METHOD FOR CLASSIFYING SCIENTIFIC MATERIALS SUCH AS SILICATE MATERIALS, POLYMER MATERIALS AND/OR NANOMATERIALS

• US 20080177481A1
• Filed: 01/23/2007
• Published: 07/24/2008
• Est. Priority Date: 01/23/2007
• Status: Active Grant

• Current Assignee: Bionorica AG
• Original Assignee: Bionorica AG
• Inventors: Popp, Michael A., Bonn, Guenther, Huck, Christian W.
• Granted Patent: US 7,715,002 B2
• Time in Patent Office: Days
• US Class Current: 702/29
• CPC Class Codes: G01N 21/3563 for analysing solids; Preparation of samples therefor
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/1293 resolving multicomponent spectra

Abstract

A method is provided for characterizing a scientific material, such as a silicate material, a polymer material and/or nanomaterial. The method can include the steps of irradiating a measuring light of a predetermined wavelength range into material specimens, recording the measuring light reflected and/or reemitted by the material specimens, determining a ratio depending on the wave lengths of irradiated to detected measuring light (spectrum), and numerical-mathematical processing of spectral data of single material specimens for determining the characteristic features of the material specimens.

Claims

1. A method for characterizing a scientific material such as a silicate material, a polymer material and/or nanomaterial comprising the following steps:

a) irradiating a measuring light of a predetermined wavelength range into material specimens,

b) recording of the measuring light reflected and/or reemitted by the material specimens,

c) determining a ratio depending on the wave lengths of irradiated to detected measuring light (spectrum), and

d) numerical-mathematical processing of spectral data of each material specimen for determining the characteristic features of the material specimens.

2. The method according to claim 1, wherein the silicate material is a silica gel, preferably a silica gel for chromatographic separation procedures and/or the polymer material is a poly(styrene/divinylbenzene) (PS/DVB) or a poly(glycidylmethacrylate/divinylbenzene) and/or the nanomaterial is a derivatized nanomaterial, in particular a carbon nanotube, fullerenes or nano fibers wherein the derivatization is preferably with C18, C8, C4, phenyl, IMAC or with ion-exchange affinity conferring groups.

3. The method according to claim 1, wherein the characteristic features of the material specimen are particle size, specific surface area, derivatization, pore porosity, pore size and/or pore diameter.

4. The method according to claim 1, wherein the predetermined wavelength range is in the NIR-VIS wavelength range, in particular in the range of approximately 400 to 2,500 nm (4,000-14,186 cm⁻

1), preferably in the range of approximately 1,000 to 2,200 nm (4,500 to 10,000 cm<sup>−

1</sup>).

5. The method according to claim 1, wherein the measuring light is conducted to the material specimens via a light conductor and/or is absorbed by the material specimens.

6. The method according to claim 1, wherein a layer of the material specimens has an optical thickness in the range of approximately 0.2 to 5 mm, preferably in the range of approximately 0.5 to approximately 3 mm.

7. The method according to claim 1, wherein the material specimens are thermostated, preferably to approximately 23°

C., for the measurement.

8. The method according to claim 1, wherein the numerical-mathematical processing comprises a data reduction, by:

normalization, smoothing, in particular according to Savitzky Golay, 1^st derivation, 2^nd derivation, multiplicative scatter correction, reciprocal value, square, mean centering, Kubelka Munc Transformation, absorption, baseline correction, addition of a constant, shift negative to zero, principal component analysis (PCA), principal component regression (PCR), partial least squares regression (PLS).

9. A method for characterizing a scientific material such as a silicate material, a polymer material and/or nanomaterial comprising the following steps:

a) providing a plurality of material classes each with a plurality of calibration and material specimens per class with a plurality of known class-characteristics;

b) irradiating a measuring light of a predetermined wavelength range into the material specimen;

c) recording of the measuring light passed through, reflected, reemitted and/or scattered by the material specimens;

d) determining a ratio depending on the wave lengths of irradiated to detected measuring light (spectrum) for each material specimen of a class;

e) numerical-mathematical processing of spectral data of each material specimen;

f) correlating the spectra of a majority of material specimens with a predetermined material class;

g) creating a database with the processed spectral data with different material classes based on the material specimens measured of individual classes for calibration of a class correlation;

h) repeating steps b) to e) at least once with at least one material sample with at least partially unknown characteristics; and

i) determining the material classes to which the unknown material specimen is related, by means of a class correlation of the spectra measured using the created calibration database from step g).

10. The method according to claim 9, wherein the silicate material is a silica gel, preferably a silica gel for chromatographic separation procedures and/or the polymer material is a poly(styrene/divinylbenzene) (PS/DVB) or a poly(glycidylmethacrylate/divinylbenzene) and/or the nanomaterial is a derivatized nanomaterial, in particular a carbon nanotube, fullerenes or nano fibers wherein the derivatization is preferably with C18, C8, C4, phenyl, IMAC or with ion-exchange affinity conferring groups.

11. The method according to claim 9, wherein the characteristics of the individual material classes at least partially comprises the following group:

particle size, particle surface, pore diameter, pore volume, derivatization and/or pore porosity.

12. The method according to claim 9, wherein the wavelength range is in the NIR-VIS wavelength range, in particular in the range of approximately 700 to 2,500 nm (4,000-14,286 cm⁻

1), preferably in the range of approximately 1,000 to 2,200 nm (4,500 to 10,000 cm<sup>−

1</sup>).

13. The method according to claim 9, wherein the measuring light is conducted via a light conductor to the material specimens and/or is absorbed by same.

14. The method according to claim 9, wherein a layer of the material specimens has an optical thickness in the range of approximately 0.2 to 5 mm, preferably in the range of approximately 0.5 to approximately 3 mm.

15. The method according to claim 9, wherein the material specimens are thermostated, preferably to approximately 23°

C., for the measurement.

16. The method according to claim 9, wherein the numerical-mathematical processing comprises a data reduction, in particular by:

normalization, smoothing, in particular according to Savitzky Golay, 1st derivation, 2nd derivation, multiplicative scatter correction, reciprocal value, square, mean centering, Kubelka Munc Transformation, absorption, baseline correction, addition of a constant, shift negative to zero, principal component analysis (PCA), principal component regression (PCR), partial least squares regression (PLS).

17. The method according to claim 9, wherein the correlation of the numerically-mathematically processed spectral data comprises multivariate methods, preferably a principal component analysis (PCA) and/or a smoothing, in particular according to Savitzky Golay, and/or at least one series expansion, in particular a Taylor Series Expansion and/or artificial neuronal network algorithm, in particular back propagation networks, Dynamic Learning Vector Quantization (DLVQ-algorithm), Radial Basis Functions (RBF-networks), in particular with the Dynamic Decay Adjustment Algorithm (DDA-Algorithm) trained RBF-networks (RBF-DDA-network).

18. The method according to claim 9, wherein the correlation is caused by formation of clusters.

19. The method according to claim 18, wherein tolerance areas of individual clusters are adjustable during calibration.

20. The method according to claim 9, wherein approximately 3 to 20 spectral-scans/material specimen are recorded.

21. The method according to claim 9, wherein approximately 10 material specimen per class characteristic are recorded for calibration.

22. The method according to claim 9, wherein approximately 70% of all material specimens measured per class are used for calibration and approximately 30% are used for validating the procedure.

23. The method according to claim 9, wherein classification results are shown as 3D-plot.