  The optimal design of nanoparticle synthesis protocols is often achieved via
one-at-a-time experimental designs. Aside from covering a limited space for the
possible input conditions, these methods neglect possible interaction between
different combinations of input factors. This is where mechanistic models
embracing various possibilities find importance. By performing global
uncertainty/sensitivity analysis (UA/SA), one can map out the various outcomes
of the process vs. different combinations of operating conditions. Moreover,
UA/SA allows for the assessment of the model behavior, an inevitable step in
the theoretical understanding of a process. Recently, we developed a coupled
thermodynamic-kinetic framework in the form of population balance modelling in
order to describe the precipitation of calcium-silicate-hydrate. Besides its
relevance in the construction industry, this inorganic nanomaterial offers
potential applications in biomedicine, environmental remediation, and catalysis
most notably due to ample specific surface area that can be achieved by
carefully tuning the synthesis conditions. Here, we apply a global UA/SA to an
improved version of our computational model in order to understand the effect
of variations in the model parameters and experimental conditions (induced by
either uncertainty or tunability) on the properties of the product. With the
specific surface area of particles as an example, we show that UA/SA identifies
the factors whose control would allow a fine-tuning of the desired properties.
This way, we can rationalize the proper synthesis protocol before any further
attempt to optimize the experimental procedure. This approach is general and
can be transferred to other nanoparticle synthesis schemes as well.
