ABSTRACT We present a portable liquid crystal (LC)‐based optoelectronic hybrid neural network system for high‐precision formaldehyde sensing. Central to the platform is an electrically tunable LC‐on‐Chip module, optimized via a progressive inverse design strategy that co‐optimizes optical and neural network parameters. We introduce LC‐chromatic aberration coding, a novel optical computing mechanism that efficiently captures rich spatial‐spectral features, which are subsequently decoded by the integrated neural network to quantify formaldehyde with high selectivity. The compact device achieves approximately triple that of commercial kits and matches laboratory‐grade spectrophotometers, despite occupying less than 1% of their volume. It further exhibits robust interference rejection against acetaldehyde and other VOCs in complex mixtures. By synergizing optical coding with co‐optimized hardware and algorithm, this work bridges the gap between portability and lab‐scale performance, enabling scalable, intelligent indoor air quality monitoring.
Li et al. (Sat,) studied this question.