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Code in support of: Physical and chemical mechanisms that influence the electrical conductivity of lignin-derived biochar

Citation

Kane, Seth et al. (2021), Code in support of: Physical and chemical mechanisms that influence the electrical conductivity of lignin-derived biochar, Dryad, Dataset, https://doi.org/10.5061/dryad.fj6q573v2

Abstract

Lignin-derived biochar is a promising, sustainable alternative to petroleum-based carbon powders (e.g., carbon black) for electrode and energy storage applications. Prior studies of these biochars demonstrate that high electrical conductivity and good capacitive behavior are achievable. These studies also show high variability in electrical conductivity between biochars (~10^-2-10^2 S/cm). The underlying mechanisms that lead to desirable electrical properties in these lignin-derived biochars are poorly understood. In this work, we examine the causes of the variation in conductivity of lignin-derived biochar to optimize the electrical conductivity of lignin-derived biochars. To this end, we produced biochar from three different lignins, a whole biomass source (wheat stem), and cellulose at two pyrolysis temperatures (900 C, 1100  C). These biochars have a similar range of conductivities (0.002 to 18.51 S/cm) to what has been reported in the literature. Results from examining the relationship between chemical and physical biochar properties and electrical conductivity indicate that decreases in oxygen content and changes in particle size are associated with increases in electrical conductivity. Lignin isolated with an acidification process yielded biochar with higher electrical conductivity than lignin isolated with sulfate processes. These findings indicate how lignin composition and processing may be further selected and optimized to target specific energy-related applications.

Methods

This code was produced by Rachel Ulrich for the statistical analysis presented in the article. Please see the methods and Supplemental Methods for a complete description of the statistical model applied in this code. https://doi.org/10.1016/j.cartre.2021.100088

Usage Notes

This code is written in R and is designed to run in RStudio Version 1.4.1106

emmeans, lmerTest, lme4, and Matrix packages are required to run this code, (see https://doi.org/10.1016/j.cartre.2021.100088 for full citations of these packages).

Funding

National Science Foundation, Award: DMS-1748883

National Science Foundation, Award: ECCS-1542210