Light-Activated Porphyrinoid-Capped Nanoparticles for Gas Sensing

The coupling of semiconductors with organic molecules results in a class of sensors whose chemoresistive properties are dictated by the nature of dyes. Organic molecules generally reduce conductivity, but in the case of optically active dyes, such as porphyrinoids, the conductivity is restored by illumination with visible light. In this iT paper, we investigated the gas sensing properties of ZnO nanoparticles coated with porphyrins and corroles. Under light illumination, the resistance of these materials increases with the adsorption of volatile compounds but decreases when these are strong electron donors. The behavior of these sensors can be explicated on the basis of the structural difference between free-base porphyrin and corrole, the influence of coordinated metal, and the corresponding electronic structures. These sensors are promising electronic noses that combine the selectivity to strong electron donors with the broad selectivity toward the other classes of chemicals. An efficient representation of the data of this peculiar array can be obtained by replacing the Euclidean distance with the angular distance. To this end, a recently introduced spherical principal component analysis algorithm is applied for the first time to gas sensor array data. Results show that a minimal gas sensor array (four elements) can produce a sort of chemotopic map, which enables us to cluster a very large class of pure chemical vapors. Furthermore, this map provides information about the composition of complex odor matrices, such as the headspaces of beef meat and their evolution over the time.

Automated summary

The paper investigated the gas sensing properties of ZnO nanoparticles coated with porphyrins and corroles under light illumination. The behavior of these sensors in response to volatile compounds and strong electron donors was studied, providing insights into their selectivity and sensitivity. Additionally, a unique gas sensor array data representation using spherical principal component analysis algorithm was introduced to generate chemotopic maps for clustering pure chemical vapors.