Nanofibrous Conductive Sensor for Limonene: One-Step Synthesis via Electrospinning and Molecular Imprinting.

Detecting volatile organic compounds (VOCs) emitted from different plant species and their organs can provide valuable information about plant health and environmental factors that affect them. For example, limonene emission can be a biomarker to monitor plant health and detect stress. Traditional methods for VOC detection encounter challenges, prompting the proposal of novel approaches. In this study, we proposed integrating electrospinning, molecular imprinting, and conductive nanofibers to fabricate limonene sensors. In detail, polyvinylpyrrolidone (PVP) and polyacrylic acid (PAA) served here as fiber and cavity formers, respectively, with multiwalled carbon nanotubes (MWCNT) enhancing conductivity. We developed one-step monolithic molecularly imprinted fibers, where S(-)-limonene was the target molecule, using an electrospinning technique. The functional cavities were fixed using the UV curing method, followed by a target molecule washing. This procedure enabled the creation of recognition sites for limonene within the nanofiber matrix, enhancing sensor performance and streamlining manufacturing. Humidity was crucial for sensor working, with optimal conditions at about 50% RH. The sensors rapidly responded to S(-)-limonene, reaching a plateau within 200 s. Enhancing fiber density improved sensor performance, resulting in a lower limit of detection (LOD) of 137 ppb. However, excessive fiber density decreased accessibility to active sites, thus reducing sensitivity. Remarkably, the thinnest mat on the fibrous sensors created provided the highest selectivity to limonene (Selectivity Index: 72%) compared with other VOCs, such as EtOH (used as a solvent in nanofiber development), aromatic compounds (toluene), and two other monoterpenes (α-pinene and linalool) with similar structures. These findings underscored the potential of the proposed integrated approach for selective VOC detection in applications such as precision agriculture and environmental monitoring.