The droplet interface bilayer (DIB) method offers simple control over initial leaflet compositions in model membranes, enabling an experimental path to filling gaps in our knowledge about the interplay between compositional lipid asymmetry, membrane properties, and the behaviors of membrane-active species. Yet, the stability of lipid leaflet asymmetry in DIBs has received very little attention, particularly in the presence of peptides and ion channels that are often studied in DIBs. Herein, we demonstrate for the first time parallel, capacitance-based measurements of intramembrane potential with arrays of asymmetric DIBs assembled in a microfluidic device to characterize the stability of leaflet asymmetry over many hours in the presence and absence of membrane-active peptides. DIBs assembled from opposing monolayers of the ester (DPhPC) and ether (DOPhPC) forms of diphytanoyl-phosphatidylcholine yielded asymmetric bilayers with leaflet compositions that were stable for at least 18 h as indicated by a stable |137 mV| intramembrane potential. In contrast, the addition of surface-bound alamethicin peptides caused a gradual, concentration-dependent decrease in the magnitude of the dipole potential difference. Intermittent current-voltage measurements revealed that alamethicin in asymmetric DIBs also shifts the threshold voltage required to drive peptide insertion and ion channel formation. These outcomes take place over the course of 1 to 5 h after membrane formation, and suggest that alamethicin peptides promote lipid flip-flop, even in the un-inserted, surface-bound state, by disordering lipids in the monolayer to which they bind. Moreover, this methodology establishes the use of parallel electrophysiology for efficiently studying membrane asymmetry in arrays of DIBs.

An artificial electrical synapse that mimics the structure, transport properties, and plasticity of biological electrical synapses exhibits voltage-controlled memristance by exploiting reconfigurable membrane geometry.

Physical systems exhibiting neuromechanical functions promise to enable structures with directly encoded autonomy and intelligence. A neuromorphic metamaterials class embodying bioinspired mechanosensing, memory, and learning functionalities obtained by leveraging mechanical instabilities integrated with memristive materials is reported. The prototype system comprises a multistable metamaterial whose bistable dome‐shaped units collectively filter, amplify, and transduce external mechanical inputs over large areas into simple electrical signals using embedded piezoresistive sensors. Dome deformations in nonvolatile memristors triggered by the transduced signals, providing a means to store loading events in measurable material states are recorded. Sequentially applied mechanical inputs result in accumulated memristance changes that allow us to physically encode a Hopfield network into the neuromorphic metamaterials. This physical network learns the history of spatially distributed input patterns. Crucially, the neuromorphic metamaterials can retrieve the learned patterns from the memristors’ final accumulated state. Therefore, the system exhibits the ability to learn without supervised training and retain spatially distributed inputs with minimal external overhead. The system's embodied mechanosensing, memory, and learning capabilities establish an avenue for synthetic neuromorphic metamaterials that learn via tactile interactions. This capability suggests new types of large‐area smart surfaces for robotics, autonomous systems, wearables, and morphing structures subjected to spatiotemporal mechanical loading.

No abstract

This work computationally investigates local flow behavior in tree-like flow networks of varying scale, bifurcation angle, and inlet Reynolds number. The performance of the tree-like flow networks were evaluated based on pressure drop and wall temperature distributions. Microscale, mesoscale, and macroscale tree-like flow networks, composed of a range of symmetric bifurcation angles (15, 30, 45, 60, 75, and 90°) and subject to a range of inlet Reynolds numbers (1000, 2000, 4000, 10000, and 20000) were evaluated. Local pressure recoveries were evident at bifurcations, regardless of scale and bifurcation angle which may result in a lower total pressure drop when compared with traditional parallel channel networks. Similarly, wall temperature spikes were also present immediately following bifurcations due to flow separation and recirculation. The magnitude of the wall temperature increases at bifurcations was dependent upon both bifurcation angle and scale. When compared with mesoscale and macroscale flow networks, microscale flow networks resulted in the largest local pressure recoveries and the smallest temperature jumps at bifurcations. Thus, while biologically-inspired flow networks offer the same advantages at all scales, the greatest performance increases are achieved at microscale.