Predicting Nanoparticle Suspension Viscoelasticity for Multimaterial 3D Printing of Silica-Titania Glass

A lack of predictive methodology is frequently a major bottleneck in materials development for additive manufacturing. Hence, exploration of new printable materials often relies on the serendipity of trial and error approaches, which is time-consuming, labor-intensive, and costly. We present an approach to overcome these issues by quantifying and controlling the viscoelasticity of inks for multimaterial 3D printing of silica-titania glass using direct ink writing (DIW). We formulate simple silica and silica-titania inks from a suspension of fumed silica nanoparticles in an organic solvent with a dissolved molecular titania precursor. We use a small set of experimental rheological data and estimates of interaction potentials from colloidal theory to develop a predictive tool that allows us to design and obtain compatible inks that are matched both in desired rheological properties (viscosity profiles and elastic modulus) as well as solids loading. The model incorporates silica particle volume fraction, particle size, particle size distribution, and titania precursor concentration, and captures the effects of all formulation parameters on the measured viscoelasticity in a single curve. We validate the ink formulations predicted by the model and find that the materials can be very well matched in rheological properties as desired for 3D printing. Using the DEN and heat treatment methods we have reported previously, we use these inks to print and process a fully transparent glass with spatial change in dopant composition and refractive index. We believe that this approach can be extended to other colloidal systems and allow predictive ink formulation design for desired printability in direct ink write manufacturing.