Ag/AgCl Nanoparticle Incorporation into Epipremnum aureum for Electrothermal Signal Amplification and Machine-Learning-Based Temperature Prediction

Recently, plant-based bioelectronic systems have been explored for environmental sensing applications. However, their intrinsically low electrical conductivity often limits signal sensitivity and measurement reliability. In this work, the electrothermal behavior of living Epipremnum aureum plants incorporating Ag/AgCl nanoparticles supported on nanocellulose was investigated. Electrical and thermal responses were simultaneously measured under controlled environmental conditions using external shunt resistances of 1, 10, 100, and 1000 Ω. Compared with the control without nanoparticle incorporation, the nanoparticle-incorporated plant exhibited stronger electrical responses and distinct electrothermal behavior over the studied temperature range. The measured signals showed nonlinear responses, temporal asymmetry, and resistance-dependent modulation, suggesting changes in charge transport within the plant tissues. Silver-enriched regions and the co-detection of chlorine within the nanoparticle-incorporated plant tissues were identified by environmental scanning electron microscopy and energy-dispersive X-ray spectroscopy. Five machine-learning regression models were trained to estimate temperature using the measured electrothermal voltage signals as predictors. The best-performing model, MLP FitRNet, achieved a mean absolute error of 0.598 °C, a root mean square error of 0.748 °C, and an R2 value of 0.974. These results demonstrate the potential of nanoparticle-incorporated biohybrid plant systems for electrothermal signal analysis and data-driven temperature estimation, while providing a foundation for future intelligent environmental monitoring applications.