Using the regression analysis for each of the response variables, the models shown herein should allow the production of single- Table Conditions of validation experiments with predicted (pred.) and observed (obs.) responses, guided by prediction expression of regression analysis. Note that the predicted error was taken from the prediction profiler, from JMP software, which uses the prediction expression to predict the outcome of a response using the conditions outlined above. The observed error was calculated from the standard deviation of each sample set. core@shell NPs with controlled shell thickness, good monodispersity and with high populations. In order to test the models, the prediction expressions were combined and three validation experiments were conducted, aiming to produce SPION@SiO 2 NPs with total sizes of 50 nm, having a shell thickness of 18 nm, classified as monodisperse, and with a high population of core@shell particles. For these studies, the experimental conditions were taken at different coordinates of the experimental design, as seen in Table and Fig. , demonstrating that different experimental conditions may lead to similar particles being produced. Size distribution of the measured nanoparticle diameter from TEM (d TEM ), can be found in Fig. , ESI. The mass susceptibility of the nanoparticles is given in Fig. , ESI. The silica shell thickness of the three validation experiments were found to be 14±3 nm, 28±3 nm, and 22±3 nm, respectively, as seen in Fig 7 . Run 1 and 3 were in good agreement with the predicted thickness, as observed in Fig. , whereas run 2 was larger than anticipated. There was strong agreement between the predicted and observed nanoparticle entropy, with all particles observed to be monodisperse. Furthermore there was also strong agreement between the predicted and observed population, which achieved populations of SPION@SiO 2 >50 % for the three samples. Run 1 used 2.69 mmol of TEOS, across 3 fractionated additions, using 0.40 mmol NH 4 OH. This supports the regression analysis for each of the responses: the silica shell thickness was dependent on the number of moles of TEOS and the combined interaction of number of moles of NH 4 OH and fractionated addition of TEOS, where the shell thickness increased as one of these terms increased. The particle dispersity was also dependent on the aforementioned factors, which decreased as the terms increased (becoming more monodisperse). Section 3.3.3 described the population of SPION@SiO 2 to be dependent on the interaction of TEOS number of moles and the number of moles of NH 4 OH, where an increase in population when one number of moles was high, and the other number of moles was low resulted in high population yield, reflective of the conditions used in run 1. Similar observations were also observed for run 2 and 3, which used higher number of moles of NH 4 OH (1.16 mmol), and lower number of moles of TEOS (0.98 mmol and 0.63 mmol, respectively) across more fractionated additions of TEOS (7 and 8 respectively). Interestingly here, particles of similar morphology, size and characteristics have been produced using different experimental conditions. These validation experiments demonstrate that DoE is valuable for the optimisation of this nanoparticle synthesis and allow flexibility for users to achieve their desired optimised NP. DoE allows accurate prediction across a variety of parameter combinations which can be of use in scenarios where experimental set ups are limited, or can aid in considering scaleup of particle synthesis.