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

Abstract

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.

Numerical Modelling of Carrier Transport in Organic Field Effect Transistors

“…At recent years, the interest with low-power consumption sensors increases, where massive studies focus on cost-effective devices for sensing different environmental as well as medical parameters glows up [1][2][3][4][5]. Different electronic organic-based devices (ex: light-emitting diodes and transistors) have been studied and researched extensively seeking for lower weight, sustainability, flexibility, and lower energy consumption [6,7].…”

A Design of Experiment Rational Optimization of the Degumming Process and Its Impact on the Silk Fibroin Properties

“…This procedure is known as one factor at a time (OFAT); it does not take into consideration the process in its complexity, neither the interaction that could be present among the different process variables. Instead, the application of statistical methods may be effectively used to optimize a process even in the case of silk fibroin. − In particular, a design of experiment (DOE) method allows one to distinguish the statistical relevant process factors from the irrelevant ones and to build empirical equations that relate the factors and their interaction with the material properties of interest (from now on called yields) − whithout engaging in a mechanistic description of the phenomena, as usually done by other disciplines. In this study, by the use of DOE, we characterized the most used alkali degumming with Na 2 CO 3 , taking into consideration the main process variables involved and modifying them within a selected range.…”

Modeling a Dynamic Printability Window on Polysaccharide Blend Inks for Extrusion Bioprinting

“…To extrapolate an empirical model, a Response Surface Method (RMS), previously used on different problems, ,− has been adopted. The entire statistical analysis has been done by the use of the programming language R following the statistical strategy described in previous works. ,,, An initial analysis was done by calculating a correlation matrix in which the value of r 2 was reported for couples of variables; this was used to verify the presence of linear correlation among them (correlation of the first order).…”