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Magnetic nanomotors enable highly efficient capture and precise identification of emerging pollutants at part-per-trillion concentrations, offering a sustainable solution for water quality monitoring.
Researchers have created a novel memristive hardware framework that enables artificial intelligence systems to efficiently explain their decision-making process.
Researchers develop miniature motorized sensors employing genetically engineered viruses to selectively detect E. coli bacteria with speed and accuracy down to medically meaningful concentrations.
Researchers have developed 2D perovskite memristors that strengthen electrical resistance in response to light, mimicking learning in biological synapses. This could enable neuromorphic AI that learns autonomously through experience.
Researchers have created an artificial synapse device that displays plasticity and learning ability by merging a photoelectric perovskite material with an organic ferroelectric polymer. The advance offers a pathway to intelligent electronics and insights into the brain.
Researchers develop 3D-printed microbots that harness living bacteria as onboard engines to enable wireless control using only structured light patterns.
Phase change memory is an emerging technology with great potential for advancing analog in-memory computing, particularly in deep neural networks and neuromorphic computing.
Researchers introduce spin orbit torque (SOT) devices to experimentally realize in-memory analogue mathematical operations such as summation, subtraction and four-quadrant multiplication, to implement general-purpose applications such as image or signal processing for edge computing. In addition to nonvolatility and scalability, the CMOS-compatible SOT technique further possesses low energy consumption, high speed and endurance. Therefore, SOT devices offer an avenue for dense in-memory analogue computing paradigms.