Flexible bioelectronics encompass a new generation of sensing devices, in which controlled interactions with tissue enhance understanding of biological processes in vivo. However, the fabrication of such thin film electronics with photolithographic processes remains a challenge for many biocompatible polymers. Recently, two shape memory polymer (SMP) systems, based on acrylate and thiol-ene/acrylate networks, were designed as substrates for softening neural interfaces with glass transitions above body temperature (37 °C) such that the materials are stiff for insertion into soft tissue and soften through low moisture absorption in physiological conditions. These two substrates, acrylate and thiol-ene/acrylate SMPs, are compared to polyethylene naphthalate, polycarbonate, polyimide, and polydimethylsiloxane, which have been widely used in flexible electronics research and industry. These six substrates are compared via dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), and swelling studies. The integrity of gold and chromium/gold thin films on SMP substrates are evaluated with optical profilometry and electrical measurements as a function of processing temperature above, below and through the glass transition temperature. The effects of crosslink density, adhesion and cure stress are shown to play a critical role in the stability of these thin film materials, and a guide for the future design of responsive polymeric materials suitable for neural interfaces is proposed. Finally, neural interfaces fabricated on thiol-ene/acrylate substrates demonstrate long-term fidelity through both in vitro impedance spectroscopy and the recording of driven local field potentials for 8 weeks in the auditory cortex of laboratory rats.

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Scaling up robot swarms to collectives of hundreds or even thousands without sacrificing sensing, processing, and locomotion capabilities is a challenging problem. Low-cost robots are potentially scalable, but the majority of existing systems have limited capabilities, and these limitations substantially constrain the type of experiments that could be performed by robotics researchers. Instead of adding functionality by adding more components and therefore increasing the cost, we demonstrate how low-cost hardware can be used beyond its standard functionality. We systematically review 15 swarm robotic systems and analyse their sensing capabilities by applying a general sensor model from the sensing and measurement community. This work is based on the HoverBot system. A HoverBot is a levitating circuit board that manoeuvres by pulling itself towards magnetic anchors that are embedded into the robot arena. We show that HoverBot's magnetic field readouts from its Hall-effect sensor can be associated to successful movement, robot rotation and collision measurands. We build a time series classifier based on these magnetic field readouts. We modify and apply signal processing techniques to enable the online classification of the time-variant magnetic field measurements on HoverBot's low-cost microcontroller. We enabled HoverBot with successful movement, rotation, and collision sensing capabilities by utilising its single Hall-effect sensor. We discuss how our classification method could be applied to other sensors to increase a robot's functionality while retaining its cost.

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