CARBON NANOTUBE STRUCTURES IN SENSOR APPARATUSES FOR ANALYZING BIOMARKERS IN BREATH SAMPLES

• US 20110098591A1
• Filed: 05/27/2009
• Published: 04/28/2011
• Est. Priority Date: 05/29/2008
• Status: Active Grant

• Current Assignee: TECHNION RESEARCH AND DEVELOPMENT FOUNDATION, LTD.
• Original Assignee: TECHNION RESEARCH AND DEVELOPMENT FOUNDATION, LTD.
• Inventors: Gang, Peng, Haick, Hossam
• Granted Patent: US 8,366,630 B2
• Time in Patent Office: Days
• US Class Current: 600/532
• 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: G01N 2033/4975 other than oxygen, carbon dioxide or alcohol, e.g. organic vapours
• CPC Class Codes: G01N 27/02 by investigating impedance
• CPC Class Codes: G01N 27/04 by investigating resistance
• CPC Class Codes: G01N 27/22 by investigating capacitance
• CPC Class Codes: G01N 27/414 Ion-sensitive or chemical field-effect transistors, i.e. ISFETS or CHEMFETS
• CPC Class Codes: G01N 33/0034 comprising neural networks or related mathematical techniques
• CPC Class Codes: G01N 33/0047 Organic compounds
• CPC Class Codes: G01N 33/497 of gaseous biological material, e.g. breath

Abstract

The present invention provides a system for measuring biomarker analytes indicative of various diseases comprising an array of sensors sensitive to volatile organic compounds. Particularly, the system is composed of a random network of single-walled carbon nanotubes (SWCNTs) coated with non-polar small organic molecules in conjunction with learning and pattern recognition algorithms. Methods of discriminating between breath samples of healthy individuals and of lung cancer patients are disclosed.

Claims

1. A system for detecting volatile organic compounds derived from a breath sample, the system comprising:

(a) an apparatus comprising an array of chemically sensitive sensors of single walled carbon nanotubes coated with non-polar small organic molecules, and

(b) a processing unit comprising a learning and pattern recognition analyzer wherein the learning and pattern recognition analyzer receives sensor output signals and compares them to stored data.

2. The system according to claim 1 further comprising a breath collector, wherein the breath collector comprises at least one of a unit for concentrating breath analytes and a dehumidifying unit.

4. The system according to claim 1, wherein the single walled carbon nanotubes are organized in a random network configuration;

or wherein the single walled carbon nanotubes have diameters ranging from about 0.9 nanometer to about 5 nanometers, and lengths ranging from about 1 micrometer to about 50 micrometers.

6. The system according to claim 1, wherein the non-polar small organic molecules comprise functional groups selected from unsubstituted or substituted alkyl, cycloalkyl, alkenyl, alkynyl, aryl, heterocyclyl, heteroaryl, alkylaryl, alkylalkenyl, alkylalkynyl, alkylcycloalkyl, alkylheterocyclyl and alkylheteroaryl groups;

combinations and derivatives thereof.

7. The system according to claim 6, wherein the functional groups are substituted by at least one of a carboxyl, an acyl, an amido, an ester, a cyano, a nitro, an azido, a halogen, a hydroxy or a haloalkyl moiety.

8. The system according to claim 1, wherein the small organic molecules are selected from the group consisting of propyl gallate, anthracene, tetracosanoic acid, tricosane, 3-methyl-2-phenyl valeric acid, tris(hydroxymethyl)nitro-methane, tetracosane, dioctyl phthalate, tetracosanoic acid, 1,2,5,6,9,10-hexabromo-cyclododecane, pentadecane, hexa-peri-hexabenzocoronene (HBC) derivatives selected from the group consisting of HBC—

C_6,2, HBC—

C_10,6 HBC—

C_14,10, and HBC—

C₁₂; and

combinations thereof.

10. The system according to claim 1, wherein the sensors are configured in a form selected from the group consisting of capacitive sensors, resistive sensors, impedance sensors, and field effect transistor sensors.

11. The system according to claim 1, wherein the learning and pattern recognition analyzer comprises at least one algorithm selected from the group consisting of artificial neural network algorithms, principal component analysis (PCA), 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.

3. (canceled)

5. (canceled)

9. (canceled)

12. (canceled)

13. A method of determining at least one of the composition and concentration of volatile organic compounds in a breath sample, comprising the steps of:

(a) providing a system comprising an apparatus comprising an array of chemically sensitive sensors of single walled carbon nanotubes coated with non-polar small organic molecules, and a processing unit comprising learning and pattern recognition analyzer wherein the learning and pattern recognition analyzer receives sensor output signals from the apparatus and compares them to stored data,

(b) exposing the sensor array of the apparatus to the sample, and

(c) using pattern recognition algorithms to detect the presence of the volatile organic compounds in the sample.

14. The method according to claim 13 further comprising the step of collecting the sample into a breath collector prior to step (b), wherein the breath collector comprises at least one of a unit for concentrating breath analytes and a dehumidifying unit.

16. The method according to claim 13, wherein the single walled carbon nanotubes are organized in a random network configuration;

or wherein the single walled carbon nanotubes have diameters ranging from about 0.9 nanometer to about 5 nanometers, and lengths ranging from about 1 micrometer to about 50 micrometers.

18. The method according to claim 13, wherein the non-polar small organic molecules comprise functional groups selected from unsubstituted or substituted alkyl, cycloalkyl, alkenyl, alkynyl, aryl, heterocyclyl, heteroaryl, alkylaryl, alkylalkenyl, alkylalkynyl, alkylcycloalkyl, alkylheterocyclyl and alkylheteroaryl groups;

combinations and derivatives thereof.

19. The method according to claim 18, wherein the functional groups are substituted by at least one of a carboxyl, an acyl, an amido, an ester, a cyano, a nitro, an azido, a halogen, a hydroxy or a haloalkyl moiety.

20. The method according to claim 13, wherein the small organic molecules are selected from the group consisting of propyl gallate, anthracene, tetracosanoic acid, tricosane, 3-methyl-2-phenyl valeric acid, tris(hydroxymethyl)nitro-methane, tetracosane, dioctyl phthalate, tetracosanoic acid, 1,2,5,6,9,10-hexabromo-cyclododecane, pentadecane, hexa-peri-hexabenzocoronene (HBC) derivatives selected from the group consisting of HBC—

C_6,2, HBC—

C_10,6, HBC—

C_14,10, and HBC—

C₁₂; and

combinations thereof.

22. The method according to claim 13, wherein the sensors are configured in a form selected from the group consisting of capacitive sensors, resistive sensors, impedance sensors, and field effect transistor sensors.

23. The method according to claim 13, wherein the learning and pattern analyzer comprises at least one algorithm selected from the group consisting of artificial neural network algorithms, principal component analysis (PCA), 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.

15. (canceled)

17. (canceled)

21. (canceled)

24. (canceled)

25. A method for diagnosing a disease from a breath sample of a subject, comprising:

(a) providing a system comprising an apparatus comprising an array of chemically sensitive sensors of single walled carbon nanotubes coated with non-polar small organic molecules, and a processing unit comprising learning and pattern recognition analyzer wherein the learning and pattern recognition analyzer receives sensor output signals from the apparatus and compares them to stored data,

(b) exposing the sensor array of the apparatus to the sample, and

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

26. The method according to claim 25 further comprising the step of collecting the sample into a breath collector prior to step (b), wherein the breath collector comprises at least one of a unit for concentrating breath analytes and a dehumidifying unit.

28. The method according to claim 25, wherein the single walled carbon nanotubes are organized in a random network configuration;

or wherein the single walled carbon nanotubes have diameters ranging from about 0.9 nanometer to about 5 nanometers, and lengths ranging from about 1 micrometer to about 50 micrometers.

30. The method according to claim 25, wherein the non-polar small organic molecules comprise functional groups selected from unsubstituted or substituted alkyl, cycloalkyl, alkenyl, alkynyl, aryl, heterocyclyl, heteroaryl, alkylaryl, alkylalkenyl, alkylalkynyl, alkylcycloalkyl, alkylheterocyclyl and alkylheteroaryl groups;

combinations and derivatives thereof.

31. The method according to claim 30, wherein the functional groups are substituted by at least one of a carboxyl, an acyl, an amido, an ester, a cyano, a nitro, an azido, a halogen, a hydroxy or a haloalkyl moiety.

32. The method according to claim 25, wherein the non-polar small organic molecules are selected from the group consisting of propyl gallate, anthracene, tetracosanoic acid, tricosane, 3-methyl-2-phenyl valeric acid, tris(hydroxymethyl)nitro-methane, tetracosane, dioctyl phthalate, tetracosanoic acid, 1,2,5,6,9,10-hexabromo-cyclododecane, pentadecane, hexa-peri-hexabenzocoronene (HBC) derivatives selected from the group consisting of HBC—

C_6,2, HBC—

C_10,6, HBC—

C_14,10, and HBC—

C₁₂; and

combinations thereof.

34. The method according to claim 25, wherein the sensors are configured in a form selected from the group consisting of capacitive sensors, resistive sensors, impedance sensors, and field effect transistor sensors.

35. The method according to claim 25, wherein the learning and pattern recognition analyzer comprises at least one algorithm selected from the group consisting of artificial neural network algorithms, principal component analysis (PCA), 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.

37. The method according to claim 25, wherein the disease is selected from the group consisting of cancer, arthritis, arthritic conditions, atherosclerosis, kidney diseases, type 2 diabetes, chronic obstructive pulmonary disease (COPD), age related macula degeneration (AMD), neurodegenerative diseases, Alzheimer'"'"'s disease, Parkinson'"'"'s disease and amyotrophic lateral sclerosis (ALS).

38. The method according to claim 37, wherein the disease is cancer selected from the group consisting of lung cancer, prostate cancer, breast cancer, skin cancer, colon cancer, pancreatic cancer, lymphoma, leukemia, head and neck cancer, kidney cancer, ovarian cancer, bone cancer, liver cancer and thyroid cancer.

27. (canceled)

29. (canceled)

33. (canceled)

36. (canceled)

39. (canceled)