Multimodal Characterization of Acerola-Derived Gold Nanoparticles through Integrated Advanced Distributional Analysis

The green synthesis of gold nanoparticles (AuNPs) from plant extracts offers a sustainable route to obtaining functional nanomaterials. However, establishing robust relationships between structure and properties remains challenging due to the inherent heterogeneity of nanoparticle populations. In this study, AuNPs derived from acerola leaf extracts were synthesized under systematically varied conditions and characterized using a coupled experimental and distributional framework combining UV-vis spectroscopy, dynamic light scattering (DLS), ζ-potential and electrophoretic mobility measurements, FTIR spectroscopy, X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and LC-Orbitrap mass spectrometry of the acerola extract. To robustly capture size heterogeneity, unsupervised statistical learning was applied to the DLS data, enabling distribution-level modeling rather than relying on single-value descriptors. The resulting size distributions were modeled using kernel density estimation, selected using quantile-quantile analysis, log-likelihood, and Wasserstein distance metrics. This approach allowed for the resolution of multimodal populations and aggregation tails that are not captured solely by average diameters. Correlated analysis of electrokinetic behavior, surface functional groups, and TEM-derived size and shape metrics reveals that colloidal stability and optical responses are governed by the combined effects of surface chemistry and population dispersion. This integrative, distribution-aware methodology provides a reproducible strategy for evaluating green synthesis parameters and supports the rational development of AuNPs with controlled properties relevant to biomedical applications.