A Systems-Level Framework Integrating Geometry-Controlled Plasmonics, AI-Driven Molecular Kinetics, and Organoid Validation for Next-Generation Biosensing

Abstract

Plasmonic nanosensors - spanning nanopores, nanoantennas, and metasurfaces - achieve extreme electromagnetic (EM) field confinement that amplifies molecular interaction signals by orders of magnitude. Yet the full diagnostic potential of these platforms remains unrealised because the non-linear coupling between geometry, near-field physics, stochastic binding kinetics, and signal transduction is poorly characterised in biologically relevant systems. Here we propose the Plasmonic-AI-Organoid (PAO) framework: a modular, systems-level architecture linking (i) geometry-controlled plasmonic structures parameterised by Gaussian-process (GP) surrogate electromagnetic models; (ii) Bayesian inference of molecular kinetic parameters - association and dissociation rate constants, analyte concentrations - from noisy time-series sensor data using Metropolis-Hastings Markov chain Monte Carlo (MCMC); and (iii) human induced pluripotent stem cell (iPSC)-derived and patient-derived organoids as meso-scale biological validators. We formalise a forward model mapping geometry to EM field maps to reaction propensities to observable localized surface plasmon resonance (LSPR) signals, and an inverse model recovering posterior distributions over kinetic and geometric latent variables. A closed design loop employing active learning with expected-improvement acquisition functions iteratively proposes optimal geometries and assay conditions. Multi-objective Pareto optimisation balances analytical sensitivity, specificity, and manufacturability. Computational benchmarks demonstrate that active learning reduces the number of FDTD simulations required to identify near-optimal geometries by 3.2-fold compared with random search, while MCMC inference recovers kinetic parameters with sub-log-unit accuracy from synthetic time-series. The PAO framework provides a conceptual and fully reproducible computational roadmap for next-generation, AI-augmented plasmonic biosensing.