Sulfur-Oxygen Competitive Coordination Allosteric Hydrogel for Triboelectric Nanogenerator-Based Intelligent Tactile...

Flexible sensing technologies based on triboelectric nanogenerators (TENG) have shown promise for wearable electronics and human‑machine interaction. However, existing hydrogel‑based TENGs suffer from an inherent trade‑off between interfacial adhesion and electrical output, and their material recognition capability is compromised in real‑wearing environments due to signal variations. Here, the authors propose a smart glove system integrating a dynamic Ag⁺─S coordinated hydrogel with a machine‑learning‑based material recognition framework. Inspired by biological allostery, a "sulfur‑oxygen competitive coordination" switch is designed using Ag⁺ and a bifunctional small molecule, 5-carboxythiophene-2-boronic acid (CTBA). By adjusting the Ag⁺/CTBA ratio, the hydrogel reversibly switches between a soft, highly adhesive state and a stiff, mechanically robust state. This enables precise modulation of mechanical, adhesive, and triboelectric properties. A lightweight 1D convolutional neural network (WaveformCNN) is developed to classify contact waveforms. The trained model achieves 99.70% accuracy on laboratory‑collected data and 93.35% on real‑wear glove data, demonstrating cross‑scenario generalization. The hydrogel exhibits graded signal output under different finger bending angles and independent five‑finger recognition capability. This work establishes a strategy that bridges molecular coordination design and intelligent tactile perception, offering a new paradigm for self-powered electronic skins, autonomous material identification, and adaptive human-environment interaction in complex scenarios.