Flexible Multimodal Magnetoresistive Sensors Based on Alginate/Poly(vinyl alcohol) Foam with Stimulus Discriminability for Soft Electronics Using Machine Learning.

Flexible foam-based sensors have attracted substantial interest due to their high specific surface area, light weight, superior deformability, and ease of manufacture. However, it is still a challenge to integrate multimodal stimuli-responsiveness, high sensitivity, reliable stability, and good biocompatibility into a single foam sensor. To achieve this, a magnetoresistive foam sensor was fabricated by an in situ freezing-polymerization strategy based on the interpenetrating networks of sodium alginate, poly(vinyl alcohol) in conjunction with glycerol, and physical reinforcement of core-shell bidisperse magnetic particles. The assembled sensor exhibited preferable magnetic/strain-sensing capability (GF ≈ 0.41 T-1 for magnetic field, 4.305 for tension, -0.735 for bending, and -1.345 for pressing), quick response time, and reliable durability up to 6000 cycles under external stimuli. Importantly, a machine learning algorithm was developed to identify the encryption information, enabling high recognition accuracies of 99.22% and 99.34%. Moreover, they could be employed as health systems to detect human physiological motion and integrated as smart sensor arrays to perceive external pressure/magnetic field distributions. This work provides a simple and ecofriendly strategy to fabricate biocompatible foam-based multimodal sensors with potential applications in next-generation soft electronics.