In vitro gastrointestinal gas monitoring with carbon nanotube sensors.

In vitro simulators of the human gastrointestinal (GI) tract are remarkable technological platforms for studying the impact of food on the gut microbiota, enabling continuous and real-time monitoring of key biomarkers. However, comprehensive real-time monitoring of gaseous biomarkers in these systems is required with a cost-effective approach, which has been challenging to perform experimentally to date. In this work, we demonstrate the integration and in-line use of carbon nanotube (CNT)-based chemiresitive gas sensors coated with a thin polydimethylsiloxane (PDMS) membrane for the continuous monitoring of gases within the Simulator of the Human Microbial Ecosystem (SHIME). The findings demonstrate the ability of the gas sensor to continuously monitor the different phases of gas production in this harsh, anaerobic, highly humid, and acidic environment for a long exposure time (16 h) without saturation. This establishes our sensor platform as an effective tool for real-time monitoring of gaseous biomarkers in in vitro systems like SHIME.

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

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.