Nanostructured Interfaces Integrated with Unsupervised Intelligence to Mitigate Global Polycrisis Complexities

This review argues that nanostructured sensors can play an important role in addressing the polycrisis, including climate instability, antimicrobial resistance, pandemics, and emerging technological disruptions, but that their effectiveness is limited by the complexity and scale of the data they generate. The authors propose unsupervised machine intelligence as a critical bridge between advanced sensing materials and system-level interpretation, highlighting techniques such as clustering, principal component analysis, manifold learning, independent component analysis, and autoencoders to extract meaningful patterns from raw sensor data without relying on labelled datasets. The review further emphasizes the value of integrating physical constraints, conservation principles, and topological regularities into learning algorithms, while exploring edge computing and federated learning as scalable solutions. It concludes that combining advanced nanosensors with constraint-aware unsupervised intelligence is essential for developing adaptive, self-calibrating sensing systems capable of supporting decision-making in an increasingly complex and interconnected polycrisis.