Domenico Suriano

Thin-Film Bulk-Acoustic-Resonator Gas Sensor Functionalized With a Nanocomposite Langmuir–Blodgett Layer of Carbon Nanotubes

A thin-film bulk acoustic resonator (TFBAR) based on a vibrating membrane of AlN/Si3N4 has been fabricated onto a silicon substrate and functionally characterized as gas sensor at a resonating frequency of 1.045 GHz. This novel TFBAR-based gas sensor has been functionalized by a sensing nanocomposite layer, prepared by a Langmuir-Blodgett (LB) technique, of single-walled carbon nanotubes (SWCNTs) embedded in a host matrix of organic material of cadmium arachidate. High-performance gas detection at room temperature of a SWCNT-coated TFBAR sensor has been reported. The sensing device exhibits high sensitivity (e.g., acetone: 12 kHz/ppm; ethylacetate: 17.3 kHz/ppm), fast response (within 2-3 min), slow reversibility (within 1 h), and good repeatability (les 5% variation) of response toward tested organic vapors of acetone, ethylacetate, and toluene.

Towards air quality indices in smart cities by calibrated low-cost sensors applied to networks

This experimental study focusses on air quality monitoring by low-cost and accurate sensors to provide a rank of air quality indices for citizens community in smart cities. Inter-comparison long-term measurements of CO, NO2, SO2 and PM10 are performed in a real scenario using referenced chemical analyzers at air-quality monitoring stations. The accuracy of the low-cost sensors is assessed in order to address the Data Quality Objectives (DQO) of the EU Air Quality Directive 2008/50/EC for indicative measurements. These preliminary results show that the low-cost sensors, when accurate, are suitable to define a rank of individual air quality index (AQI) to inform effectively general public and enhance environmental awareness. Finally, we show that the developed multiparametric sensor-system NASUS can be integrated both in a distributed city-network based on cost-effective fixed nodes, and in portable handheld sensor-systems to monitor air-pollution personal exposure.

Surface Acoustic Wave 915 MHz resonator oscillator gas sensors using SnO 2 nanowires-based nanocomposite layer

In this paper we report on Surface Acoustic Wave (SAW) gas sensors based on quartz two-port resonators configured as oscillators at resonant frequency of 915 MHz. Nanowires (NW) of semiconducting tin dioxide (SnO2) have been grown by Vapor Phase (VP) process and used as filler in a nanocomposite layer to fabricate a highly-sensitive nanomaterial for gas detection, at room temperature. The nanocomposite layer consisting of an organic host-matrix of cadmium arachidate (CdA) and a weight-tailored filler of SnO2 NW has been deposited as thin film onto SAW resonators by means of the molecular engineering Langmuir-Blodgett (LB) technique. SAW gas sensors performance was investigated in presence of ppm-level of ethanol, methanol, ethylacetate, toluene, at room temperature. The results demonstrate good sensitivity to vapors under test at ppm-level and a SAW gas response tuned by the weight-content of filler of SnO2 NW in the LB nanocomposite layer.

Organic Vapor Detection by QCM Sensors Using CNT-Composite Films

A Quartz Crystal Microbalance (QCM) gas sensor coated with carbon nanotubes (CNTs) layered films as chemically interactive nanomaterial is described. A QCM resonator integrated on AT-cut quartz substrate has been functionally characterized as oscillator at the resonant frequency of 10 MHz. The CNTs have been grown by chemical vapor deposition (CVD) system onto alumina substrates, coated with 2.5 nm thick Fe catalyst, at a temperature of 750°C in H2/C2H2 gaseous ambient as active materials for gas sensors. CNTs multilayers, with and without buffer layer of cadmium arachidate (CdA), have been prepared by the Langmuir-Blodgett (LB) technique to coat at the double-side the QCM sensors for organic vapor detection, at room temperature. It was demonstrated that the highest mass sensitivity has been achieved for CNTs multilayer onto CdA buffer material due to the greatest gas adsorbed mass. The sensing properties of the CNTs-sensors at enhanced mass sensitivity have been investigated for different vapors of ethanol, methanol, acetone, m-xylene, toluene and ethylacetate in a wide range of concentration from 10 to 800 ppm. The CNTs-based QCM-sensors exhibit high sensitivity (e.g., 5.55 Hz/ppm to m-xylene of the CNTs-multilayer) at room temperature, fast response, linearity, reversibility, repeatability, low drift of the baseline frequency, potential sub-ppm range detection limit.

Application of Artificial Neural Networks to a Gas Sensor-Array Database for Environmental Monitoring

A sensors array based on two different types of chemical sensors such as tin dioxide commercial sensors and carbon nanotubes innovative sensors developed in the ENEA laboratories to monitor gases (e.g., CO, NO2, SO2, H2S and CO2) of relevance in polluted air has been analyzed. Measurements of chemical sensing of the sensors array have been performed in laboratory to create a database for applying artificial neural networks (ANNs) algorithms to quantify gas concentration of individual air pollutants and binary gas-mixture. A total number of 3,875 data-samples based on 413 distinct gas concentrations measured by 14 gas sensors has been used in the database. The ANN performance has been assessed for each targeted air-pollutant. The lowest normalized mean square error (NMSE) of 6%, 9% and 11% has been achieved for NO2, SO2 and CO2, respectively. In the contrast, NMSE as high as 28% and 39% has been measured for CO and H2S, respectively. The aim of this study is the selection of an optimal set of gas sensors in the array for enhanced environmental measurements of gas concentration in real-scenario.

A Portable Sensor System for Air Pollution Monitoring and Malodours Olfactometric Control

A portable sensor-system based on solid-state gas sensors has been designed and implemented as proof-of-concept for environmental air-monitoring applications and malodours olfactometric control. Commercial gas sensors (metal-oxides, n-type) and nanotechnology sensors (carbon nanotubes, p-type) are arranged in a configuration of array for multisensing and multiparameter devices. Wireless sensors at low-cost are integrated to implement a portable and mobile node, that can be used as early-detection system in a distributed sensor network. Real-time and continuous monitoring of hazardous air-contaminants (e.g., NO2, CO, SO2, BTEX, etc.) has been performed by in-field measurements. Moreover, monitoring of landfill gas generated by fermentation of wastes in a municipal site has been carried out by the portable sensor-system. Also, it was demonstrated that the sensor-system is able to assess the malodours emitted from a municipal waste site and remarkably compared to the olfactometry method based on a trained test panel.

Thin Film Bulk Acoustic Resonator Vapor Sensors with Single-Walled Carbon Nanotubes-based Nanocomposite Layer

We demonstrate the successful operation of a chemical microsensor based on thin film bulk acoustic resonator (TFBAR) for organic vapor detection at room temperature. The TFBAR consists of a vibrating membrane of AlN/Si3N4 fabricated on silicon substrate and resonating at the frequency of 1.045 GHz. Using a nanocomposite layer based on Single-Walled Carbon Nanotubes (SWCNTs) and prepared by the Langmuir-Blodgett technique onto the TFBAR device as highly-sensitive nanomaterial, the sensing performance of TFBAR sensor has been evaluated both as a passive device by a network analyzer with phase and insertion loss responses, and as oscillator with frequency response. The vapor sensing characteristics of SWCNTs-based TFBAR sensor are presented illustrating interesting results.

A Portable Gas Sensor System for Air Quality Monitoring

In ENEA, at Brindisi Research Center, a portable gas sensor system called NASUS IV based on solid-state gas sensors was built. This system is the last result of our technology researches in the area of tiny and portable sensor systems for air quality control. The main goal of the system designed and built in our laboratory is the development of a handheld device for detecting some pollutant gases such as CO, SO2, NO2, and H2S. In order to test this machine in our laboratory under conditions similar to real situations, we employed a wide-volume gas chamber provided by an input and an output pipe. We put NASUS IV in the previously mentioned chamber, and we performed several tests with different kind of targeted gases. Future works concern about the employment of the NASUS IV in real environment by performing an experimental campaign in collaboration with the public regional environmental protection agency (ARPA-Puglia), which will provide in-field fixed stations in order to compare the performance of our machine with the conventional gas analyzers.

Tuned Sensing Properties of Metal-Modified Carbon-Based Nanostructures Layers for Gas Microsensors

In this work, carbon nanomaterials have been prepared by CVD technology onto alumina substrates, coated by nanosized Co-catalyst at different thickness (2.5 nm and 7.5 nm) and used for a simple gas sensor device. The surface has been functionalized with sputtered Pt-nanocluster at a tuned loading of 8, 15 and 30 nm. The response of the chemiresistors in terms of p-type electrical conductance has been investigated as a function of the thickness of the Pt-nanoclusters towards different gases (NO2, NH3, CO, CH4, CO2). Furthermore, the effect of the temperature ranging from 20°C to 250°C on the sensor response has been addressed as well. Additionally, a short-term stability of the carbon nanomaterials based sensor towards NO2 gas detection has been investigated for a 2-month period. The gas sensors based on Pt-modified carbon nanomaterials exhibit higher sensitivity compared to unmodified material, fast response, reversibility, repeatability, moderate drift of the baseline signal, sub-ppm range detection limit.

A Gas Sensor Array For Environmental Air Monitoring: A Study Case Of Application Of Artificial Neural Networks

An array of commercial gas sensors and nanotechnology sensors has been integrated to quantify gas concentration of air‐pollutants. A variety of chemoresistive gas sensors, commercial (Figaro and Fis) and developed at ENEA laboratories (metal‐modified carbon nanotubes) were tested to implement a database useful for applied artificial neural networks (ANNs). The ANN algorithm used is the common perceptron multi‐layer feed‐forward network based on error back‐propagation. Electronic Noses based on various sensor arrays related to mammalian olfactory systems have been largely reported [1,2]. Here, we reported on the perceptron‐based ANNs applied to a large database of 3875 datapoints for environmental air monitoring. The ANNs performance has been individually assessed for any targeted gas. The response of the classifier has been measured for NO2, CO, CO2, SO2, and H2S gas. The NO2 characteristics exhibit that real concentrations and predicted concentrations are very close with a normalized mean square error (N...