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We present Jammed Interconnected Bilayer Emulsions (JIBEs) as a class of tissue-like materials with macroscopic scalability and rapid fabrication, comprising millions to billions of bilayer-separated aqueous compartments. These materials closely mimic the organizational structure and properties of biological tissues. Our rapid self-assembly method for producing JIBEs generates milliliter- to deciliter-scale volumes within minutes representing over 10,000-fold improvement in the fabrication speed of droplet-based artificial tissues compared to existing droplet-based methods, enabling the creation of a truly macroscopic material. The method is highly adaptable to a wide range of amphiphiles, including lipids and block-copolymers, providing flexibility in tailoring JIBEs for diverse applications. The jammed architecture of JIBEs imparts unique properties, such as direct 3D-printabilty into aqueous solutions or onto air-exposed surfaces. Their membrane-bound structure also allows functionalization with biological and artificial nanochannels, enabling the material to exhibit the specific properties of the incorporated channels. In this work, we demonstrate three key features of JIBEs using distinct ion channels: tunable conductance, selective transport, and memristance. Incorporating an E. coli outer membrane protein increased ionic conductance by approximately 4,400-fold compared to non-functionalized tissues. Introducing a peptide-based transporter produced ion-selective membranes capable of discriminating ammonium over sodium at a ratio greater than 15:1. Finally, incorporating a model voltage-gated pore enabled the construction of a massively networked memristive device. We propose that functionalizing JIBEs with additional membrane proteins or synthetic ion channels could unlock a broad range of applications, including separations, energy generation and storage, neuromorphic computing, tissue engineering, drug delivery, and soft robotics. 

Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional methods may fail. However, because nano-indentation does not yield a homogeneous deformation field, interpreting the resulting load–displacement curves is non-trivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load–displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neo-Hookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nano-indentation on soft materials.