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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. 

Despite its prevalence in neurosensory systems for pattern recognition, event detection, and learning, the effects of sensory adaptation (SA) are not explored in reservoir computing (RC). Monazomycin‐based biomolecular synapse (MzBS) devices that exhibit volatile memristance and short‐term plasticity with two strength‐dependent modes of response are studied: facilitation and facilitation‐then‐depression (i.e., SA). Their ability to perform RC tasks including digit recognition, nonlinear function learning, and aerodynamic gust classification via combination of model‐based device simulations and physical experiments where SA presence is controlled is studied. Simulations exhibiting moderate SA achieve significantly higher accuracy classifying a custom 5 × 5 binary digit set, with experimental validation achieving maximum testing accuracies of 90%. Classifications of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset achieve a maximum testing accuracy of 94.34% in devices with SA. Fitting error of the Mackey–Glass time series is also significantly reduced by SA. Experimentally obtained pressure distributions representing gusts on an airfoil in a wind tunnel are classified by MzBS reservoirs. Reservoirs exhibiting SA achieve 100% accuracy, unlike MzBS reservoirs without SA and comparable static neural networks. 

Abstract Two-terminal memory elements, or memelements, capable of co-locating signal processing and memory via history-dependent reconfigurability at the nanoscale are vital for next-generation computing materials striving to match the brain’s efficiency and flexible cognitive capabilities. While memory resistors, or memristors, have been widely reported, other types of memelements remain underexplored or undiscovered. Here we report the first example of a volatile, voltage-controlled memcapacitor in which capacitive memory arises from reversible and hysteretic geometrical changes in a lipid bilayer that mimics the composition and structure of biomembranes. We demonstrate that the nonlinear dynamics and memory are governed by two implicitly-coupled, voltage-dependent state variables—membrane radius and thickness. Further, our system is capable of tuneable signal processing and learning via synapse-like, short-term capacitive plasticity. These findings will accelerate the development of low-energy, biomolecular neuromorphic memelements, which, in turn, could also serve as models to study capacitive memory and signal processing in neuronal membranes.