Review on Characterization of Biochar Derived from Biomass Pyrolysis via Reactive Molecular Dynamics Simulations

Biochar is a carbon-rich solid produced during the thermochemical processes of various biomass feedstocks. As a low-cost and environmentally friendly material, biochar has multiple significant advantages and potentials, and it can replace more expensive synthetic carbon materials for many applications in nanocomposites, energy storage, sensors, and biosensors. Due to biomass feedstock species, reactor types, operating conditions, and the interaction between different factors, the compositions, structure and function, and physicochemical properties of the biochar may vary greatly, traditional trial-and-error experimental approaches are time consuming, expensive, and sometimes impossible. Computer simulations, such as molecular dynamics (MD) simulations, are an alternative and powerful method for characterizing materials. Biomass pyrolysis is one of the most common processes to produce biochar. Since pyrolysis of decomposing biomass into biochar is based on the bond-order chemical reactions (the breakage and formation of bonds during carbonization reactions), an advanced reactive force field (ReaxFF)-based MD method is especially effective in simulating and/or analyzing the biomass pyrolysis process. This paper reviewed the fundamentals of the ReaxFF method and previous research on the characterization of biochar physicochemical properties and the biomass pyrolysis process via MD simulations based on ReaxFF. ReaxFF implicitly describes chemical bonds without requiring quantum mechanics calculations to disclose the complex reaction mechanisms at the nano/micro scale, thereby gaining insight into the carbonization reactions during the biomass pyrolysis process. The biomass pyrolysis and its carbonization reactions, including the reactivity of the major components of biomass, such as cellulose, lignin, and hemicellulose, were discussed. Potential applications of ReaxFF MD were also briefly discussed. MD simulations based on ReaxFF can be an effective method to understand the mechanisms of chemical reactions and to predict and/or improve the structure, functionality, and physicochemical properties of the products.