Method for classifying scientific materials such as silicate materials, polymer materials and/or nanomaterials

• US 7,715,002 B2
• Filed: 01/23/2007
• Issued: 05/11/2010
• Est. Priority Date: 01/23/2007
• Status: Expired due to Fees

• Current Assignee: Bionorica AG
• Original Assignee: Bionorica AG
• Inventors: Popp, Michael A., Bonn, Guenther, Huck, Christian W.
• Primary Examiner(s): Evans; F. L
• Application Number:
  US11/626,321
• Publication Number:
  US 20080177481A1
• Time in Patent Office: 1,204 Days
• Field of Search: 356/319, 356/323, 356/325
• US Class Current: 356/300
• 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 selected from the group consisting of silicate material, a polymer material and a nanomaterial, comprising the following steps:

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

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

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

d) numerical-mathematical processing spectral data of the material specimen to determine at least one characteristic feature of the material specimen selected from particle size, specific surface area, derivatization, pore porosity, pore size and pore diameter and in the case of polymers the characteristic feature may additionally be one of degree of polymerization, molar mass distribution, crystallinity, thermal phase transitions, branching, stereoregularity and tacticity.

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.

7. The method according to claim 1, wherein the material specimens are thermostated to a maintain a constant temperature for the measurement.

2. A method for characterizing a scientific material selected from the group consisting of silicate material, a polymer material and a nanomaterial, comprising the following steps:

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

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

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 at least one characteristic feature of the material specimen,

wherein the silicate material is a silica gel, the polymer material is a poly(styrene/divinylbenzene) (PS/DVB) or a poly(glycidylmethacrylate/divinylbenzene) and the nanomaterial is a derivatized nanomaterial.

3. The method according to claim 2, wherein the characteristic features of the material specimen determined by numerical-mathematical processing are particle size, specific surface area, derivatization, pore porosity, pore size and/or pore diameter and in the case of polymers the characteristic feature may additionally be one of degree of polymerization, molar mass distribution, crystallinity, thermal phase transitions, branching, stereoregularity and tacticity.

4. A method for characterizing a scientific material selected from the group consisting of silicate material, a polymer material and a nanomaterial, comprising the following steps:

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

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

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 at least one characteristic feature of the material specimen,

wherein the predetermined wavelength range is in the wavelength range of approximately 1,000 to 2,500 nm (4,500-14,186 cm<sup>−

1</sup>).

8. A method for characterizing a scientific material selected from the group consisting of silicate material, a polymer material and a nanomaterial, comprising the following steps:

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

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

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 at least one characteristic feature of the material specimen,

wherein the numerical-mathematical processing comprises a data reduction, by at least one of;

normalization, smoothing, 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), and partial least squares regression (PLS).

9. A method for characterizing a scientific silicate material, a polymer material or a 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, the polymer material is a poly(styrene/divinylbenzene)(PS/DVB) or a poly(glycidyl-methacrylate/divinylbenzene) and the nanomaterial is a derivatized nanomaterial.

11. The method according to claim 9, wherein the characteristics of the individual material classes determined by numerical-mathematical processing is at least one of:

particle size, particle surface, pore diameter, pore volume, derivatization and pore porosity and in the case of polymers the characteristic feature may additionally be one of degree of polymerization, molar mass distribution, crystallinity, thermal phase transitions, branching, stereoregularity and tacticity.

12. The method according to claim 9, wherein the wavelength is in the wavelength range of approximately 1,000 to 2,500 nm (4,500-14,286 cm⁻

1).

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.

15. The method according to claim 9, wherein the material specimens are thermostated to maintain a constant temperature for the measurement.

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

normalization, smoothing, 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), and partial least squares regression (PLS).

17. The method according to claim 9, wherein the correlation of the numerically-mathematically processed spectral data is by at least one multivariate method selected from principal component analysis (PCA) and/or a smoothing, and/or at least one series expansion and/or artificial neuronal network algorithm, Dynamic Learning Vector Quantization (DLVQ-algorithm), Radial Basis Functions (RBF-networks).

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.