ABSTRACT
As the most abundant renewable aromatic polymer on the planet, lignin is gaining growing interest in replacing petroleum-based chemicals and products. However, only <5 % of industrial lignin waste is revalorized in its macromolecular form as additives, stabilizing agents or dispersant and surfactants. Herein, revalorization of this biomass was achieved by implementing an environmentally-friendly continuous sonochemical nanotransformation to obtain highly concentrated lignin nanoparticles (LigNPs) dispersions for added-value material applications. With the aim to further model and control a large-scale ultrasound-assisted lignin nanotransformation, a two-level factorial design of experiment (DoE) was implemented varying the ultrasound (US) amplitude, flow rate, and lignin concentration. Size and polydispersity measurements together with the UV-Vis spectra of lignin recorded at different time intervals of sonication allowed to monitor and understand the sonochemical process on a molecular level. The light scattering profile of sonicated lignin dispersions showed a significant particle size reduction in the first 20 min, followed by moderate particle size decrease below 700 nm until the end of the 2 h process. The response surface analysis (RSA) of the particle size data revealed that the lignin concentration and sonication time were the most important factors to achieve smaller NPs. From a mechanistic point of view, a strong impact of the particle-particle collisions due to sonication seems to be responsible for the decrease in particle size and homogenization of the particle distribution. Unexpectedly, a strong interaction between the flow rate and US amplitude on the particle size and nanotransformation efficiency was observed, yielding smaller LigNPs at high amplitude and low flow rate or vice versa. The data derived from the DoE were used to model and predict the size and polydispersity of the sonicated lignin. Furthermore, the use of the NPs spectral process trajectories calculated from the UV-Vis spectra showed similar RSA model as the dynamic light scattering (DLS) data and will potentially allow the in-line monitoring of the nanotransformation process.
