Volume 7 Issue 4, April 2024

Nanofluidic memristors that rely on mechanical deformations to modulate ionic conductance can be coupled to form logic circuits, opening a route to ionic machinery that could implement neural networks.

A new approach to measuring qubits offers an alternative path to scaling quantum computers.

A flexible, biodegradable and self-powered electronic bandage is designed to deliver dual-mode electrical stimulation, which can synergistically accelerate local intestinal wound healing. This approach also shows promise for reducing postoperative complications and could have broad potential for application in other tissues and organs.

Distributed sensing of a dynamic environment is typically characterized by the sparsity of events, such as neuronal firing in the brain. Using the brain as inspiration, an event-driven communication strategy is developed that enables the efficient transmission, accurate retrieval and interpretation of sparse events across a network of thousands of wireless microsensors.

Nanofluidic devices with a large entrance asymmetry can function as memristive switches—operating on the second timescale and with a conductance ratio of up to sixty—and can be assembled into basic logic circuits.

By integrating a metal‒oxide‒semiconductor capacitor into a two-terminal diode, a multifunctional single device can be created that operates as a tunable light-emitting diode with a built-in bias tee circuit and a detector with a reconfigurable optoelectronic logic function.

Bolometers can be used for qubit readout, offering a scalable, sensitive and high-speed alternative to current parametric-amplifier-based approaches.

A biodegradable electronic bandage that applies pulsed and d.c. electrostimulation can accelerate the healing of intestinal wounds in mice via transfection of cells and stimulation-induced secretion of healing factor from those cells.

A scalable communications protocol for transmitting spike-train data generated from multiple microchip sensors can be used with spiking neural network models for brain–machine interface and biosensing applications.