1D Titanium Dioxide: Achievements in Chemical Sensing

For the last two decades, titanium dioxide (TiO₂) has received wide attention in several areas such as in medicine, sensor technology and solar cell industries. TiO₂-based gas sensors have attracted significant attention in past decades due to their excellent physical/chemical properties, low cost and high abundance on Earth. In recent years, more and more efforts have been invested for the further improvement in sensing properties of TiO₂ by implementing new strategies such as growth of TiO₂ in different morphologies. Indeed, in the last five to seven years, 1D nanostructures and heterostructures of TiO₂ have been synthesized using different growth techniques and integrated in chemical/gas sensing. Thus, in this review article, we briefly summarize the most important contributions by different researchers within the last five to seven years in fabrication of 1D nanostructures of TiO₂-based chemical/gas sensors and the different strategies applied for the improvements of their performances. Moreover, the crystal structure of TiO₂, different fabrication techniques used for the growth of TiO₂-based 1D nanostructures, their chemical sensing mechanism and sensing performances towards reducing and oxidizing gases have been discussed in detail.

Multidisciplinary Approach to Characterizing the Fingerprint of Italian EVOO

Extra virgin olive oil (EVOO) is characterized by its aroma and other sensory attributes. These are determined by the geographical origin of the oil, extraction process, place of cultivation, soil, tree varieties, and storage conditions. In the present work, an array of metal oxide gas sensors (called S3), in combination with the SPME-GC-MS technique, was applied to the discrimination of different types of olive oil (phase 1) and to the identification of four varieties of Garda PDO extra virgin olive oils coming from west and east shores of Lake Garda (phase 2). The chemical analysis method involving SPME-GC-MS provided a complete volatile component of the extra virgin olive oils that was used to relate to the S3 multisensory responses. Furthermore, principal component analysis (PCA) and k-Nearest Neighbors (k-NN) analysis were carried out on the set of data acquired from the sensor array to determine the best sensors for these tasks and to assess the capability of the system to identify various olive oil samples. k-NN classification rates were found to be 94.3% and 94.7% in the two phases, respectively. These first results are encouraging and show a good capability of the S3 instrument to distinguish different oil samples.