Machine learning-enabled feature classification of evaporation-driven multi-scale 3D printing.

The freeform generation of active electronics can impart advanced optical, computational, or sensing capabilities to an otherwise passive construct by overcoming the geometrical and mechanical dichotomies between conventional electronics manufacturing technologies and a broad range of three-dimensional (3D) systems. Previous work has demonstrated the capability to entirely 3D print active electronics such as photodetectors and light-emitting diodes by leveraging an evaporation-driven multi-scale 3D printing approach. However, the evaporative patterning process is highly sensitive to print parameters such as concentration and ink composition. The assembly process is governed by the multiphase interactions between solutes, solvents, and the microenvironment. The process is susceptible to environmental perturbations and instability, which can cause unexpected deviation from targeted print patterns. The ability to print consistently is particularly important for the printing of active electronics, which require the integration of multiple functional layers. Here we demonstrate a synergistic integration of a microfluidics-driven multi-scale 3D printer with a machine learning algorithm that can precisely tune colloidal ink composition and classify complex internal features. Specifically, the microfluidic-driven 3D printer can rapidly modulate ink composition, such as concentration and solvent-to-cosolvent ratio, to explore multi-dimensional parameter space. The integration of the printer with an image-processing algorithm and a support vector machine-guided classification model enables automated, in-situ pattern classification. We envision that such integration will provide valuable insights in understanding the complex evaporative-driven assembly process and ultimately enable an autonomous optimisation of printing parameters that can robustly adapt to unexpected perturbations.

3D Printing of Supramolecular Polymer Hydrogels with Hierarchical Structure.

Liquid crystalline hydrogels are an attractive class of soft materials to direct charge transport, mechanical actuation, and cell migration. When such systems contain supramolecular polymers, it is possible in principle to easily shear align nanoscale structures and create bulk anisotropic properties. However, reproducibly fabricating and patterning aligned supramolecular domains in 3D hydrogels remains a challenge using conventional fabrication techniques. Here, a method is reported for 3D printing of ionically crosslinked liquid crystalline hydrogels from aqueous supramolecular polymer inks. Using a combination of experimental techniques and molecular dynamics simulations, it is found that pH and salt concentration govern intermolecular interactions among the self-assembled structures where lower charge densities on the supramolecular polymers and higher charge screening from the electrolyte result in higher viscosity inks. Enhanced hierarchical interactions among assemblies in high viscosity inks increase the printability and ultimately lead to greater nanoscale alignment in extruded macroscopic filaments when using small nozzle diameters and fast print speeds. The use of this approach is demonstrated to create materials with anisotropic ionic and electronic charge transport as well as scaffolds that trigger the macroscopic alignment of cells due to the synergy of supramolecular self-assembly and additive manufacturing.

Hybrid 3D Printing of Soft Electronics.

Hybrid 3D printing is a new method for producing soft electronics that combines direct ink writing of conductive and dielectric elastomeric materials with automated pick-and-place of surface mount electronic components within an integrated additive manufacturing platform. Using this approach, insulating matrix and conductive electrode inks are directly printed in specific layouts. Passive and active electrical components are then integrated to produce the desired electronic circuitry by using an empty nozzle (in vacuum-on mode) to pick up individual components, place them onto the substrate, and then deposit them (in vacuum-off mode) in the desired location. The components are then interconnected via printed conductive traces to yield soft electronic devices that may find potential application in wearable electronics, soft robotics, and biomedical devices.