Towards a predictive understanding of direct ink writing of graphene-based inks

Abstract

Direct ink writing (DIW) presents a flexible and resource-efficient approach towards the prototyping of functional materials and devices with complex shapes. Printed functional materials for electronic devices depend on conductive fillers such as graphene nanoplatelets (GNPs), which are increasingly popular in printed electronics and energy materials thanks to their low cost, non-toxicity and high specific surface area. However, non-spherical colloids with large filler-to-nozzle size ratios like GNPs present a challenge for high-resolution DIW due to risk of nozzle clogging. As DIW of platelet-based inks is gaining traction in several fields, the feasibility of high-resolution DIW of platelet-based inks is demonstrated here on the example of GNPs (< 50 μm). A workflow for the combined optimization of ink rheology and printing process parameters was developed to gain a predictive understanding of filament quality and morphology. Using two inks and two nozzle diameters per ink, filaments ranging from <100 – 1200 μm in width and 30 – 300 μm in height were produced, with conductivities suitable for application in sensors or electrodes. The derived predictive models were successfully deployed to predict filament dimensions and to achieve excellent print quality even for fine sub-nozzle size structures with very high filler-to-nozzle size ratios within only one iteration of the workflow. With this study, we advocate for the integrated development of materials for processes and processes for materials. This study will benefit high-resolution rapid prototyping of a large class of functional materials for wearable electronics, sensors, RF passives, energy materials and tissue engineering.