Aerosol-Assisted CVD-Grown PdO Nanoparticle-Decorated Tungsten Oxide Nanoneedles Extremely Sensitive and Selective to Hydrogen.

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.

E-Nose and e-Tongue combination for improved recognition of fruit juice samples.

There are many important challenges related to food security analysis by application of chemical and electrochemical sensors. One critical parameter is the development of reliable tools, capable of performing an overall sensory analysis. In these systems, as much information as possible is required in relation to smell, taste and colour. Here, we investigated the possibility of using a multisensor data fusion approach, which combines an e-Nose and an e-Tongue, adept in generating combined aroma and taste profiles. In order to shed light on this concept, classification of various Tunisian fruit juices using a low-level of abstraction data fusion technique was attempted. Five tin oxide-based Taguchi Gas Sensors were applied in the e-Nose instrument and the e-Tongue was designed using six potentiometric sensors. Four different commercial brands along with eleven fruit juice varieties were characterised using the e-Nose and the e-Tongue as individual techniques, followed by a combination of the two together. Applying Principal Component Analysis (PCA) separately on the respective e-Nose and e-Tongue data, only few distinct groups were discriminated. However, by employing the low-level of abstraction data fusion technique, very impressive findings were achieved. The Fuzzy ARTMAP neural network reached a 100% success rate in the recognition of the eleven-fruit juices. Therefore, data fusion approach can successfully merge individual data from multiple origins to draw the right conclusions that are more fruitful when compared to the original single data. Hence, this work has demonstrated that data fusion strategy used to combine e-Nose and e-Tongue signals led to a system of complementary and comprehensive information of the fruit juices which outperformed the performance of each instrument when applied separately.