Back

Using Chaos for Facile High-throughput Fabrication of Ordered Multilayer Micro- and Nanostructures

Chavez-Madero, C.; Diaz de Leon-Derby, M.; Samandari, M.; Mendoza-Buenrostro, C. C.; Ceballos-Gonzalez, C. F.; Bolivar-Monsalve, E. J.; Holmberg, S.; Garza-Flores, N. A.; Almajhadi, M. A.; Gonzalez-Gamboa, I.; Yee-de Leon, J. F.; Martinez-Chapa, S. O.; Rodriguez, C. A.; Wickramasinghe, H. K.; Madou, M.; Khademhosseini, A.; Zhang, Y. S.; Alvarez, M. M.; Trujillo-de Santiago, G.

2019-11-08 bioengineering
10.1101/833772 bioRxiv
Show abstract

This paper introduces the concept of continuous chaotic printing, i.e., the use of chaotic flows for deterministic and continuous fabrication of fibers with internal multilayered micro-or nanostructures. Two free-flowing materials are coextruded through a printhead containing a miniaturized Kenics static mixer (KSM) composed of multiple helicoidal elements. This produces a fiber with a well-defined internal multilayer microarchitecture at high speeds (>1.0 m min-1). The number of mixing elements and the printhead diameter determine the number and thickness of the internal lamellae, which are generated according to successive bifurcations that yield a vast amount of inter-material surface area (~102 cm2 cm3) and high resolution features (~10 m). In an exciting further development, we demonstrate a scale-down of the microstructure by 3 orders of magnitude, to the nanoscale level (~10 nm), by feeding the output of a continuous chaotic 3D printhead into an electrospinner. Comparison of experimental and computational results demonstrates the robust and predictable output and performance of continuous chaotic 3D printing. The simplicity and high resolution of continuous chaotic printing strongly supports its potential use in novel applications, including--but not limited to--bioprinting of multi-scale tissue-like structures, modeling of bacterial communities, and fabrication of smart multi-material and multilayered constructs.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.