Lignin, an abundant organic polymer and a byproduct of pulp and biofuel production, has potential applications owing to its high carbon content and aromatic structure. Processing–structure relationships are difficult to predict because of the heterogeneity of lignin. This work discusses the roles of unit operations in the carbonization process of softwood lignin, and their resulting impacts on the material structure and electrochemical properties in application as the anode in lithium‐ion cells. The processing variables include the lignin source, temperature, and duration of thermal stabilization, pyrolysis, and reduction. Materials are characterized at the atomic and microscales. High‐temperature carbonization, at 2000 °C, produces larger graphitic domains than at 1050 °C, but results in a reduced capacity. Coulombic efficiencies over 98 % are achieved for extended galvanostatic cycling. Consequently, a properly designed carbonization process for lignin is well suited for the generation of low‐cost, high‐efficiency electrodes.
Composite materials possessing both crystalline and amorphous domains, when subjected to X‐ray and neutron scattering, generate diffraction patterns that are often difficult to interpret. One approach is to perform atomistic simulations of a proposed structure, from which the analogous diffraction pattern can be obtained for validation. The structure can be iteratively refined until simulation and experiment agree. The practical drawback to this approach is the significant computational resources required for the simulations. In this work, an alternative approach based on a hierarchical decomposition of the radial distribution function is used to generate a physics‐based model allowing rapid interpretation of scattering data. In order to demonstrate the breadth of this approach, it is applied to a series of carbon composites. The model is compared with atomistic simulation results in order to demonstrate that the contributions of the crystalline and amorphous domains, as well as their interfaces, are correctly captured. Because the model is more efficient, additional structural refinement is performed to increase the agreement of the simulation result with the experimental data. The model achieves a reduction in computational effort of six orders of magnitude relative to simulation. The model can be generally extended to other composite materials.
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