The impact of magnesium content on lithium-magnesium alloy electrode performance with argyrodite solid electrolyte.

Solid-state lithium-based batteries offer higher energy density than their Li-ion counterparts. Yet they are limited in terms of negative electrode discharge performance and require high stack pressure during operation. To circumvent these issues, we propose the use of lithium-rich magnesium alloys as suitable negative electrodes in combination with Li6PS5Cl solid-state electrolyte. We synthesise and characterise lithium-rich magnesium alloys, quantifying the changes in mechanical properties, transport, and surface chemistry that impact electrochemical performance. Increases in hardness, stiffness, adhesion, and resistance to creep are quantified by nanoindentation as a function of magnesium content. A decrease in diffusivity is quantified with 6Li pulsed field gradient nuclear magnetic resonance, and only a small increase in interfacial impedance due to the presence of magnesium is identified by electrochemical impedance spectroscopy which is correlated with x-ray photoelectron spectroscopy. The addition of magnesium aids contact retention on discharge, but this must be balanced against a decrease in lithium diffusivity. We demonstrate via electrochemical testing of symmetric cells at 2.5 MPa and 30∘C that 1% magnesium content in the alloy increases the stripping capacity compared to both pure lithium and higher magnesium content alloys by balancing these effects.

A reaction-diffusion framework for hydrolytic degradation of amorphous polymers based on a discrete chain scission model.

Hydrolytic degradation of polymers involves the scission of long chain molecules, leading to molecular weight reduction and mass loss. The precise degradation response however depends on the scission probability of individual bonds along the polymer backbone. In particular, bonds near the chain ends are considered to be more susceptible to hydrolysis than inner bonds. In this paper, we incorporate a discrete chain scission model that can handle arbitrary bond scission probabilities within a continuum reaction-diffusion framework. Overall hydrolysis kinetics (including autocatalysis) is described independently of the chain scission model. By decoupling the description of the chain scission mechanism from kinetics, our framework enables the identification of the chain scission mechanism from molecular weight reduction and mass loss curves commonly reported in experimental degradation studies. We further propose a reduced continuum model which is better suited for large-scale simulations while retaining the predictive capability of the full discrete-continuum model. The model capability is illustrated in representative case studies based on experimental data from the literature for different materials and geometries. STATEMENT OF SIGNIFICANCE: Many models have been proposed to predict the evolution of molecular weight and mass loss in biodegradable polymers undergoing hydrolytic degradation. However, existing models remain limited in their ability to describe the degradation mechanism, autocatalytic kinetics and short chains diffusion simultaneously. Moreover, existing models often rely on empirical relations and a large number of fitting parameters. Here, we propose a conceptually simple discrete-continuum mathematical framework with a small number of parameters which all have a clear physical meaning. Model calibration against experimental data is simplified, and further provides insights into the degradation mechanisms at play, namely random scission, chain-end scission, or a combination of both. The framework can serve as a basis for future generalisations, including a description of evolving crystallinity, or other degradation mechanisms, such as thermal oxidation or photo-degradation.

Reversible fusion and fission of graphene oxide-based fibers.

Stimuli-responsive fusion and fission are widely observed in both bio-organizations and artificial molecular assemblies. However, the design of a system with structure and property persistence during repeated fusion and fission remains challenging. We show reversible fusion and fission of wet-spun graphene oxide (GO) fibers, in which a number of macroscopic fibers can fuse into a thicker one and can also separate into original individual fibers under stimulation of solvents. The dynamic geometrical deformation of GO fiber shells, caused by solvent evaporation and infiltration, is the key to the reversible fusion-fission cycles. This principle is extended to implement flexible transitions between complex fiber assemblies and the inclusion or expulsion of guest compounds.

Relative contributions of chain density and topology to the elasticity of two-dimensional polymer networks.

Understanding the relationships between the structure of polymer networks and their mechanical properties is important for the design of advanced soft materials with optimal properties. However, classical rubber elasticity theories often fall short in their description of the network structure, while simulation techniques at molecular scale remain impractical at that length scale. Here we develop a computational approach based on random discrete networks, in which the polymer network is represented as an assembly of non-linear springs connected at crosslinking points. The density of elastically-effective chains, average network coordination and chain contour lengths are varied independently in order to identify their respective contributions to the network elasticity. Numerical results suggest scaling relations between network parameters and elastic properties that are markedly different from the predictions of classical rubber elasticity theories. In particular, the elastic modulus of 2D random networks is found to be independent of density at constant topology, and proportional to the average coordination at constant density. The discrepancy is due to the pre-straining of the chains in the discrete network, which is not accounted for in classical models of rubber elasticity. Our results have implications for the interpretation of experimental data for ideal network gels that are formed by the cross-coupling of macromolecular building blocks in solution.

Surface coating mediated swelling and fracture of silicon nanowires during lithiation.

Surface passivation of silicon anodes is an appealing design strategy for the development of reliable, high-capacity lithium-ion batteries. However, the structural stability of the coating layer and its influence on the lithiation process remain largely unclear. Herein, we show that surface coating mediates the swelling dynamics and the fracture pattern during initial lithiation of crystalline silicon nanopillars. We choose conformally nickel coated silicon architectures as a model system. Experimental findings are interpreted based on a chemomechanical model. Markedly different swelling and fracture regimes have been identified, depending on the coating thickness and silicon nanopillar diameter. Nanopillars with relatively thin coating display anisotropic swelling similar to pristine nanopillars, but with different preferred fracture sites. As the coating thickness increases, the mechanisms become isotropic, with one randomly oriented longitudinal crack that unzips the core-shell structure. The morphology of cracked pillars resembles that of a thin-film electrode on a substrate, which is more amenable to cyclic lithiation without fracture. The knowledge provided here helps clarify the cycling results of coated nanosilicon electrodes and further suggests design rules for better performance electrodes through proper control of the lithiation and fracture.