Recent Advances in Nano-Mechanical Biosensors

Prof. Thomas Thundat
SUNY, Buffalo

Nano-mechanical sensors based on cantilevers have been used for the high sensitivity detection of biomolecules. Biomolecular adsorption on a cantilever results in cantilever deflection due to adsorption forces. In addition, the resonance frequency varies due to mass loading. Selectivity in detection is achieved using immobilized chemical or biological interfaces. Enhancing selectivity, sensitivity, and reliability of these sensors has been the focus of intensive investigations over the last decade or so. Although much success has been achieved in enhancing sensitivity, obtaining very high selectivity and reliability remains elusive. Improving selectivity and reliability of these sensors is essential in translating this highly sensitive sensor platform into a commercial reality. A cantilever operating in vacuum can detect changes in adsorbed mass with sub-femtogram resolution, but severe damping limits the mass resolution by orders of magnitude when operated under solution. Recent advances in fabricating the cantilevers as hollow channels allow confining liquids inside the channel for operation under vacuum for enhanced sensitivity. By coupling a hollow channel cantilever with capillary electrophoresis, it is possible to develop a time of flight mass spectrometer that can detect separated biomolecules in their native state. These cantilevers, when fabricated as bimaterial cantilevers, can be used as highly sensitive thermal sensors for photothermal spectroscopy of confined liquids. Cantilever-based photothermal spectroscopy overcomes many of the selectivity challenges encountered when using receptor-based approaches. Irreproducibility is another major challenge for nano-mechanical biosensors. However, adsorption-induced stress energy evolution pathways offer an exciting new way of increasing the reliability of nano-mechanical chemical and biological sensing.  Understanding energy dissipation at resonance holds the key and has the potential to impart high selectivity to the nano-mechanical sensors. Multi-modal data obtained with these approaches when analyzed using deep learning techniques, can enhance the selectivity, sensitivity, and reliability of nano-mechanical sensor platform.


Prof. Thomas Thundat

Thomas Thundat is a member of Chemical and Biological Engineering RENEW faculty at SUNY, Buffalo. He was formerly Canada Excellence Research Chair professor at the University of Alberta, Edmonton, Canada, and is a Distinguished Professor (hon.) at the Indian Institute of Technology, Madras, and Centenary Professor at the Indian Institute of Science, Bangalore. He is the author of over 400 publications in refereed journals, 45 book chapters, and 40 patents. Dr. Thundat is an elected Fellow of the American Physical Society (APS), the Electrochemical Society (ECS), the American Association for Advancement of Science (AAAS), the American Society of Mechanical Engineers (ASME), the SPIE, and the National Academy of Inventors (NAI). Dr. Thundat’s research is currently focused on novel physical, chemical, and biological detection using micro and nano mechanical sensors and electrical power delivery using the single wire concept.