Publications
Nanoporous framework materials interfaced with mechanical sensors for highly-sensitive chemical sensing
We will describe how novel nanoporous framework materials (NFM) such as metal-organic frameworks (MOFs) can be interfaced with common mechanical sensors, such as surface acoustic wave (SAW), microcantilever array, and quartz crystal microbalance (QCM) devices, and subsequently be used to provide selectivity and sensitivity to a broad range of analytes including explosives, nerve agents, and volatile organic compounds (VOCs). NFM are highly ordered, crystalline materials with considerable synthetic flexibility resulting from the presence of both organic and inorganic components within their structure. Chemical detection using micro-electro-mechanical-systems (MEMS) devices (i.e. SAWs, microcantilevers) requires the use of recognition layers to impart selectivity. Unlike traditional organic polymers, which are dense, the nanoporosity and ultrahigh surface areas of NFM allow for greater analyte uptake and enhance transport into and out of the sensing layer. This enhancement over traditional coatings leads to improved response times and greater sensitivity, while their ordered structure allows chemical tuning to impart selectivity. We describe here experiments and modeling aimed at creating NFM layers tailored to the detection of water vapor, explosives, CWMD, and volatile organic compound (VOCs), and their integration with the surfaces of MEMS devices. Molecular simulation shows that a high degree of chemical selectivity is feasible. For example, a suite of MOFs can select for strongly interacting organics (explosives, CWMD) vs. lighter volatile organics at trace concentrations. At higher gas pressures, the CWMD are deselected in favor of the volatile organics. We will also demonstrate the integration of various NFM on the surface of microcantiliver arrays, QCM crystals, and SAW devices, and describe new synthetic methods developed to improve the quality of NFM coatings. Finally, MOF-coated MEMS devices show how temperature changes can be tuned to improve response times, selectivity, and sensitivity.