A gas-sensitive hybrid material consisting of Cu2O nanoparticle-decorated WO3 nanoneedles is successfully grown for the first time in a single step via aerosol-assisted chemical vapor deposition. Morphological, structural, and composition analyses show that our method is effective for growing single-crystalline, n-type WO3 nanoneedles decorated with p-type Cu2O nanoparticles at moderate temperatures (i.e., 380 °C), with cost effectiveness and short fabrication times, directly onto microhot plate transducer arrays with the view of obtaining gas sensors. The gas-sensing studies performed show that this hybrid nanomaterial has excellent sensitivity and selectivity to hydrogen sulfide (7-fold increase in response compared with that of pristine WO3 nanoneedles) and a low detection limit (below 300 ppb of H2S), together with unprecedented fast response times (2 s) and high immunity to changes in the background humidity. These superior properties arise because of the multiple p-n heterojunctions created at the nanoscale in our hybrid nanomaterial.

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We report for the first time the successful synthesis of palladium (Pd) nanoparticle (NP)-decorated tungsten trioxide (WO3) nanoneedles (NNs) via a two-step aerosol-assisted chemical vapor deposition approach. Morphological, structural, and elemental composition analysis revealed that a Pd(acac)2 precursor was very suitable to decorate WO3 NNs with uniform and well-dispersed PdO NPs. Gas-sensing results revealed that decoration with PdO NPs led to an ultrasensitive and selective hydrogen (H2) gas sensor (sensor response peaks at 1670 at 500 ppm of H2) with low operating temperature (150 °C). The response of decorated NNs is 755 times higher than that of bare WO3 NNs. Additionally, at a temperature near that of the ambient temperature (50 °C), the response of this sensor toward the same concentration of H2 was 23, which is higher than that of some promising sensors reported in the literature. Finally, humidity measurements showed that PdO/WO3 sensors displayed low-cross-sensitivity toward water vapor, compared to bare WO3 sensors. The addition of PdO NPs helps to minimize the effect of ambient humidity on the sensor response.

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a b s t r a c tThis paper reports the use of a hybrid electronic tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modified graphite-epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles, while LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types.

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