Wirelessly powered implants are increasingly being developed to interface with neurons in the brain. They often rely on microelectrode arrays which are limited by their scalability and long-term stability because of their physical size and rigid configuration. Yet some clinical and research applications prioritize a distributed neural interface over one that offers high resolution. One solution to make large scale, fully specifiable, electrical stimulation/recording possible, is to disconnect the electrodes from the base, so that they can be arbitrarily placed, using a syringe, freely in the nervous system. In this work, a wirelessly powered stimulating implant is miniaturized using a novel electrode integration technique, and its implanted depth maximized using innovative optimization design methods for the transmitter and receiver coils. The stimulating device is implemented in a 130nm CMOS technology with the following characteristics: 300 μm × 300 μm × 80 μm size; optimized wireless powering; and integrated circuit, electrodes and coil. The wireless and stimulation capability of the implant is validated in a conductive medium and in-vivo. To the best of our knowledge, the fabricated free-floating miniaturized implant has the best depth-to-volume ratio making it an excellent tool for minimally-invasive scalable neural interface, and thus could eventually replace the rigid arrays that are currently the state-of-the-art in brain set-ups.

Material properties gradually degrade under cyclic loading, leading to catastrophic failure. It results in large costs for inspection, maintenance, and downtime. Besides, materials require combinations of performance such as load bearing and energy dissipation. However, improving one performance of a material often sacrifices another performance, making it difficult to create materials with optimal performance profiles. Here we report a liquid-infused porous piezoelectric scaffold (LIPPS) that simultaneously enhances its load-bearing and energy dissipation capability under cyclic loading. For example, after 12 million loading cycles, LIPPS increases its modulus by 3600% and hysteresis by 3000%. From a CT study, this behavior is attributed to the self-recoverable mineralization under mechanical loading. Moreover, LIPPS shows a reprogrammable stiffness distribution based on the loading distribution, which enables the material to generate multiple shapes by self-folding. Our findings can contribute toward unprecedented opportunities in soft robotics, vehicles, infrastructure, and tissue engineering and contribute to the new paradigm of material selection with improved resilience and sustainability.

Carbon–polymer composite-based pressure sensors have many attractive features, including low cost, easy integration, and facile fabrication. Previous studies on carbon–polymer composite sensors focused on very high sensitivities for low pressure ranges (10 s of kPa), which saturate quickly at higher pressures and thus are ill-suited to measure the high pressure ranges found in various applications, including those in underwater (>1 atm, 101 kPa) and industrial environments. Current sensors designed for high pressure environments are often difficult to fabricate, expensive, and, similarly to their low-pressure counterparts, have a narrow sensing range. To address these issues, this work reports the design, synthesis, characterization, and analysis of high-pressure TPU-MWCNT based composite sensors, which detect pressures from 0.5 MPa (4.9 atm) to over 10 MPa (98.7 atm). In this study, the typical approach to improve sensitivity by increasing conductive additive concentration was found to decrease sensor performance at elevated pressures. It is shown that a better approach to elevated pressure sensitivity is to increase sensor response range by decreasing the MWCNT weight percentage, which improves sensing range and resolution. Such sensors can be useful for measuring high pressures in many industrial (e.g., manipulator feedback), automotive (e.g., damping elements, bushings), and underwater (e.g., depth sensors) applications.

Neuromorphic systems mimic the functionality and communication protocol of biological neurons with efforts to design the most size and power efficient computing systems. We demonstrate the capability of an in-house neural array of 4080 Mihalas-Niebur neurons designed in 0.5μm CMOS technology to perform various event-based image processing tasks including warping and filtering.