Biomimicking human tactile sensation using flexible tactile sensors

Abstract

The sense of touch is essential for human interaction with the environment, allowing us to perform tasks ranging from simple actions to intricate procedures. Human tactile perception, through mechanoreceptors in the skin, enables the detection of various stimuli, including pressure, texture, temperature, and vibration. This sensory capability is fundamental to tasks such as grasping objects, maintaining balance, and communicating emotions. Mimicking the complexity of human touch in artificial systems has been a longstanding challenge in robotics, prosthetics, and wearable technology. Achieving humanlike sensitivity in machines could transform industries, allowing robots to handle delicate objects, prosthetics to offer better tactile feedback for amputees, and wearables to enhance human-computer interaction. Advances in materials science and flexible electronics have opened new possibilities for developing tactile sensors that replicate the mechanical and sensory functions of human skin. Flexible sensors, which can conform to various surfaces and endure mechanical stress, are key to creating electronic skin (e-skin) capable of sensing pressure, strain, temperature, and other physical stimuli. This thesis explores the development of flexible tactile sensors that bio-mimic human tactile systems for use in robotics and prosthetics, while also leveraging computational simulations to improve design of these sensors. Towards this, in response to the need for reliable and sensitive strain sensors, this work initially presents stretchable strain sensors that operate over a wide range and exhibit excellent gauge factors. The sensors, composed of elastomer, conductive filler, and graphene–carbon paste (GCP), demonstrated high stretchability and sensitivity. Molecular dynamics simulations showed that adding GCP to the composite material enhances sensor response. A strain sensor with a high gauge factor of 1,834,140 was also developed. Additionally, a neuromorphic strain sensor system was developed using this piezoresistive sensor and a simulated neuron to mimic the behavior of mechanoreceptors. Applications of this neuromorphic strain sensing system in stretch and angle bending have been successfully demonstrated. Next, a hybrid sensor system was developed combining capacitive pressure sensors and triboelectric nanogenerators, mimicking both slowly and rapidly adapting mechanoreceptors. The hybrid system successfully detected static and dynamic stimuli and showed good applications for impact detection and slip monitoring. Later, a simulation-based study investigated PDMS based soft capacitive pressure sensors enhanced with ZnO nanowires. Results indicated a significant improvement in sensor sensitivity by ~3 times, especially with vertically aligned ZnO nanowires. This work demonstrates the potential of using simulation methods to optimize sensor design and enhance tactile sensor performance for future applications in robotics, prosthetics, and human-machine interfaces. Finally, atomistic simulations explored the behavior of water molecules trapped between a graphene and gold interface, which is a common issue in graphene-based devices due to the wet-transfer process. The effect of contaminants like water on the electronic properties of such devices is highly unexplored. The molecular dynamics simulations revealed that when the water film thickness is below 5 Å, it forms an ice-like structure, potentially causing strain in the graphene layer and affecting sensor performance. As the water thickness increases, the water transitions to a liquid state, reducing strain. This study provides critical insights into the challenges posed by trapped water at the grapheneAu interface, which can impact the performance of devices such as graphene-based fieldeffect transistors (GFETs). The research work presented in this thesis could provide a foundation for future work in the field of flexible and hybrid sensor systems, with potential applications in robotics, prosthetics, and human-machine interfaces.