Room Temperature CO Detection by Hybrid Porphyrin-ZnO Nanoparticles

AbstractPorphyrins are the natural candidates to the detection of carbon monoxide however the physical properties of solid-state layers of porphyrins limit their use as gas sensors mainly with mass and optical transducers. Recently we shown that the photonic properties of porphyrins, brilliantly exploited in organic solar cells, can lead to a new class of photo-activated sensors made by porphyrins coated metal oxides. Here we investigate the sensitivity to carbon monoxide of resistive sensors made by zinc oxide nanoparticles coated by a porphyrin layer. Sensors were prepared following two different routes and tested, at room temperature and in various light conditions, to CO and few volatile compounds. Results show a significant sensitivity and selectivity to CO

An array of physical sensors and an adaptive regression strategy for emotion recognition in a noisy scenario

Several studies demonstrate that since emotions are spontaneously manifested through different measurable quantities (e.g. vocal and facial expressions), this makes possible a sort of automatic estimation of emotion from objective measurements. However, the reliability of such estimations is strongly influenced by the availability of the different sensor modalities used to monitor the affective status of a subject, and furthermore the extraction of objective parameters is sometime thwarted in a noisy and disturbed environment. This paper introduces a personalized emotion estimation based on a heterogeneous array of physical sensors for the measurement of vocal, facial, and physiological (electro-cardiogram and electro-dermal) activities. As a proof of concept, changes in the levels of both emotion reactiveness and pleasantness are estimated under critical operative conditions. The estimator model takes advantage from the time-varying selection of the most relevant non-spurious sensors features and the adaptation of the k-nearest neighbour paradigm to the continuous identification of the most affine model templates. The model, once trained, demonstrated to autonomously embed new sensorial input and adapt to unwanted/unpredicted sensor noise or emotion alteration. The proposed approach has been successfully tested on the RECOLA database, a multi-sensorial corpus of spontaneous emotional interactions in French