System and method of fluctuation enhanced gas-sensing using saw devices

• US 7,286,942 B1
• Filed: 10/02/2003
• Issued: 10/23/2007
• Est. Priority Date: 05/30/2003
• Status: Expired due to Fees

• Current Assignee: The Government Of The United States As Represented By The Secretary Of The Navy
• Original Assignee: the united states of america as represented by the secretary of the navy
• Inventors: Kish, Laszlo B., Schmera, Gabor
• Primary Examiner(s): SINES, BRIAN J
• Application Number:
  US10/677,684
• Time in Patent Office: 1,482 Days
• Field of Search: 422/50, 422/62, 422/67, 422/68.1, 422/82.01, 422/83, 422/98, 73/10.1, 73/10.2, 436/43, 436/149, 700/1, 700/266, 702/1, 702/19, 702/22, 702/30, 702/31, 702/32, 702/57, 702/23
• US Class Current: 702/22
• CPC Class Codes: G01N 2291/0215 Mixtures of three or more gases, e.g. air
• CPC Class Codes: G01N 2291/0255 (Bio)chemical reactions, e.g. on biosensors
• CPC Class Codes: G01N 2291/0256 Adsorption, desorption, surface mass change, e.g. on biosensors
• CPC Class Codes: G01N 2291/02818 Density, viscosity
• CPC Class Codes: G01N 2291/0423 Surface waves, e.g. Rayleigh waves, Love waves
• CPC Class Codes: G01N 29/022 Fluid sensors based on microsensors, e.g. quartz crystal-microbalance [QCM], surface acoustic wave [SAW] devices, tuning forks, cantilevers, flexural plate wave [FPW] devices microdevices per se B81B
• CPC Class Codes: G01N 29/036 by measuring frequency or resonance of acoustic waves
• CPC Class Codes: G01N 29/42 by frequency filtering or by tuning to resonant frequency
• CPC Class Codes: G01N 29/46 by spectral analysis, e.g. Fourier analysis or wavelet analysis spectral signal processing per se G06F17/14
• CPC Class Codes: Y10T 436/11 Automated chemical analysis

Abstract

A system and method of fluctuation enhanced gas-sensing using SAW devices includes processes for improved chemical analyte detection, identification, and quantification through the measurement and spectral analysis of frequency fluctuations in the instantaneous frequency of a chemical sensor arranged to produce an oscillatory output signal when exposed to chemical substances. The system and method may use a chemical sensor, such as a surface acoustic wave (SAW) device. The spectral analysis produces the power spectral density of the frequency fluctuations, which are represented as a pattern that includes information about the analyte(s) such as, total adsorbed gas mass and diffusion coefficients. The diffusion coefficients may then be used to determine the number of molecule types and/or the concentration of each.

Claims

1. A method of analyzing a chemical analyte, said method comprising the steps of:

generating a fluctuation output signal in response to a plurality of frequency fluctuations in an oscillatory output signal of a SAW device;

transforming said fluctuation output signal into a power spectral density (PSD) signal, representative of the power spectral density of said frequency fluctuations;

generating a diffusion coefficient signal in response to said power spectral density signal, representative of a diffusion coefficient of said analyte; and

generating an analyte output signal that identifies a characteristic of said analyte if said diffusion coefficient signal corresponds to a characteristic of a known analyte.

2. The method as in claim 1, wherein said analyte output signal represents a characteristic of said analyte selected from the group consisting of:

identification and concentration.

3. The method as in claim 1, wherein said analyte output signal is representative of a number of molecules for each of a plurality of molecule types in said chemical analyte and is responsive to the function S(f)=N₁S(f,D₁)+N₂S(f,D₂)+ . . . N_nS(f,D_n), where S(f) is the total power spectral density, n is the number of molecule types, Nn is the number of molecules of molecule type n, Dn is the diffusion coefficient of said molecule type n, and S(f,Dn) is the power spectral density determined by each said diffusion coefficient.

4. The method as in claim 1, wherein said generating a diffusion coefficient signal step further includes the step of comparing said power spectral density signal to a calculated power spectral density S(ω

), represented substantially by the equation;

$S<br><br>\left(\mathrm{\omega <br><br>}\right)=N_{\mathrm{tot}}·<br><br>{\frac{K·<br><br>L^2}{D·<br><br>{\mathrm{\Theta <br><br>}}^3}<br><br>\left[1-\left(\cos <br><br><br><br>\mathrm{\Theta <br><br>}+\sin <br><br><br><br>\mathrm{\Theta <br><br>}\right)·<br><br>{\mathrm{e<br><br>}}^{-\mathrm{\Theta <br><br>}}\right]}^2,$

where N_tot is the total number of gas molecules, K is a constant characterizing a time average of a frequency shift due to a single molecule, L is a length of a gas-sensing region of said SAW device, ω

is the angular frequency, D is said diffusion coefficient, and

$\mathrm{\Theta <br><br>}=L·<br><br>\sqrt{\frac{\mathrm{\omega <br><br>}}{2·<br><br>D}}.$

5. A chemical sensor system comprising:

a chemical sensor that produces an oscillatory output signal when exposed to a chemical analyte;

measurement means for measuring a plurality of frequency fluctuations of said oscillatory output signal;

PSD means, coupled to said measurement means, for generating a power spectral density signal representative of the power spectral density (PSD) of said plurality of frequency fluctuations;

diffusion coefficient means, coupled to said PSD means, for generating a diffusion coefficient signal representative of the diffusion coefficient of said chemical analyte; and

decision means, coupled to said diffusion coefficient means, for generating an analyte output signal that identifies a characteristic of said chemical analyte if said diffusion coefficient signal corresponds to a characteristic of a known analyte.

6. The chemical sensor system as in claim 5, wherein said analyte output signal represents a characteristic of said analyte selected from the group consisting of:

identification and concentration.

7. The chemical sensor system as in claim 5, wherein said chemical sensor is a Surface Acoustic Wave (SAW) device having a total length l_tot.

8. The chemical sensor system as in claim 7, wherein said SAW device comprises:

a chemical sensing region; and

first and second electrode pairs coupled to opposing ends of said chemical sensing region.

9. The chemical sensor system as in claim 5, wherein said chemical sensor further comprises a bandpass filter for selecting a single oscillatory mode.

10. The chemical sensor system as in claim 5, wherein said measurement means comprises a frequency fluctuation counter.

11. The chemical sensor system as in claim 5, wherein said PSD means comprises a fast Fourier transformation spectrum analyzer.

12. The chemical sensor system as in claim 5, wherein said decision means comprises a pattern recognizer for correlating patterns in said power spectral density to a characteristic of known chemicals.

13. A computer program product for use with an chemical sensor system including a chemical sensor arranged to produce an oscillatory output signal when exposed to a chemical analyte, said computer program product comprising:

a machine-readable recording medium;

a first instruction means, recorded on said recording medium, for directing said chemical sensor system to generate a fluctuation output signal in response to a plurality of frequency fluctuations in said oscillatory output signal;

a second instruction means, recorded on said recording medium, for directing said chemical sensor system to generate a power spectral density signal representative of the power spectral density (PSD) of said plurality of frequency fluctuations in response to said fluctuation output signal;

a third instruction means, recorded on said recording medium, for directing said chemical sensor system to generate a diffusion coefficient signal representative of the diffusion coefficient of said chemical analyte, responsive to said power spectral density signal; and

a fourth instruction means, recorded on said recording medium, for directing said chemical sensor system to generate an analyte output signal that identifies a characteristic of said chemical analyte if said diffusion coefficient signal corresponds to a characteristic of a known analyte.

14. The computer program product as in claim 13, wherein said analyte output signal is representative of a characteristic of said analyte selected from the group consisting of:

identification and concentration.

15. The computer program product as in claim 13, wherein said chemical sensor is a Surface Acoustic Wave (SAW) device.

16. The computer program product as in claim 13, further comprising:

a fifth instruction means, recorded on said recording medium, for directing said chemical sensor system to correlate patterns in said power spectral density to a characteristic of known chemicals.

17. A method of analyzing a chemical analyte, said method comprising the steps of:

generating a surface acoustic wave across a surface of a structure;

transducing said surface acoustic wave into a oscillatory output signal;

generating a fluctuation output signal in response to a plurality frequency fluctuations in said oscillatory output signal;

transforming said fluctuation output signal into a power spectral density (PSD) signal, representative of the power spectral density of said frequency fluctuations;

generating a diffusion coefficient signal in response to said power spectral density signal, representative of a diffusion coefficient of said analyte; and

generating an analyte output signal that identifies a characteristic of said analyte if said diffusion coefficient signal corresponds to a characteristic of a known analyte.

18. The method as in claim 17, wherein said analyte output signal represents a characteristic of said analyte selected from the group consisting of:

identification and concentration.

19. The method as in claim 17, wherein said analyte output signal is representative of a number of molecules for each of a plurality of molecule types in said chemical analyte and is responsive to the function S(f)=N₁S(f,D₁)+N₂S(f,D₂)+ . . . N_nS(f,D_n), where S(f) is the total power spectral density, n is the number of molecule types, Nn is the number of molecules of molecule type n, D is the diffusion coefficient of said SAW device with respect to molecule type n, and S(f,Dn) is the power spectral density determined by each said diffusion coefficient.

20. The method as in claim 17, wherein said generating a diffusion coefficient signal step further includes the step of comparing said power spectral density signal to a calculated power spectral density S(ω

), represented substantially by the equation;

$S<br><br>\left(\mathrm{\omega <br><br>}\right)=N_{\mathrm{tot}}·<br><br>{\frac{K·<br><br>L^2}{D·<br><br>{\mathrm{\Theta <br><br>}}^3}<br><br>\left[1-\left(\cos <br><br><br><br>\mathrm{\Theta <br><br>}+\sin <br><br><br><br>\mathrm{\Theta <br><br>}\right)·<br><br>{\mathrm{e<br><br>}}^{-\mathrm{\Theta <br><br>}}\right]}^2,$

where N_tot is the total number of gas molecules, K is a constant characterizing a time average of a frequency shift due to a single molecule, L is a length of a gas-sensing region of said SAW device, ω

is the angular frequency, D is said diffusion coefficient, and

$\mathrm{\Theta <br><br>}=L·<br><br>\sqrt{\frac{\mathrm{\omega <br><br>}}{2·<br><br>D}}·<br><br>$