Abstract
Abstract Metal-oxide-semiconductor (MOS)-based chemiresistive gas sensors are promising for real-time detection, but poor gas accessibility, stochastic charge transport, low selectivity, and high thermal burdens hinder their practical translation. Polymer templating provides a powerful architectural solution, converting self-assembled or lithographically defined organic templates into geometrically defined, non-stochastic 3D metal oxide networks. This review comprehensively summarizes recent advances in template-derived MOS nanostructures, focusing on polystyrene bead-templated hollow configurations and proximity-field nanopatterning (PnP)-derived scaffolds. We systematically analyze how precise geometric controls—such as template size, neck junctions, and laser-assisted post-modifications—optimize gas diffusion and electrical signal transduction. These structural parameters are systematically mapped onto receptor functions, transducer functions, and utility factors to clarify their roles in chemical and physical sensing mechanisms. Furthermore, we evaluate surface catalyst engineering using noble/transition metals and redox-active oxides alongside low-power paradigms, including light-activated and molecular-sieving frameworks. To bridge material design with intelligent data processing, we highlight multi-channel sensor arrays optimized for comprehensive feature extraction and advanced signal preprocessing. Crucially, we emphasize how template-defined structural uniformity minimizes channel-to-channel morphological variance, significantly improving data reliability for pattern-recognition methods such as principal component analysis (PCA), supervised machine learning models, convolutional neural networks (CNNs), and other advanced deep learning algorithms. Ultimately, this review establishes practical design guidelines for developing reproducible, energy-efficient, and intelligent chemical sensing platforms.