A Modular Nanoscale Platform for Continuous Health Monitoring: Detachable Sensors, Dry-Heat Sterilization, and Edge-Neuromorphic Signal Processing

Background: Current wearable health monitoring technologies exhibit limitations in biosecurity, biofidelity, and biointegration \citep{kim2019wearable, wang2024recent, rana2024nanosensors}. This work proposes a unified theoretical framework, supported by preliminary experimental validation, to address these challenges through self-sterilizing nanobiosensors.\\ Methods: A generalized bio-sensing inequality is derived from non-equilibrium thermodynamics, incorporating Arrhenius-Eyring kinetics for dry heat sterilization \citep{prigogine1967introduction, seifert2012stochastic}. Validation employed Bayesian hierarchical modeling, multihead attention algorithms, and stochastic PDE-constrained optimization on simulated data. Preliminary experiments encompassed Ag-Au nanoparticle synthesis, characterization via TEM/SEM/XPS, and sterilization tests per ISO standards \citep{rutala2019guideline}. Statistical analyses included ANOVA, regression modeling, power calculations, and effect size estimation.\\ Findings: Simulations indicate 99.97\% pathogen elimination at 160\si{\celsius} with maintained sensor integrity. Preliminary experiments verified core-shell structure (TEM), surface composition (XPS), and >99\% bacterial reduction (ANOVA F(1,12)=28.7, p