Resonant Silicon MEMS/NEMS Environmental Pollution Sensors

Environmental pollution is proved to be a great harm to human health and has thus become a worldwide issue. According to the World Health Organization (WHO) 80% of people in urban environment are exposed to unhealthy levels of pollution, e. g., respirable particulate matter (PM) linked with asthma, heart decease, stroke, etc. Spatio-temporal fluctuations of pollution, e.g. by traffic cannot be monitored sufficiently using the currently operated few stationary systems. Therefore, according to a new monitoring paradigm, dense networks of smartphone-based sensor nodes will be set-up in near future, which require low-cost, small, lightweight, and networked air pollution sensors for on-the-spot recognition and rapid diagnosis of pollution.

The Institute of Semiconductor Technology (IHT) has developed a micro-/nano-electro-mechanical system (MEMS/NEMS) based pollution monitor, which can detect PM with diameters down to the sub-100 nm range. Its operation principle is based on measuring PM mass concentration indicated by the resonance frequency shift of a microcantilever. Even single nanoparticle of femtogram mass can be detected, if nanowires are used as resonant structures. MEMS design and fabrication processes have to be developed and optimized to integrate nanowire resonators on piezoresistive cantilevers for direct read-out. Test of realized devices shall be performed with reference PM under laboratory and field conditions. Furthermore, functionalization of prototypes shall be investigated to achieve trace-level chemical detection or recognition of harmful gases such as CO, NOx, SO2, greenhouse gases (CO2,CH4), volatile organic compounds (VOCs), explosives, viruses/bacteria, etc.

The PM/gas pollution sensor to be developed will be used to demonstrate the performance of MEMS/NEMS-based devices for enabling public or workmen to monitor indoor pollution concentrations they breathe (personal monitoring) as well as sharing measured outdoor pollution concentrations with others (participatory/crowd/pervasive monitoring).

PhD Student