Development of a Room Temperature SAW Methane Gas Sensor Incorporating a Supramolecular Cryptophane A Coating

A new room temperature supra-molecular cryptophane A (CrypA)-coated surface acoustic wave (SAW) sensor for sensing methane gas is presented. The sensor is composed of differential resonator-oscillators, a supra-molecular CrypA coated along the acoustic propagation path, and a frequency signal acquisition module (FSAM). A two-port SAW resonator configuration with low insertion loss, single resonation mode, and high quality factor was designed on a temperature-compensated ST-X quartz substrate, and as the feedback of the differntial oscillators. Prior to development, the coupling of modes (COM) simulation was conducted to predict the device performance. The supramolecular CrypA was synthesized from vanillyl alcohol using a double trimerisation method and deposited onto the SAW propagation path of the sensing resonators via different film deposition methods. Experiential results indicate the CrypA-coated sensor made using a dropping method exhibits higher sensor response compared to the unit prepared by the spinning approach because of the obviously larger surface roughness. Fast response and excellent repeatability were observed in gas sensing experiments, and the estimated detection limit and measured sensitivity are ~0.05% and ~204 Hz/%, respectively

Computational Optimization of Metal-Organic Framework (MOF) Arrays for Chemical Sensing

Although commercial gas sensors exist for applications such as product quality control, industrial food monitoring, and smoke detection, there are many potential applications for which adequate gas sensing technology is lacking. There is an unmet need for gas sensors to detect natural gas leaks, for disease detection via breath analysis, and for environmental monitoring, to name just a few examples. Current gas sensors do not exhibit the sensitivity and/or selectivity required to detect trace amounts of the required gases in complex gas mixture environments (e.g., ambient air or a patient’s breath). It is known that arrays of sensors, or electronic noses, improve chemical detection when compared to single sensor elements. Although some work has been done to optimize sensor device performance, there are many potential sensing materials that have not yet been extensively explored. Herein, we explore the use of metal-organic framework (MOF) materials in sensor arrays, exploiting their high adsorption capabilities to yield more selective and sensitive electronic noses. As a relatively new class of materials, MOFs have not been thoroughly investigated for gas sensing applications. In particular, prior to our work, there had only been a few investigations of MOF sensor arrays and those were limited to purely experimental work that relied heavily on trial-and-error. We demonstrate that leveraging computational modeling and optimization to rationally design MOF sensor arrays can yield significantly improved sensing performance. Our novel computational method was carried out first by predicting individual MOF sensor responses via molecular simulations. Then, we developed a method to analyze those individual responses and provide output signals for entire sensor arrays to predict unknown gas mixtures. Following this, the prediction ability of each array was evaluated according to the Kullback-Liebler divergence (KLD), where we determined the best arrays for detecting methane-in-air mixtures. Finally, we developed and validated a genetic algorithm that enables the optimization of large MOF arrays