Lignin Nanoparticles as Pickering Stabilizers: Emulsion Engineering through Physicochemical Design

DOI: 10.1016/j.cis.2025.103752

Ronald Marquez1, Roberto J. Aguado1, Quim Tarrés1, Laura Tolosa2, Blaise L. Tardy3,*, Orlando J. Rojas4,5,6,*, Martin A. Hubbe7,*, Marc Delgado Aguilar1,* (Published in Advances in Colloid and Interface Science)

Abstract

This review systematically examines the principal physicochemical parameters that govern the formation, stability, and properties of Pickering emulsions stabilized by lignin nanoparticles (LNPs). We consider the role of particle size, charge and concentration, oil volume fraction, as well as emulsification variables (pH and shearing method). We demonstrate the broad applicability of fundamental physical chemistry principles in explaining the long-term stability of LNP-stabilized Pickering emulsions. LNPs with diameters of 25–50 nm, at concentrations between 0.2 and 2 wt.%, generally yield emulsions stable for over six months. This suggests that rapid interfacial coverage by smaller particles, facilitated by high-energy emulsification, is critical for preventing initial coalescence. Such emulsions are generally more stable at acidic to neutral pH (pH 3–7). In addition, more negative LNP zeta potentials (up to −64 mV) correlate with enhanced colloidal stability due to the electrostatic repulsion between oil droplets. Furthermore, LNP modification such as acetylation and polymer grafting can significantly enhance emulsion stability by balancing surface wettability (hydrophilicity/hydrophobicity) and interfacial activity. A meta-analysis and support vector regressor with Bayesian optimization and eXplainable artificial intelligence (AI) analysis confirmed high-energy emulsification (ultrasonication, high-shear mixing), pH, and packing parameter (LNPsize/LNPconc ratio) as the main features that can influence the formulation of emulsions and lead to smaller droplets and larger lifetimes. Finally, we propose heuristics to tailor LNP-stabilized Pickering emulsions that require stability and functionality, including those used in agriculture and crop protection, food, nutraceuticals, stimuli-responsive and energy systems, as well as coatings.

1. LNP Emulsion Data Explorer

Explore the relationship between any two variables from the database. Use the dropdown menus to select parameters for the X and Y axes. You can color the data points by a specific category (e.g., Lignin Type) and filter the data to isolate subsets of interest. Check the "Log" boxes to switch to a logarithmic scale. Click on any point to open the original article in a new tab.

2. Droplet Size Distribution

This box plot shows the distribution of the emulsion droplet size (in micrometers) grouped by different categories. It allows for a visual comparison of how a categorical variable (e.g., Lignin Type, Oil Type) influences the median droplet size and the variability of results reported in the literature.

3. Correlation Matrix

This heatmap visualizes the Pearson correlation coefficient between all numerical variables in the database. Warm colors (red) indicate a strong positive correlation, while cool colors (blue) indicate a strong negative correlation. A value near zero (white) suggests an absence of a linear correlation.