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Autonomous Microsensors for Chemical Analysis

Autonomous Microsensors for Chemical Analysis. PI: Alexander Mamishev Department of Electrical Engineering University of Washington, Seattle mamishev@ee.washington.edu http://www.ee.washington.edu/research/seal. Outline. Motivation Theoretical background System overview Smart tubing

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Autonomous Microsensors for Chemical Analysis

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  1. Autonomous Microsensors for Chemical Analysis PI: Alexander Mamishev Department of Electrical Engineering University of Washington, Seattle mamishev@ee.washington.edu http://www.ee.washington.edu/research/seal

  2. Outline • Motivation • Theoretical background • System overview • Smart tubing • External sensors • Internal sensors • In-line sensors • Cost considerations • Conclusions

  3. Motivation • Continuous process monitoring and control for microreactors • Before, during, and after reaction • Feedforward and feedback control • Auto-calibration of sensors through multi-point measurements • Miniaturization of sensor arrays • Cost-reduction of sensing systems

  4. Physics of Dielectrics The figure depicts an idealized view of dielectric phenomena. A variety of physical mechanisms contribute to the polarization of the material. Each mechanism has a characteristic frequency above which its contribution vanishes. Therefore dielectric spectra will generally appear as decreasing functions of frequency. The table shows the different physical mechanisms and their typical characteristic frequencies.

  5. Multipoles The concept of multipoles is used to describe the distribution of charges (and, consequently, distribution of space potential) in complex molecules. Multiple color peaks in the figure on the right indicate equivalent multiple charge concentrations with the associated radii larger then those of outer electrons. Naturally, such structures have slower response to alternating electric field excitation. Practical example: Organic materials contain long molecular chains that have specific relaxation times. Detection of associated peaks may allow identification of molecular structure (in a limited subset of suspect materials). This color map show the distribution of space potential in n-pentane (alkaline).

  6. Nature of dielectric signatures Dielectric signatures are characterized by smooth variation rather than narrowband features. Best selectivity will be obtained by measuring with relatively few data points over a large a frequency range. Measurements below about 1 Hz are impractical for a real time system because measurement time becomes too long (proportional to period). For selectivity, the method should be combined with other techniques.

  7. Dielectrometry

  8. Sensor Embodiments Internal sensor External sensor In-line sensor Micro- reactor Smart tubing

  9. Smart tubing: spectroscopy and imaging • Concentration • Composition • Reaction stage • Micro-particle size • Viscosity

  10. Smart tubing: differential measurements • Flow uniformity • Auto-calibration • Residual contamination

  11. Macroworld example of “smart tubing” • Metal pipe section replaced with custom made plastic pipe fitted with an dielectric spectroscopy sensor

  12. External sensors Non-destructive, one-sided access Non-uniform field energy distribution Non-uniform measurement sensitivity – allows correction for barrier effects

  13. The Ideal Internal Sensor • Wireless • Self-powered • Non-invasive • Can withstand harsh environments • Explosion proof • Inexpensive

  14. WISP system RFID Reader power ID sensor Data WISP Power Harvesting Computing No battery! ID Sensing

  15. PCB version of the WISP • Completed fully function WISP • Ready for applications • Still working on range and antenna design Generations of Power Harvesters and WISPs

  16. In-line sensor on NeSSI Platform Integration of a microfabricated sensor with a NeSSI block for fluid measurement • Initial tests will be focused on detection of water in oil, and mixtures of alcohols • Allows for high material feed rates due to non-contact sensor design • Offers non-destructive, inline testing of material • Uses data acquisition hardware previously developed in SEAL

  17. Test Wafers Back form Fab

  18. Reaction Monitoring Example

  19. Cost considerations • “Headquarter” labs routinely install analytical instrumentation in the price range of $50K to $200K • High-end dielectric spectroscopy equipment falls in this range (Agilent, Solartron, Novocontrol) • Small plants and micro-reactors cannot support this level of investment • Dielectric spectroscopy equipment in the range $5K - $10K would be adequate • Commerciallization of system developed in SEAL is currently supported by the NSF STTR grant in partnership with Kraft (we could use additional partners)

  20. mini-DiSPEC • Small, low-cost, portable, dielectric spectroscopy system

  21. LabView Screenshot

  22. Conclusions: Sensor Miniaturization • Dielectric spectroscopy sensors are positioned for installation throughout the micro-reactor platform • Measurements range from sophisticated dielectric spectroscopy (instrumentation cost about $15K per channel) to simple single frequency units (instrumentation cost below $1K per channel) • Inexpensive and mature technology available for mass fabrication at MEMS level • Small and disposable

  23. Acknowledgements Presented research work includes results from projects sponsored by: • National Science Foundation • Center for Processing Analytical Chemistry • Kraft Key contributing graduate students: • Xiaobei Li, Kishore Sundara-Rajan, Gabriel Rowe, Abhinav Mathur

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