Palma, SICJ, Esteves C, Pádua AC, Alves CM, Santos GMC, Costa HMA, Dionisio M, Gamboa H, Gruber J, Roque ACA.
2019.
Enhanced gas sensing with soft functional materials, May 2019. ISOEN 2019 - 18th International Symposium on Olfaction and Electronic Nose, Proceedings. , Fukuoka, Japan: Institute of Electrical and Electronics Engineers Inc.
AbstractThe materials described in this work result from the selfassembly of liquid crystals and ionic liquids into droplets,
stabilized within a biopolymeric matrix. These systems are
extremely versatile gels, in terms of composition, and offer
potential for fine tuning of both structure and function, as
each individual component can be varied. Here, the
characterization and application of these gels as sensing thin
films in gas sensor devices is presented. The unique
supramolecular structure of the gels is explored for molecular
recognition of volatile organic compounds (VOCs) by
employing gels with distinct formulations to yield
combinatorial optical and electrical responses used in the
distinction and identification of VOCs.
Esteves C, Santos GMC, Alves C, Palma S, Porteira AR, Filho J, HA C, Alves VD, Faustino BMM, Ferreira I, Gamboa H, Roque ACA.
2019.
Effect of film thickness in gelatin hybrid gels for artificial olfaction. Materials Today Bio. 1:-.
AbstractArtificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 μm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 μm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.