A dual-band radio-frequency biosensing platform with physics-informed deep learning for simultaneous sweat pH and lactate monitoring.

Continuous monitoring of sweat pH and lactate is of great value for physiological assessment, yet existing sensors face two critical bottlenecks: prolonged response times that hinder real-time tracking, and signal decoding relying on simple linear calibration that fails to handle nonlinearities and dynamic hysteresis. Here, we report a synergistic "RF-materials-AI" dual-band biosensing platform integrating (1) a stub-loaded dual-resonance microstrip antenna at 2.61 GHz and 3.98 GHz for frequency-domain discrimination of pH and lactate responses; (2) ternary nanocomposite interfaces-rGO/TiO₂/MWCNTs for pH and rGO/CuO/NiCo-MOF for lactate-for dielectric modulation and redox-mediated charge transfer, respectively; and (3) a Physics-BiLSTM-Attention network embedding first-order temporal difference priors and a dual-branch attention mechanism for robust multiparameter regression. The sensor achieves response times of 55 s (pH) and 135 s (lactate) with good stability and selectivity. On human sweat data from 10 subjects under LOSO-CV evaluation, the model delivers lactate prediction with R = 0.98, R²=0.96, MAE = 0.87 mM, RMSE = 1.12 mM, and pH prediction with R = 0.94, R²=0.88, MAE = 0.12 pH units, RMSE = 0.15 pH units-substantially outperforming static calibration (29.8% and 31.4% MAE reduction, respectively). The model achieves 94.0% accuracy in health status classification and 91.2% in exercise intensity classification, with well-calibrated uncertainty [Formula: see text]. Ablation studies confirm that removing any core component degrades MAE by 27-73%, underscoring the indispensable synergy of the three design dimensions. This work establishes a materials-hardware-algorithm co-design framework for multiparameter sweat analysis, offering a conceptually new pathway toward intelligent, passive, and continuous biosensing.