Biochar data into structure: A methodology for generating large-scale atomistic representations

Abstract

A well-defined methodology for constructing appropriate atomistic representations of biochar will aid in visualizing the structural features and elucidating biochar behavior with molecular dynamics (MD) simulations. Such knowledge will facilitate engineering biochars tailored to specific applications. To achieve this goal, we adapted modeling strategies applied in coal science by employing multi-cross-polarization 13C nuclear magnetic resonance, ultimate analysis, Fourier-transform infrared spectroscopy, and X-ray photoelectron spectroscopy to identify functional groups. Helium density, surface area, and porosity were used to assess structural features. Biochar's aromatic cluster size distribution was proposed based on data from the benzene polycarboxylic acid method. The computational framework reduces bias by incorporating chemical information derived from density functional theory, reactive MD simulations, and advanced characterization data. The construction approach was successfully applied to cellulose biochars produced at four temperatures, obtaining independent representations with a relative error on the atomic contents of <10 % for oxygen and nitrogen and <5 % for carbon and hydrogen. The atomistic representations were validated using X-ray diffraction, electron spin resonance data, and laser desorption/ionization Fourier-transform ion cyclotron resonance-mass spectrometry. The code will assist others in overcoming structural creation barriers and enable the utilization of the generated structures for further simulations.