetation response to climate extremes and result in the maximum vegetation response across TP.To fill this knowledge gap,we combined the hydrometeorological data and the satellite-derived vegetation index to detect two critical thresholds that determine the response of vegetation productivity to droughts,high-temperature extremes,and low-temperature extremes,respectively,during 2001-2018.Our results show that the response of vegetation productivity to droughts rapidly increases once crossing -1.41±0.6 standard deviation(σ)below the normal conditions of soil moisture.When crossing-2.98σ±0.9σ,vegetation productivity is maximum damaged by droughts.High-temperature extremes,which have the two thresholds of 1.34σ±0.4σand 2.31σ±0.4σover TP,are suggested to trigger the strong response of vegetation productivity at a milder stress level than low-temperature extremes(two thresholds:-1.44σ±0.5σand-2.53σ±0.8σ).Moreover,we found the compounded effects of soil moisture deficit in reducing the threshold values of both high-and low-temperature extremes.Based on the derived thresholds of climate extremes that impact vegetation productivity,Earth System Models project that southwestern TP and part of the northeastern TP will become the hotspots with a high exposure risk to climate extremes by 2100.This study deciphers the high-impact extreme climates using two important thresholds across TP,which advances the understanding of the vegetation response to different climate extremes and provides a paradigm for assessing the impacts of climate extremes on regional ecosystems.

Federated Edge Learning for the Wireless Physical Layer:Opportunities and Challenges

Deep learning(DL)has been applied to the physical layer of wireless communication systems,which directly extracts environment knowledge from data and outperforms conventional methods either in accuracy or computation complexity.However,most related research works employ centralized training that inevitably involves collecting training data from edge devices.The data uploading process usually results in excessive communication overhead and privacy disclosure.Alternatively,a distributed learning approach named federated edge learning(FEEL)is introduced to physical layer designs.In FEEL,all devices collaborate to train a global model only by exchanging parameters with a nearby access point.Because all datasets are kept local,data privacy is better protected and data transmission overhead can be reduced.This paper reviews the studies on applying FEEL to the wireless physical layer including channel state information acquisition,transmitter,and receiver design,which represent a paradigm shift of the DL-based physical layer design.In the meantime they also reveal several limitations inherent in FEEL,particularly when applied to the wireless physical layer,thus motivating further research efforts in the field.

Regulation of the water status in three co-occurring phreatophytes at the southern fringe of the Taklamakan Desert

Aims We investigated the regulation of the water status in three predominant perennial C3 phreatophytes(Alhagi sparsifolia,Populus euphratica,Tamarix ramosissima)at typical sites of their occurrence at the southern fringe of the hyperarid Taklamakan Desert(north-west China).Methods In the foreland of the river oasis of Qira(Cele),we determined meteorological variables,plant biomass production,plant water potentials(WL)and the water flux through the plants.We calculated the hydraulic conductance on the flow path from the soil to the leaves(kSL)and tested the effects of kSL,WL and the leaf-to-air difference in the partial pressure of water vapour(Dw)on stomatal regulation using regression analyses.Important Findings Despite high values of plant water potential at the point of turgor loss,all plants sustained WL at levels that were high enough to maintain transpiration throughout the growing season.In A.sparsifolia,stomatal resistance(rs;related to leaf area or leaf mass)was most closely correlated with kSL;whereas in P.euphratica,~70%of the variation in rs was explained by Dw.In T.ramosissima,leaf area-related rs was significantly correlated with WL and kSL.The regulation mechanisms are in accordance with the growth patterns and the occurrence of the species in relation to their distance to the ground water.

AI Enabled Wireless Communications with Real Channel Measurements:Channel Feedback

Artificial intelligence(AI)has shown great potential in wireless communications.AI-empowered communication algorithms have beaten many traditional algorithms through simulations.However,the existing works just use the simulated datasets to train and test the algorithms,which can not represent the power of AI in practical communication systems.Therefore,Peng Cheng Laboratory holds an AI competition,National Artificial Intelligence Competition(NAIC):AI+wireless communications,in which one of the topics is AI-empowered channel feedback system design using practical measurements.In this paper,we give a baseline neural network design,QuanCsiNet,for this competition,and the details of the channel measurements.QuanCsiNet shows excellent performance on channel feedback and the complexity of the neural networks is also given.