CHEMICALLY SENSITIVE FIELD EFFECT TRANSISTORS AND USES THEREOF IN ELECTRONIC NOSE DEVICES

• US 20100198521A1
• Filed: 07/24/2008
• Published: 08/05/2010
• Est. Priority Date: 07/24/2007
• Status: Active Grant

• Current Assignee: TECHNION RESEARCH AND DEVELOPMENT FOUNDATION, LTD.
• Original Assignee: TECHNION RESEARCH AND DEVELOPMENT FOUNDATION, LTD.
• Inventors: Haick, Hossam
• Granted Patent: US 10,011,481 B2
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: B82Y 15/00 Nanotechnology for interacting, sensing or actuating, e.g. quantum dots as markers in protein assays or molecular motors
• CPC Class Codes: B82Y 30/00 Nanotechnology for materials or surface science, e.g. nanocomposites
• CPC Class Codes: G01N 2033/4975 other than oxygen, carbon dioxide or alcohol, e.g. organic vapours
• CPC Class Codes: G01N 27/4146 involving nanosized elements, e.g. nanotubes, nanowires
• CPC Class Codes: G01N 27/4148 Integrated circuits therefor, e.g. fabricated by CMOS processing CMOS processing per se H01L21/82
• CPC Class Codes: G01N 33/0034 comprising neural networks or related mathematical techniques
• CPC Class Codes: G01N 33/54373 involving physiochemical end-point determination, e.g. wave-guides, FETS, gratings
• CPC Class Codes: H01L 29/0843 Source or drain regions of field-effect devices
• CPC Class Codes: H01L 29/772 Field effect transistors
• CPC Class Codes: Y10S 977/70 Nanostructure
• CPC Class Codes: Y10S 977/742 Carbon nanotubes, CNTs

Abstract

The present invention provides an electronic nose device based on chemically sensitive field effect transistors. In particular, the sensors of the electronic nose device are composed of non-oxidized, functionalized silicon nanowires which can detect volatile organic compounds with very high sensitivity. Methods of use in diagnosing diseases including various types of cancer are disclosed.

Claims

1. An electronic device comprising at least one chemically sensitive sensor for the detection of volatile organic compounds, wherein the chemically sensitive sensor comprises field effect transistors comprising non-oxidized, functionalized silicon nanowires, wherein the functional group is other than methyl.

4. The device according to claim 1, wherein said functionalized silicon nanowires comprise surface modified silicon nanowires comprising at least one functional group attached through a direct Si—

C bond.

5. The device according to claim 4, wherein said at least one functional group is selected from the group consisting of alkyl, cycloalkyl, alkenyl, alkynyl, aryl, heterocyclyl, heteroaryl, alkylaryl, alkylalkenyl, alkylalkynyl, alkylcycloalkyl, alkylheterocyclyl and alkylheteroaryl groups;

combinations and derivatives thereof, wherein the alkyl is other than methyl.

6. The device according to claim 5, wherein said at least one functional group is substituted by one or more halogens selected from the group consisting of fluorine, chlorine, bromine, and iodine.

7. The device according to claim 4, wherein said at least one functional group is selected from the group consisting of ethyl, isopropyl, tert-butyl, hexyl, octyl, phenyl, cyclic C₆ hydrocarbonyl, 1,3-cyclohexadienyl, 1,4-cyclohexadienyl, cyclohexyl, H-terminated bicyclo[2.2.2]octyl, Cl-terminated bicyclo[2.2.2]octyl, and combinations thereof.

8. The device according to claim 4, wherein said at least one functional group is selected from the group consisting of 1-pentyl, 1-dodecyl, 2-hexynyl, 1-octenyl, 1-pentenyl, 1-dodecenyl, 1-octadecenyl, cis-2-pentenyl, trans-2-hexenyl, 2,3-dimethyl-2-pentenyl, styrenyl and five-, six-, eight-membered ring derivatives thereof;

and combinations thereof.

9. The device according to claim 4, wherein said at least one functional group is selected from the group consisting of phenylacetylenyl, 1-phenyl-2-(trimethylsilyl) acetylenyl, 1-octynyl, dodec-1-ynyl, 1-trimethylsilyldodec-1-ynyl, pentynyl, diphenylphosphino-acetylenyl, arynyl, diphenyl-phosphinoethynyl, and combinations thereof.

10. The device according to claim 1, wherein the surface of the silicon nanowire is modified with polymer films selected from polypropylene and polynorbornene.

2-3. -3. (canceled)

11. A system comprising:

(i) an electronic device comprising an array of chemically sensitive sensors for the detection of volatile organic compounds, wherein the chemically sensitive sensors comprise field effect transistors comprising non-oxidized, functionalized silicon nanowires, and

(ii) a learning and pattern recognition analyzer, wherein said learning and pattern recognition analyzer receives sensor output signals and compares them to stored data.

14. The system according to claim 11, wherein said functionalized silicon nanowires comprise surface modified silicon nanowires comprising at least one functional group attached through a direct Si—

C bond.

15. The system according to claim 14, wherein said at least one functional group is selected from the group consisting of alkyl, cycloalkyl, alkenyl, alkynyl, aryl, heterocyclyl, heteroaryl, alkylaryl, alkylalkenyl, alkylalkynyl, alkylcycloalkyl, alkylheterocyclyl and alkylheteroaryl groups;

combinations and derivatives thereof.

16. The system according to claim 15, wherein said at least one functional group is substituted by one or more halogens selected from the group consisting of fluorine, chlorine, bromine, and iodine.

17. The system according to claim 14, wherein said at least one functional group is selected from the group consisting of methyl, ethyl, isopropyl, tert-butyl, hexyl, octyl, phenyl, cyclic C₆ hydrocarbonyl, 1,3-cyclohexadienyl, 1,4-cyclohexadienyl, cyclohexyl, H-terminated bicyclo[2.2.2]octyl, Cl-terminated bicyclo[2.2.2]octyl, and combinations thereof.

18. The system according to claim 14, wherein said at least one functional group is selected from the group consisting of 1-pentyl, 1-dodecyl, 2-hexynyl, 1-octenyl, 1-pentenyl, 1-dodecenyl, 1-octadecenyl, cis-2-pentenyl, trans-2-hexenyl, 2,3-dimethyl-2-pentenyl, styrenyl and five-, six-, eight-membered ring derivatives thereof;

and combinations thereof.

19. The system according to claim 14, wherein said at least one functional group is selected from the group consisting of phenylacetylenyl, 1-phenyl-2-(trimethylsilyl) acetylenyl, 1-octynyl, dodec-1-ynyl, 1-trimethylsilyldodec-1-ynyl, pentynyl, diphenylphosphino-acetylenyl, arynyl, diphenyl-phosphinoethynyl, and combinations thereof.

20. The system according to claim 11, wherein the surface of the silicon nanowire is modified with polymer films selected from polypropylene and polynorbornene.

21. The system according to claim 11, wherein the learning and pattern recognition analyzer comprises at least one algorithm selected from the group consisting of principal component analysis (PCA), artificial neural network algorithms, multi-layer perception (MLP), generalized regression neural network (GRNN), fuzzy inference systems (FIS), self-organizing map (SOM), radial bias function (RBF), genetic algorithms (GAS), neuro-fuzzy systems (NFS), adaptive resonance theory (ART), partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), discriminant function analysis (DFA), linear discriminant analysis (LDA), cluster analysis, and nearest neighbor.

12-13. -13. (canceled)

22. (canceled)

23. A method for detecting volatile organic compounds in a sample, comprising the steps of:

i) providing a system comprising an electronic device for detecting volatile organic compounds comprising an array of chemically sensitive sensors, wherein the chemically sensitive sensors comprise field effect transistors comprising non-oxidized, functionalized silicon nanowires, and wherein the system further comprises a learning and pattern recognition analyzer, wherein said learning and pattern recognition analyzer receives sensor output signals from the electronic device and compares them to stored data,

ii) exposing the sensor array of said electronic device to the sample, and

iii) using pattern recognition algorithms to detect the presence of said volatile compounds in the sample.

24. The method according to claim 23, wherein said volatile organic compounds in a sample are at concentrations below one part per million (ppm) or wherein said volatile organic compounds in a sample are at concentrations of less than 100 parts per billion (ppb).

26. The method according to claim 23, wherein said learning and pattern recognition analyzer comprises at least one algorithm selected from the group consisting of principal component analysis (PCA), artificial neural network algorithms, multi-layer perception (MLP), generalized regression neural network (GRNN), fuzzy inference systems (FIS), self-organizing map (SOM), radial bias function (RBF), genetic algorithms (GAS), neuro-fuzzy systems (NFS), adaptive resonance theory (ART), partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), discriminant function analysis (DFA), linear discriminant analysis (LDA), cluster analysis, and nearest neighbor.

25. (canceled)

27. (canceled)

28. A method for diagnosing a disease in a subject, comprising the steps of:

a) providing a system comprising an electronic device for detecting volatile organic compounds comprising an array of chemically sensitive sensors, wherein the chemically sensitive sensors comprise field effect transistors comprising non-oxidized, functionalized silicon nanowires, and wherein the system further comprises a learning and pattern recognition analyzer, wherein said learning and pattern recognition analyzer receives sensor output signals from the electronic device and compares them to stored data,

b) exposing the sensor array of said electronic device to the breath of a subject, or to the headspace of a container in which a bodily fluid of the subject has been deposited, and

c) using pattern recognition algorithms to detest volatile organic compounds in the sample indicative of a disease in the subject.

29. The method according to claim 28, wherein the disease is selected from the group consisting of cancer, oral infections, periodontal diseases, halitosis, ketosis, yeast infections, pneumonia, lung infections, sexually transmitted diseases, vaginitis, nephritis, bilirubin production, renal disease, cardiovascular disease, hypercholesterolemia, gastrointestinal infections, diabetes, and phenylketonuria.

30. The method according to claim 29, wherein the disease is cancer.

31. The method according to claim 28, wherein said bodily fluid is selected from the group consisting of serum, urine, feces, sweat, vaginal discharge, saliva and sperm.

32. The method according to claim 28, wherein said learning and pattern recognition analyzer comprises at least one algorithm selected from the group consisting of principal component analysis (PCA), artificial neural network algorithms, multi-layer perception (MLP), generalized regression neural network (GRNN), fuzzy inference systems (FIS), self-organizing map (SOM), radial bias function (RBF), genetic algorithms (GAS), neuro-fuzzy systems (NFS), adaptive resonance theory (ART), partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), discriminant function analysis (DFA), linear discriminant analysis (LDA), cluster analysis, and nearest neighbor.

33-42. -42. (canceled)