Advancing intelligent sensors with antennal molecular assemblies and supramolecular materials

Advancing intelligent sensors through the integration of antennal molecular assemblies and supramolecular materials represent a significant leap in precision, selectivity, and adaptability across diverse applications. These cutting-edge sensors are inspired by the extraordinary sensitivity of natural biological systems, such as insect antennae, enabling heightened molecular recognition even in complex environments. Recent developments in nanoarchitectonics and supramolecular chemistry offer transformative approaches to sensor design, facilitating the precise assembly of nanoscale structures and creating highly responsive materials. This article delves into the interdisciplinary application of these technologies, covering theoretical foundations and practical implementations across fields including environmental monitoring, biomedical diagnostics, and energy systems. By comparing various sensor architectures, the article reveals the advantages of using molecular assemblies, nanoscale surface engineering, and supramolecular interactions to address existing limitations in traditional sensors. Emphasis is placed on future research directions and innovations, such as adaptive sensing and AI integration, which hold the potential to revolutionize real-time monitoring and detection across industries. This synthesis aims to bridge current knowledge gaps, showcasing the novel possibilities in sensor technology through biomimetic inspiration and advanced material science. The sensing behaviors discussed in this work are strongly influenced by intrinsic nonlinear interactions at the molecular and nanoscale levels. These nonlinearities dictate signal amplification, stochastic response behavior, and dynamic adaptability across diverse environments. Recent advances in nonlinear physics further reinforce how nonlinear coupling, bifurcation behavior, and multistability can significantly enhance the performance of intelligent sensor materials. Graphical abstract