Lignin is one of the most abundant and inexpensive natural biopolymers. It can be efficiently converted to low cost carbon fiber, monolithic structures, or powders that could be used directly in the production of anodes for lithium-ion batteries. In this work, we report thermomechanical processing methods relevant for the conversion of lignin precursors into carbon fiber-based anode materials, the impact of lignin precursor modification on melt processing, and the microstructure of the final carbon material. Modification of softwood lignin produced functionalities and rheological properties that more closely resemble hardwood lignin thereby enabling the melt processing of softwood lignin in oxidative atmospheres (air). The conversion process encompasses melt spinning of the lignin precursor, oxidative stabilization, and a low temperature carbonization step in a nitrogen/hydrogen atmosphere. We determined resistivities of individual carbon fiber samples and characterized the microstructure by scanning electron microscopy. Neutron diffraction reveals nanoscale graphitic domains embedded in an amorphous carbon matrix. These unique structural characteristics make biomass-derived carbon fibers a suitable material for energy storage applications with enhanced electrochemical performance.

Most solid-state electrolytes exhibit significant structural disorder, which requires careful consideration when modeling the defect energetics. Here, we model the native defect chemistry of a disordered solid electrolyte, Li10GeP2S12.

We present an alternative and, for the purpose of non-crystalline materials design, a more suitable description of covalent and ionic glassy solids as statistical ensembles of crystalline local minima on the potential energy surface. Motivated by the concept of partially broken ergodicity, we analytically formulate the set of approximations under which the structural features of ergodic systems such as the radial distribution function (RDF) and powder X-ray diffraction (XRD) intensity can be rigorously expressed as statistical ensemble averages over different local minima. Validation is carried out by evaluating these ensemble averages for elemental Si and SiO2 over the local minima obtained through the first-principles random structure sampling that we performed using relatively small simulation cells, thereby restricting the sampling to a set of predominantly crystalline structures. The comparison with XRD and RDF from experiments (amorphous silicon) and molecular dynamics simulations (glassy SiO2) shows excellent agreement, thus supporting the ensemble picture of glasses and opening the door to fully predictive description without the need for experimental inputs.