Conductance stable and mechanically durable bi-layer EGaIn composite-coated stretchable fiber for 1D bioelectronics
By Joseph L. Watson, David Juergens, N. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, A. J. Borst, R. Ragotte, L. Milles and 18 more. Nature Communications, 2023
599 citations recorded by the source corpus.
Deformable semi-solid liquid metal particles (LMP) have emerged as a promising substitute for rigid conductive fillers due to their excellent electrical properties and stable conductance under strain. However, achieving a compact and robust coating of LMP on fibers remains a persistent challenge, mainly due to the incompatibility of conventional coating techniques with LMP. Additionally, the limited durability and absence of initial electrical conductivity of LMP restrict their widespread application. In this study, we propose a solution process that robustly and compactly assembles mechanically durable and initially conductive LMP on fibers. Specifically, we present a shearing-based deposition of polymer-attached LMP followed by additional coating with CNT-attached LMP to create bi-layer LMP composite with exceptional durability, electrical conductivity, stretchability, and biocompatibility on various fibers. The versatility and reliability of this manufacturing strategy for 1D electronics are demonstrated through the development of sewn electrical circuits, smart clothes, stretchable biointerfaced fiber, and multifunctional fiber probes.
Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/a294b5b62a28f304e4995f067d47878fb547d7ec
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
Citations in the corpus
- A Survey on Generative Diffusion Models (2022, background): “Structure-based cross-modal methods leverage prior knowledge from structure prediction models to assist in precisely guided generation, combining protein sequences and functional information [204].”