Accelerating lithium-sulfur battery reaction kinetics and inducing 3D deposition of Li_(2)S using interactions between Fe_(3)Se_(4)and lithium polysulfides

Although lithium-sulfur batteries(LSBs)exhibit high theoretical energy density,their practical application is hindered by poor conductivity of the sulfur cathode,the shuttle effect,and the irreversible deposition of Li_(2)S.To address these issues,a novel composite,using electrospinning technology,consisting of Fe_(3)Se_(4)and porous nitrogen-doped carbon nanofibers was designed for the interlayer of LSBs.The porous carbon nanofiber structure facilitates the transport of ions and electrons,while the Fe_(3)Se_(4)material adsorbs lithium polysulfides(LiPSs)and accelerates its catalytic conversion process.Furthermore,the Fe_(3)Se_(4)material interacts with soluble LiPSs to generate a new polysulfide intermediate,Li_(x)FeS_(y)complex,which changes the electrochemical reaction pathway and facilitates the three-dimensional deposition of Li_(2)S,enhancing the reversibility of LSBs.The designed LSB demonstrates a high specific capacity of1529.6 mA h g^(-1)in the first cycle at 0.2 C.The rate performance is also excellent,maintaining an ultra-high specific capacity of 779.7 mA h g^(-1)at a high rate of 8 C.This investigation explores the mechanism of the interaction between the interlayer and LiPSs,and provides a new strategy to regulate the reaction kinetics and Li_(2)S deposition in LSBs.

A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

Spatiotemporal Input Control:Leveraging Temporal Variation in Network Dynamics

The number of available control sources is a limiting factor to many network control tasks.A lack of input sources can result in compromised controllability and/or sub-optimal network performance,as noted in engineering applications such as the smart grids.The mechanism can be explained by a linear timeinvariant model,where structural controllability sets a lower bound on the number of required sources.Inspired by the ubiquity of time-varying topologies in the real world,we propose the strategy of spatiotemporal input control to overcome the source-related limit by exploiting temporal variation of the network topology.We theoretically prove that under this regime,the required number of sources can always be reduced to 2.It is further shown that the cost of control depends on two hyperparameters,the numbers of sources and intervals,in a trade-off fashion.As a demonstration,we achieve controllability over a complex network resembling the nervous system of Caenorhabditis elegans using as few as 6%of the sources predicted by a static control model.This example underlines the potential of utilizing topological variation in complex network control problems.

Quantitative Biology 2019: Dynamic Signaling in Cells and Embryos

The International Conference on"Quantitative Biology 2019:Dynamic Signaling in Cells and Embryos"was successfully held at Dongshan Hotel,Yantai,China,from June 22nd to 24th.The conference was co-sponsored by the Center for Quantitative Biology(CQB)of Peking University and Yantai University and was organized by Prof.Chao Tang,Prof.Feng Liu,and Prof.Yihan Lin from Peking University,China,Prof.Michael B.Elowitz from Caltech,USA,and Prof.Yibao Chen and Prof.Xuran Wu from Yantai University,China.Totally 26 experts in the field of quantitative biology were invited to deliver talks from Canada,China,France,Germany,Israel,Japan,Singapore,South Korea,Spain,and United States.A total of 267 researchers worldwide attended the conference,and participated in diverse conference activities,including invited and contributed talks,flash talks,poster presentations,and panel discussions.