Breaking the Trilemma: Synergistic Strategies for High-Performance Flexible Strain Sensors

Abstract

As the core sensing unit of the next-generation electronic skin, personalized health monitoring and soft robots, the ultimate performance bottleneck of flexible strain sensors lies in the inherent contradiction among high sensitivity (GF), wide linear working range and low hysteresis, that is, the “trinity trade-off” problem. This paper, from the perspective of materials and chemical engineering, delves deeply into the physicochemical roots of this trade-off: the mutability of highly sensitive mechanisms (such as the breakage of conductive networks) and the energy dissipation caused by the intrinsic viscoelasticity and interfacial slippage of materials. To solve this difficult problem, we systematically reviewed cross-scale collaborative optimization strategies ranging from the molecular/nanoscale (dynamic bonding networks, multi-level composites), micro/macroscopic structures (bionic hierarchy, programmable wrinkles) to system intelligence (interface engineering, machine learning compensation). The article highlights cutting-edge and high-level research cases such as liquid metal-based adaptive interfaces, negative Poisson’s ratio fabric structures, ultra-low hysteresis high-toughness hydrogels, and artificial intelligence-driven reverse design. Finally, this paper looks forward to the future path of achieving performance boundary breakthroughs through bionic integrated design and the closed loop of material genomics and machine learning, providing a theoretical framework and technical guidelines for the design of high-fidelity flexible sensing systems for practical applications.