Multivariate optimization and characterization of graphene oxide via design of experiments and chemometric analysis

Controlling the structure and properties of graphene oxide (GO) remains a challenge due to the poor reproducibility of conventional synthetic protocols and limited understanding of parameter-property relationships. In this study, we present an integrated analytical framework that combines Design of Experiments (DoE) with chemometric modelling to systematically assess the effects of eight synthesis variables on GO's physicochemical and functional features. A Plackett–Burman experimental design enabled efficient screening of synthesis conditions, while comprehensive characterization (spanning UV–Vis spectroscopy, XPS, SEM–EDX, TEM–EDX, and XRD) was coupled with multivariate tools (Principal Component Analysis and Multiple Linear Regression) to identify statistically significant correlations between synthetic inputs and material responses. Notably, we demonstrate that UV–Vis spectra can serve as a robust proxy for oxidation state, offering a rapid and accessible alternative to surface-sensitive methods. The approach yields a predictive analytical toolkit for guiding GO synthesis and highlights a generalizable strategy for the rational design of flat nanomaterials. This work supports reproducible, resource-efficient material development aligned with Safe and Sustainable by Design (SSbD) principles. • Design of Experiments was applied to optimize graphene oxide synthesis conditions • Twelve GO samples were synthesized via a controlled Tours' method • Structural and optical properties were systematically characterized by multiple techniques • PCA and MLR were used to correlate synthesis parameters with GO features • The multivariate approach enables predictive control over GO material design