Correlation of gaseous emissions to water stress in tomato and maize crops: From field to laboratory and back

Zonta

Gherardi

et al. 2020

Sensors

Self Cite

Among the various chemoresistive gas sensing properties studied so far, the sensing response reproducibility, i.e., the capability to reproduce a device with the same sensing performance, has been poorly investigated. However, the reproducibility of the gas sensing performance is of fundamental importance for the employment of these devices in on-field applications, and to demonstrate the reliability of the process development. This sensor property became crucial for the preparation of medical diagnostic tools, in which the use of specific chemoresistive gas sensors along with a dedicated algorithm can be used for screening diseases. In this work, the reproducibility of SmFeO3 perovskite-based gas sensors has been investigated. A set of four SmFeO3 devices, obtained from the same screen-printing deposition, have been tested in laboratory with both controlled concentrations of CO and biological fecal samples. The fecal samples tested were employed in the clinical validation protocol of a prototype for non-invasive colorectal cancer prescreening. Sensors showed a high reproducibility degree, with an error lower than 2% of the response value for the test with CO and lower than 6% for fecal samples. Finally, the reproducibility of the SmFeO3 sensor response and recovery times for fecal samples was also evaluated.

Section: Introductionmentioning

confidence: 99%

Nanostructured SmFeO3 Gas Sensors: Investigation of the Gas Sensing Performance Reproducibility for Colorectal Cancer Screening

Zonta

Gherardi

et al. 2020

Sensors

Self Cite

“…The market for gaseous sensors is continuously growing. There is a significant demand for small, low-cost and high-performance gas sensors in several sectors, including chemical process control, precision agriculture, environmental monitoring, and medical applications [1][2][3][4][5]. Among the various new classes of smart gas detectors, one of the most investigated and used are the chemoresistive gas sensors, which show high sensitivity, allowing to detect low concentrations of gases (ppm-ppb range) with high accuracy [6][7][8][9].…”

Section: Discussionmentioning

confidence: 99%

“…A desirability approach has been used to perform the numerical optimization, this method is quite general and has been applied to solve different typology of problems, from the inkjet printing optimization [25][26][27] to biopolymer processing [28][29][30]. This method is based on the desirability functions which value can vary in the [1,0] range, where 1 represents the optimum solution. A single desirability function was assigned to each of the yield (Yi).…”

Section: Methodsmentioning

confidence: 99%

Dataset of the Optimization of a Low Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis

Novel

Scattolo

et al. 2021

Data

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Over the last few years, employment of the standard silicon microfabrication techniques for the gas sensor technology has allowed for the development of ever-small, low-cost, and low-power consumption devices. Specifically, the development of silicon microheaters (MHs) has become well established to produce MOS gas sensors. Therefore, the development of predictive models that help to define a priori the optimal design and layout of the device have become crucial, in order to achieve both low power consumption and high mechanical stability. In this research dataset, we present the experimental data collected to develop a specific and useful predictive thermal-mechanical model for high performing silicon MHs. To this aim, three MH layouts over three different membrane sizes were developed by using the standard silicon microfabrication process. Thermal and mechanical performances of the produced devices were experimentally evaluated, by using probe stations and mechanical failure analysis, respectively. The measured thermal curves were used to develop the predictive thermal model towards low power consumption. Moreover, a statistical analysis was finally introduced to cross-correlate the mechanical failure results and the thermal predictive model, aiming at MH design optimization for gas sensing applications. All the data collected in this investigation are shown.

“…Although the advent of these innovative materials for chemoresistive gas sensors [ 22 , 23 ], Semiconducting Metal Oxide (SMO) still remain the most widely investigated and commercially used [ 24 , 25 ], employed in different applications [ 26 , 27 , 28 , 29 , 30 ]. These devices are composed of a nanostructured SMO as the sensing material and a heated substrate [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Low-Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis

Novel

Scattolo

et al. 2021

Sensors

Self Cite

The substrate plays a key role in chemoresistive gas sensors. It acts as mechanical support for the sensing material, hosts the heating element and, also, aids the sensing material in signal transduction. In recent years, a significant improvement in the substrate production process has been achieved, thanks to the advances in micro- and nanofabrication for micro-electro-mechanical system (MEMS) technologies. In addition, the use of innovative materials and smaller low-power consumption silicon microheaters led to the development of high-performance gas sensors. Various heater layouts were investigated to optimize the temperature distribution on the membrane, and a suspended membrane configuration was exploited to avoid heat loss by conduction through the silicon bulk. However, there is a lack of comprehensive studies focused on predictive models for the optimization of the thermal and mechanical properties of a microheater. In this work, three microheater layouts in three membrane sizes were developed using the microfabrication process. The performance of these devices was evaluated to predict their thermal and mechanical behaviors by using both experimental and theoretical approaches. Finally, a statistical method was employed to cross-correlate the thermal predictive model and the mechanical failure analysis, aiming at microheater design optimization for gas-sensing applications.