An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices distributed over large areas and capable of delivering synaptic behavior with long- and short-term memory. Here, we present an approach to realize synaptic transistors (12-by-14 array) using ZnO nanowires printed on flexible substrate with 100% yield and high uniformity. The presented devices show synaptic behavior under pulse stimuli, exhibiting excitatory (inhibitory) post-synaptic current, spiking rate-dependent plasticity, and short-term to long-term memory transition. The as-realized transistors demonstrate excellent bio-like synaptic behavior and show great potential for in-hardware learning. This is demonstrated through a prototype computational e-skin, comprising event-driven sensors, synaptic transistors, and spiking neurons that bestow biological skin-like haptic sensations to a robotic hand. With associative learning, the presented computational e-skin could gradually acquire a human body–like pain reflex. The learnt behavior could be strengthened through practice. Such a peripheral nervous system–like localized learning could substantially reduce the data latency and decrease the cognitive load on the robotic platform.

The development of Printed Circuit boards (PCBs) has so far followed a traditional linear economy value chain, leading to high volumes of waste production and loss of value at the end-of-life. Consequentially, the electronics industry requires a transition to more sustainable practices. This review article presents an overview of the potential solutions and new opportunities that may arise from the greater use of emerging sustainable materials and resource-efficient manufacturing. A brief contextual summary about how the international management of Waste PCBs (WPCBs) and legalization have evolved over the past 20 years is presented along with a review of the existing materials used in PCBs. The environmental and human health assessment of these materials relative to their usage with PCBs is also explained. This enables the identification of which eco-friendly materials and new technologies will be needed to improve the sustainability of the industry. Following this, a comprehensive analysis of existing WPCB processing is presented. Finally, a detailed review of potential solutions is provided, which has been partitioned by the use of emerging sustainable materials and resource-efficient manufacturing. It is hoped that this discussion will transform existing manufacturing facilities and inform policies, which currently focus on waste management towards waste reduction and zero waste.

This work presents the design and implementation of a porous polydimethylsiloxane (PDMS)-based wide-range flexible pressure sensor for autonomous underwater vehicles. The capacitive sensor, with porous PDMS as dielectric, is encapsulated in bulk PDMS polymer. The fabricated sensor was evaluated over a wide pressure range (0-230 kPa), which is similar to pressures experienced up to approx. 24m below the sea level. The sensors showed linear response when tested in air and near-linear response (98%) when submerged in water. The sensor showed much higher sensitivity (0.375 kPa -1 ) in water than in the air environment. However, the sensor exhibited the performance and sensitivity similar to the air condition (0.005 kPa -1 ) when the readout electronics (encapsulated inside a watertight enclosure) was also submerged inside the water along with the sensor. The fabricated sensor also exhibited fast response and recovery time (190 ms), as well as excellent repeatability and stability (no drift) over tested range of 50 loading and unloading cycles. These results demonstrate the suitability of presented sensors for potential use in applications requiring a wide range of pressure, particularly the underwater robotics where real-time pressure monitoring is critical for autonomous operation.

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