Species Distribution During Solid Electrolyte Interphase Formation on Lithium Using MD/DFT-Parameterized Kinetic Monte Carlo Simulations

Lithium metal batteries are one of the promising technologies for future energy storage. One open challenge is the generation of a stable and well performing Solid Electrolyte Interphase (SEI) between lithium metal and electrolyte. Understanding the complex interaction of reactions at the lithium surface and the resulting SEI is crucial for knowledge-driven improvement of the SEI. This study reveals the internal species distribution and geometrical aspects of the native SEI during formation by model-based analysis. To achieve this, a combination of molecular dynamics, density functional theory, and stand-alone 3D-kinetic Monte Carlo simulations is used. The kinetic Monte Carlo model determines the SEI growth features over a long time and length scale so that the SEI can be analyzed quantitatively. The simulation confirms the frequently postulated layered SEI structure arising from the decomposition of an ethylene carbonate/lithium hexafluorophosphate (2 M) electrolyte with lithium metal. These layers are not clearly separated, which is contrary to what is often reported. The gradient distribution of the species within the SEI therefore corresponds to a partly mosaic structured SEI at the borders of the layers. At the lithium surface, an inorganic layer of lithium fluoride and then lithium carbonate is observed, followed by an organic, more porous SEI layer consisting of lithium ethylene dicarbonate. Simulations further reveal the strong prevalence of corrosion processes of the metal, which provide more than 99% of the lithium for the SEI reaction processes. The salt contributes less than 1% to the SEI formation. Additionally, SEI formation below and above the initial interface was observable. The here presented novel modeling approach allows an unprecedented in-depth analysis of processes during native SEI formation that can be used to improve design for high battery performance and durability

Non-Cooperative Game in Block Bidding Markets Considering Demand Response

With the reform of electricity markets, demand response (DR) plays an important role in providing flexibility to the markets. Block bidding market is a new market mode, which is based on the concept of “the same quality, the same price”. The mechanism has great effects in reducing start-stop related costs. In this paper, we propose a double-sided non-cooperative game model in block bidding markets with a DR program. The model combines the advantages of block bidding and the simplicity of hourly bidding. In the model, one side is the non-cooperative game of supply-side power firms, and we propose a novel supply function bidding model based on block duration and load capacity to maximize each firm’s profit. The other side is the demand-side different types of customers, and we propose a DR model that combines hourly-various prices with the block bidding mechanism to maximize each customer’s payoff. The overall market optimization problem is solved by a distributed iterative algorithm, which has great convergence performance. We verify the proposed model on real data, and the results show that the demand load curve becomes flattened with DR, and the total generation costs decrease while the social welfare is significantly improved

Structural Engineering of Hierarchical Aerogels Comprised of Multi-dimensional Gradient Carbon Nanoarchitectures for Highly Efficient Microwave Absorption

Abstract Recently, multilevel structural carbon aerogels are deemed as attractive candidates for microwave absorbing materials. Nevertheless, excessive stack and agglomeration for low-dimension carbon nanomaterials inducing impedance mismatch are significant challenges. Herein, the delicate “3D helix–2D sheet–1D fiber–0D dot” hierarchical aerogels have been successfully synthesized, for the first time, by sequential processes of hydrothermal self-assembly and in-situ chemical vapor deposition method. Particularly, the graphene sheets are uniformly intercalated by 3D helical carbon nanocoils, which give a feasible solution to the mentioned problem and endows the as-obtained aerogel with abundant porous structures and better dielectric properties. Moreover, by adjusting the content of 0D core–shell structured particles and the parameters for growth of the 1D carbon nanofibers, tunable electromagnetic properties and excellent impedance matching are achieved, which plays a vital role in the microwave absorption performance. As expected, the optimized aerogels harvest excellent performance, including broad effective bandwidth and strong reflection loss at low filling ratio and thin thickness. This work gives valuable guidance and inspiration for the design of hierarchical materials comprised of dimensional gradient structures, which holds great application potential for electromagnetic wave attenuation

Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium

Background: Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. Results: The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14 x 10(-3)). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naive (FEDN) and non-FEDN patients. Conclusions: Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD