Flexible pressure sensors face an irreconcilable trade-off among linearity, sensitivity, and signal stability due to viscoelastic creep. Inspired by the gradient modulus characteristics of human skin, this study employs electrospinning to construct a heterogeneous structure composed of a high-modulus nanofiber network embedded in a low-modulus ionic gel. This structure mimics epidermal rigidity (high-modulus nanofiber layer), dermal viscoelasticity (fiber-gel hybrid), and hypodermal compliance (soft ionic matrix) to synergistically redistribute stress and suppress ion migration. The sensor achieves breakthrough performance: a wide linear range (1 MPa) with near-perfect linearity (R 2 = 0.999) and ultrahigh sensitivity (81.3 kPa -1 ), yielding a record linear sensing factor (LSF, 81,300). Simultaneously, it exhibits ultralow creep (1.76% signal drift under sustained loading)—96.8% lower than non-structured iongels—enabled by nanofiber-restricted ion pathways. Theoretical modeling reveals a dynamic compensation mechanism where pressure-induced changes in dielectric properties, contact area expansion, and electric double-layer thinning interact linearly. Laboratory validation demonstrates high-fidelity plantar pressure monitoring during gait cycles and machine learning-based prediction of vertical ground reaction forces with exceptional accuracy (R 2 > 0.95). This work establishes a new design paradigm for high-precision flexible sensing by fundamentally resolving long-standing material limitations.
Due to their superior flexibility and stability, flexible pressure sensors are becoming more and more popular for wearable electronics. It is still quite difficult to find high-performance pressure sensors with large detection ranges and great sensitivity. In this paper, an EC/PDMS porous blend with ultraflexible, multilevel porosity and robustness was successfully prepared using ethyl cellulose (EC)-reinforced porous polydimethylsiloxane (PDMS). The porous blends exhibit excellent sensing performance, including an ultrawide linear sensing range (0–251.4 kPa, R2=0.995), high sensitivity (8.94 kPa–1), quick response time, and excellent long-term stability (3500 cycles). It shows good detection capabilities for various deformations in human motion, including compression, deformation, and bending. Furthermore, we assembled the EC/PDMS porous pressure sensor into a portable integrated glove for real-time interaction with mobile devices. Using deep learning algorithms, the glove achieves fast gesture translation and mobile, portable real-time output. It achieves 99.7% accuracy for translating static gestures representing 26 English letters, showcasing its ability to translate daily conversations and complex sentences in real time. Additionally, it can recognize continuous dynamic gestures representing some interesting phrases, demonstrating its great potential as a high-performance wearable pressure sensor for human–computer interaction.
危险品化学品经营许可证（带存储）