Nanostructured interfaces integrated with unsupervised intelligence to mitigate global polycrisis complexities

Abstract

Nanostructured sensors are increasingly deployed to mitigate the complexities of the global polycrisis, including climate instability, antimicrobial resistance, pandemics, and emerging technological disruptions. While advanced nano-interfaces (such as MXenes, quantum dots, and MOFs) possess the requisite sensitivity, their efficiency is hindered by large-scale, high-dimensional, and stochastic physicochemical responses. This review articulates a necessary paradigm shift toward unsupervised machine Intelligence as the primary interface between nanostructured sensor hardware, raw data manifolds, and system-level interpretation. It critically examines the foundational methodologies, including clustering for discrete-state identification, Principal Component Analysis for decoupling cross-sensitive material kinetics, manifold learning for nonlinear structure visualization, Independent Component Analysis for blind source separation, and autoencoders for nonlinear denoising and anomaly detection. These approaches extract latent dynamical structures directly from raw nanosensor measurements without dependence on extensive labelled datasets, effectively handling drift, hysteresis, and environmental noise. Moving beyond purely statistical optimisation, it analyse hybrid architectures that embed conservation principles, symmetry conditions, and topological regularities directly into learning algorithms, ensuring outputs follow the system's physical constraints. Finally, to address scalability challenges, including edge-native computing and privacy-preserving federated learning, it argues that converging advanced sensing nano-interfaces with constraint-regulated unsupervised intelligence is critical for developing self-calibrating material-sensor intelligence ecosystems to navigate polycrisis.