No abstract

Temperature sensing is an important parameter needed to be measured by the eSkin during the physical interaction of robots with real-world objects. Yet, most of the work on sensors in eSkin has focused on pressure sensing. Here we present a skin conformable printed temperature sensor with poly(3,4-ethylenedioxythiophene): poly (styr-enesulfonate) (PEDOT:PSS)-graphene oxide (GO) as a temperature sensitive layer and silver (Ag) as contact electrodes. The demonstration of PEDOT:PSS/GO as a highly temperature sensitive layer is the distinct feature of the work. The response of presented sensor observed over ∼25 • C (room temperature (RT)) to 100 • C, by measuring the variation in resistance across the GO/PEDOT:PSS layer showed ∼80% decrease in resistance. The sensitivity of the sensor was found to be 1.09% per • C. The sensor's response was also observed under static and dynamic bending (for 1000 cycles) conditions. The stable and repeatable response of sensor, in both cases, signifies strong adhesion of the layers with negligible delamination or debonding. In comparison to the commercial thermistor, the printed GO/PEDOT:PSS sensor is faster (∼73% superior) with response and recovery times of 18 s and 32 s respectively. Finally, the sensor was attached to a robotic hand to allow the robot to act by using temperature feedback.

This paper presents poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate polymer microchannel (diameter ≈175 µm) based stretchable strain sensor developed inside polydimethylsiloxane substrate. The microchannel diameter changes when subjected to various strains, leading to change in the resistance of strain sensor. The sensor exhibits about three order (ΔR/R0 ≈ 1200) increase in the resistance (R) for 10% applied strain (ΔL/L, L = length of the sensor). This leads to a gauge factor (GF Δ (ΔR/R0)/(ΔL/L) of ≈12 000, which is about ≈400 times higher than most of the reported polymer‐based strain sensors. The sensor is evaluated up to a maximum strain of 30%, which is the standard strain limit associated with human body parts such as fingers and wrists. The sensor exhibits a considerably good average degree of hysteresis (<9%). Further, the sensor is also studied for bending and twisting experiments. A response of (ΔR/R0 ≈ 250) and (ΔR/R0 ≈ 300) is recorded for 90° bending and 150° twisting, respectively. The sensor shows an electrical resolution of ≈150% per degree of free bending and ≈12k% per percentage of stretching. Finally, the potential application of presented sensor in robotics and wearable systems is demonstrated by using sensor feedback from human hand to remotely control the robotic hand movements.

Inspired by biology, significant advances have been made in the field of electronic skin (eSkin) or tactile skin. Many of these advances have come through mimicking the morphology of human skin and by distributing few touch sensors in an area. However, the complexity of human skin goes beyond mimicking few morphological features or using few sensors. For example, embedded computing (e.g. processing of tactile data at the point of contact) is centric to the human skin as some neuroscience studies show. Likewise, distributed cell or molecular energy is a key feature of human skin. The eSkin with such features, along with distributed and embedded sensors/electronics on soft substrates, is an interesting topic to explore. These features also make eSkin significantly different from conventional computing. For example, unlike conventional centralized computing enabled by miniaturized chips, the eSkin could be seen as a flexible and wearable large area computer with distributed sensors and harmonized energy. This paper discusses these advanced features in eSkin, particularly the distributed sensing harmoniously integrated with energy harvesters, storage devices and distributed computing to read and locally process the tactile sensory data. Rapid advances in neuromorphic hardware, flexible energy generation, energy-conscious electronics, flexible and printed electronics are also discussed. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

No abstract