Demonstration of an energy-efficient Ising solver composed of Ovonic threshold switch (OTS)-based nano-oscillators (OTSNOs).

As there is an increasing need for an efficient solver of combinatorial optimization problems, much interest is paid to the Ising machine, which is a novel physics-driven computing system composed of coupled oscillators mimicking the dynamics of the system of coupled electronic spins. In this work, we propose an energy-efficient nano-oscillator, called OTSNO, which is composed of an Ovonic Threshold Switch (OTS) and an electrical resistor. We demonstrate that the OTSNO shows the synchronization behavior, an essential property for the realization of an Ising machine. Furthermore, we have discovered that the capacitive coupling is advantageous over the resistive coupling for the hardware implementation of an Ising solver by providing a larger margin of the variations of components. Finally, we implement an Ising machine composed of capacitively-coupled OTSNOs to demonstrate that the solution to a 14-node MaxCut problem can be obtained in 40 µs while consuming no more than 2.3 µJ of energy. Compared to a previous hardware implementation of the phase-transition nano-oscillator (PTNO)-based Ising machine, the OTSNO-based Ising machine in this work shows the performance of the increased speed by more than one order while consuming less energy by about an order.

Implanted Carbon Nanotubes Harvest Electrical Energy from Heartbeat for Medical Implants.

Reliability of power supply for current implantable electronic devices is a critical issue for longevity and for reducing the risk of device failure. Energy harvesting is an emerging technology, representing a strategy for establishing autonomous power supply by utilizing biomechanical movements in human body. Here, a novel "Twistron energy cell harvester" (TECH), consisting of coiled carbon nanotube yarn that converts mechanical energy of the beating heart into electrical energy, is presented. The performance of TECH is evaluated in an in vitro artificial heartbeat system which simulates the deformation pattern of the cardiac surface, reaching a maximum peak power of 1.42 W kg-1 and average power of 0.39 W kg-1 at 60 beats per minute. In vivo implantation of TECH onto the left ventricular surface in a porcine model continuously generates electrical energy from cardiac contraction. The generated electrical energy is used for direct pacing of the heart as documented by extensive electrophysiology mapping. Implanted modified carbon nanotubes are applicable as a source for harvesting biomechanical energy from cardiac motion for power supply or cardiac pacing.

Structurally Aligned Multifunctional Neural Probe (SAMP) Using Forest-Drawn CNT Sheet onto Thermally Drawn Polymer Fiber for Long-Term In Vivo Operation.

Neural probe engineering is a dynamic field, driving innovation in neuroscience and addressing scientific and medical demands. Recent advancements involve integrating nanomaterials to improve performance, aiming for sustained in vivo functionality. However, challenges persist due to size, stiffness, complexity, and manufacturing intricacies. To address these issues, a neural interface utilizing freestanding CNT-sheets drawn from CNT-forests integrated onto thermally drawn functional polymer fibers is proposed. This approach yields a device with structural alignment, resulting in exceptional electrical, mechanical, and electrochemical properties while retaining biocompatibility for prolonged periods of implantation. This Structurally Aligned Multifunctional neural Probe (SAMP) employing forest-drawn CNT sheets demonstrates in vivo capabilities in neural recording, neurotransmitter detection, and brain/spinal cord circuit manipulation via optogenetics, maintaining functionality for over a year post-implantation. The straightforward fabrication method's versatility, coupled with the device's functional reliability, underscores the significance of this technique in the next-generation carbon-based implants. Moreover, the device's longevity and multifunctionality position it as a promising platform for long-term neuroscience research.