Experimental and Simulation Study of the Solvent Effects on the Intrinsic Properties of Spherical..

Full title: Experimental and Simulation Study of the Solvent Effects on the Intrinsic Properties of Spherical Lignin Nanoparticles


Tao Zou, Nonappa, Mohammad Khavani, Maisa Vuorte, Paavo Penttilä, Aleksi Zitting, Juan Jose Valle-Delgado, Anna Maria Elert, Dorothee Silbernagl, Mikhail Balakshin, Maria Sammalkorpi, Monika Österberg


The Journal of Physical Chemistry B, 2021, 125, 44, 12315–12328



Abstract: Spherical lignin nanoparticles (LNPs) fabricated via nanoprecipitation of dissolved lignin are among the most attractive biomass-derived nanomaterials. Despite various studies exploring the methods to improve the uniformity of LNPs or seeking more application opportunities for LNPs, little attention has been given to the fundamental aspects of the solvent effects on the intrinsic properties of LNPs. In this study, we employed a variety of experimental techniques and molecular dynamics (MD) simulations to investigate the solvent effects on the intrinsic properties of LNPs. The LNPs were prepared from softwood Kraft lignin (SKL) using the binary solvents of aqueous acetone or aqueous tetrahydrofuran (THF) via nanoprecipitation. The internal morphology, porosity, and mechanical properties of the LNPs were analyzed with electron tomography (ET), small-angle X-ray scattering (SAXS), atomic force microscopy (AFM), and intermodulation AFM (ImAFM). We found that aqueous acetone resulted in smaller LNPs with higher uniformity compared to aqueous THF, mainly ascribing to stronger solvent–lignin interactions as suggested by MD simulation results and confirmed with aqueous 1,4-dioxane (DXN) and aqueous dimethyl sulfoxide (DMSO). More importantly, we report that both LNPs were compact particles with relatively homogeneous density distribution and very low porosity in the internal structure. The stiffness of the particles was independent of the size, and the Young’s modulus was in the range of 0.3–4 GPa. Overall, the fundamental understandings of LNPs gained in this study are essential for the design of LNPs with optimal performance in applications.



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