(570e) Chaotic Printing: Using Chaos to Fabricate Densely Packed Micro- and Nanostructure at High Resolution and Speed

Nature generates densely packed micro- and nanostructures to enable key functionalities in cells, tissues, and other materials. Current fabrication techniques, due to limitations in resolution and speed, are far less effective at creating microstructures. Yet, the development of extensive amounts of surface area per unit of volume will enable applications and manufacturing strategies not possible today. Here we describe the use of chaotic flows for rapid generation of complex, high-resolution microstructures, a technique that we call chaotic printing.

We use two classic mixing systems, the Journal Bearing (JB) Flow and the Kenics static mixer (KSM), as models to demonstrate the use of chaotic printing. In a miniaturized JB flow (miniJB), we induced deterministic chaotic flows in viscous liquids. These flows deform an “ink” (i.e., a drop of a miscible liquid, fluorescent beads, or cells) at an exponential rate to render a densely packed lamellar microstructure that is then preserved by curing or photocrosslinking. In a continuous version of chaotic printing, we created chaotic flows by coextruding two streams of alginate (two inks) through a printing head containing an online miniaturized Kenics static mixer with multiple mixing elements (or sections). In this way, we continuously 3D-printed multi-material lamellar structures with different degrees of surface area (as a function of the number of elements used) and full spatial control of the internal microstructure. The combined outlet stream was then submerged in a calcium chloride solution in order to cross-link the emerging alginate fibers and preserve the microstructure.

We show that the exponentially fast creation of fine microstructure achievable through chaotic printing exceeds the limits of resolution and speed of the currently available 3D-printing techniques. Moreover, we show that the architecture of the microstructure to be created with chaotic printing can be predicted using computational fluid dynamic (CFD) techniques.

We present different proof-of-principle applications for this technology, including the development of densely packed biocatalytic surfaces and highly complex multi-lamellar and multi-component tissue-like structures for biomedical applications