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Incorporation of VOC-Selective Peptides in Gas Sensing Materials

Enhancing the selectivity of gas sensing materials towards specific volatile organic compounds (VOCs) is challenging due to the chemical simplicity of VOCs as well as the difficulty in interfacing VOC selective biological elements with electronic components used in the transduction process. We aimed to tune the selectivity of gas sensing materials through the incorporation of VOC-selective peptides into gel-like gas sensing materials. Specifically, a peptide (P1) known to discriminate single carbon deviations among benzene and derivatives, along with two modified versions (P2 and P3), were integrated with gel compositions containing gelatin, ionic liquid and without or with a liquid crystal component (ionogels and hybrid gels respectively). These formulations change their electrical or optical properties upon VOC exposure, and were tested as sensors in an in-house developed e-nose. Their ability to distinct and identify VOCs was evaluated via a supervised machine learning classifier. Enhanced discrimination of benzene and hexane was detected for the P1-based hybrid gel. Additionally, complementarity of the electrical and optical sensors was observed considering that a combination of both their accuracy predictions yielded the best classification results for the tested VOCs. This indicates that a combinatorial array in a dual-mode e-nose could provide optimal performance and enhanced selectivity.