Flow‐Adaptive Gas Sensing Enabled Using a Uniform Au Nanosheet Sensor Array and a Neural Network Inference

ABSTRACT Gas sensor responses are considerably affected by gas flow rates, thereby inhibiting the accurate detection of target gas concentrations in variable‐flow applications such as breath analyzers. To address this challenge, a flow‐adaptive sensing system using a spatially distributed array of uniform Au nanosheet sensors was developed. The array layout was designed via computational fluid dynamics (CFD) simulations to generate spatiotemporal signal patterns from the array output upon gas exposure. These patterns were used to train a deep neural network, which accurately estimated gas flow rates and concentrations under various conditions. This system does not require active flow control during operation. The proposed approach employs sensor array design, CFD‐guided layout, and artificial‐intelligence‐based inference to overcome the fundamental limitations of gas sensing. The sensing system can operate reliably under various flow conditions, highlighting the conceptual potential of the array‐based sensing method for use in portable and pumpless gas‐sensing platforms.