Thiospinels,
such as CoNi2S4, are showing
promise for numerous applications, including as catalysts for the
hydrogen evolution reaction, hydrodesulfurization, and oxygen evolution
and reduction reactions; however, CoNi2S4 has
not been synthesized as small, colloidal nanocrystals with high surface-area-to-volume
ratios. Traditional optimization methods to control nanocrystal attributes
such as size typically rely upon one variable at a time (OVAT) methods
that are not only time and labor intensive but also lack the ability
to identify higher-order interactions between experimental variables
that affect target outcomes. Herein, we demonstrate that a statistical
design of experiments (DoE) approach can optimize the synthesis of
CoNi2S4 nanocrystals, allowing for control over
the responses of nanocrystal size, size distribution, and isolated
yield. After implementing a 25–2 fractional factorial
design, the statistical screening of five different experimental variables
identified temperature, Co:Ni precursor ratio, Co:thiol ratio, and
their higher-order interactions as the most critical factors in influencing
the aforementioned responses. Second-order design with a Doehlert
matrix yielded polynomial functions used to predict the reaction parameters
needed to individually optimize all three responses. A multiobjective
optimization, allowing for the simultaneous optimization of size,
size distribution, and isolated yield, predicted the synthetic conditions
needed to achieve a minimum nanocrystal size of 6.1 nm, a minimum
polydispersity (σ/d̅) of 10%, and a maximum
isolated yield of 99%, with a desirability of 96%. The resulting model
was experimentally verified by performing reactions under the specified
conditions. Our work illustrates the advantage of multivariate experimental
design as a powerful tool for accelerating control and optimization
in nanocrystal syntheses.