Initiatorless Solar Photopolymerization of Versatile and Sustainable Eutectogels as Multi‐Response and Self‐Powered Sensors for Human–Computer Interface

Abstract

Eutectogels are emerging as an appealing soft conductor for self‐powered sensing and the next generation of flexible human–computer interactive devices owing to their inherent mechanical elasticity and high ionic conductivity. However, it still remains a challenge to simultaneously achieve multi‐functional and multi‐response integrations through a facile and sustainable approach. Herein, a self‐healing, environment tolerant, intrinsically conductive, and recyclable eutectogel with multiple responses is developed via one‐step solar‐initiated polymerization of deep eutectic solvents (DESs) and ionic liquids (ILs). Abundant hydrogen bonds and ion‐dipole interactions impart eutectogels with high mechanical strength (8.8 MPa), ultra‐stretchability (>1100%), strong self‐adhesion (≈12 MPa), recyclability, and autonomously self‐healing ability. Furthermore, the intrinsically conductive eutectogels with appealing versatile sensations on strain, temperature, and humidity can serve as wearable sensors for wireless motion recognition and human–computer interaction control. More importantly, the eutectogel‐assembled single‐electrode triboelectric nanogenerator (TENG) exhibits extreme environment‐tolerant and fast self‐healable properties that contribute to maintaining excellent and stable electrical outputs in a wide work temperature range (approximately −40–60 °C), which appear to be promising in self‐powered flexible electronics with high environmental adaptability.

Lithium-Ion-Doped Eutectogel for Surface-Capacitive Sensing Touch Panel

“…As shown in Figure a, as the same phase AC voltage (17 kHz, ±0.6 V) is loaded to both ends of the eutectogel, a uniform electrostatic field will be generated across the strip. After being touched by fingertip, the circuit was grounded and generated a potential gradient distribution between electrodes and the touch point. − Once the finger touches the boundary point, the voltage of the touch point will decline. The touch position (x) is normalized relative to the left electrode and can be described as (1 – x) ∝ U1 or x ∝ U2 as shown in Figure a and Figure S11.…”

Holistic and localized preparation methods for triboelectric sensors: principles, applications and perspectives

“…In addition to the enhanced voltage output, other properties of the tribolayer can be imparted by specific group chemistry in the solution method [36,56,83], such as the ability to perform reversible/irreversible self-repair through physical and chemical cross-linking in hydrogels; the inclusion of conductive substances such as CNTs and silver nanoparticles (Ag NPs) in the hydrogel improves the electrical conductivity and bacteriostatic properties, and then an anti-freezing nanocomposites hydrogel (PAGCA) is formed by adding a cryoprotectant to the CNT@TA@Ag hybrids and gelatin by in situ polymerization (figure 5(c)) [84]. PAGCA is embedded in PDMS to obtain a single-electrode triboelectric sensor so that this sensor can be used at −30 • C (figure 5(d)) and has a selfhealing function (figure 5(e)).…”

“…However, the performance and functionality of sensors largely depend on their preparation methods. In this section, various preparation methods for triboelectric sensors are introduced, including MEMS technology [33], 3D printing [34], textile technology [35], materials synthesis [36] and template assisted methods [32] (figure 1). Moreover, based on the basic principles of preparation, the advantages of the device and its application scenarios are also described.…”

Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence

“…TENG-based sensors can capture the mechanical energy generated by user interactions, such as tapping, swiping, or scrolling on touchscreens. Machine learning algorithms can analyze the TENG-generated data to recognize specific gestures and optimize power consumption. − AI integration can enhance security systems through gesture-based authentication by capturing unique hand movements or gestures of individuals, which can be used as biometric identifiers. Machine learning algorithms can learn and recognize these gestures, enabling secure and convenient authentication methods.…”