Abstract
This research article focuses on the targeted color design of silver-gold alloy nanoparticles (NPs), employing a multivariate optimization approach. NP synthesis involves interconnected process parameters, making independent variation challenging. Data-based property-process relationships are established to optimize optical properties effectively. We define a color target, employ a green chemical co-reduction method at room temperature and optimize process parameters accordingly. The CIEL*a*b* color space (Commission Internationale de l'Éclairage - International Commission on Illumination) and Euclidean distances facilitate accurate color matching to establish the property-process relationship. Concurrently, theoretical Mie calculations explore the structure-property relationship across particle sizes, concentrations, and molar gold contents. The theoretically optimal structure agrees very well with experimental particle structures at the property-process relationship's optimum. The data-driven property-process relationship provides valuable insights into the formation mechanism of a complex particle system, sheds light on the role of relevant process parameters and allows to evaluate the practically available property space. Model validation beyond the original grid demonstrates its robustness, yielding colors close to the target. Additionally, Design of Experiments (DoE) methods reduce experimental work by threefold with slight accuracy trade-offs. Our novel methodology for targeted color design demonstrates how data-based methods can be utilized alongside structure-property relationships to unravel property-process relationships in the design of complex nanoparticle systems and paves the way for future developments in targeted property design.