Quantitative NMR Spectroscopy for the Prediction of Base Oil Properties
The performance characteristics of mineral base oils depend largely on their physiochemical properties. These properties in turn are dependent on the type and relative amount of different hydrocarbons present in the system. The physical properties of some molecules are known to be quite different wh...
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| Veröffentlicht in: | Tribology transactions 2000-01, Vol.43 (2), p.245-250 |
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| creator | Adhvaryu, Atanu Perez, Joseph M. Duda, Larry J. |
| description | The performance characteristics of mineral base oils depend largely on their physiochemical properties. These properties in turn are dependent on the type and relative amount of different hydrocarbons present in the system. The physical properties of some molecules are known to be quite different when they are in a mixture and their influence on bulk physical properties of base oils can vary considerably. The evaluation of these properties can involve long procedures, considerable manpower, a large sample and a costly laboratory infrastructure. A rapid method was developed using quantitative
13
C NMR derived structural information on a set of Group 1 type base oils. The molecular level characterization is considered more accurate, requiring less time and test sample to study base oil properties. If such structural data are correlated suitably with the bulk physical properties, they can be used as reliable tools for predictive models. A best subset multi-component regression analysis of the NMR data generated on several base oils are used to develop correlations to predict base oil properties such as API gravity, pour point, aniline point and viscosity. The method and some results are reported.
Presented at the 54th Annual Meeting Las Vegas, Nevada May 23-27, 1999 |
| doi_str_mv | 10.1080/10402000008982335 |
| format | Article |
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13
C NMR derived structural information on a set of Group 1 type base oils. The molecular level characterization is considered more accurate, requiring less time and test sample to study base oil properties. If such structural data are correlated suitably with the bulk physical properties, they can be used as reliable tools for predictive models. A best subset multi-component regression analysis of the NMR data generated on several base oils are used to develop correlations to predict base oil properties such as API gravity, pour point, aniline point and viscosity. The method and some results are reported.
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13
C NMR derived structural information on a set of Group 1 type base oils. The molecular level characterization is considered more accurate, requiring less time and test sample to study base oil properties. If such structural data are correlated suitably with the bulk physical properties, they can be used as reliable tools for predictive models. A best subset multi-component regression analysis of the NMR data generated on several base oils are used to develop correlations to predict base oil properties such as API gravity, pour point, aniline point and viscosity. The method and some results are reported.
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These properties in turn are dependent on the type and relative amount of different hydrocarbons present in the system. The physical properties of some molecules are known to be quite different when they are in a mixture and their influence on bulk physical properties of base oils can vary considerably. The evaluation of these properties can involve long procedures, considerable manpower, a large sample and a costly laboratory infrastructure. A rapid method was developed using quantitative
13
C NMR derived structural information on a set of Group 1 type base oils. The molecular level characterization is considered more accurate, requiring less time and test sample to study base oil properties. If such structural data are correlated suitably with the bulk physical properties, they can be used as reliable tools for predictive models. A best subset multi-component regression analysis of the NMR data generated on several base oils are used to develop correlations to predict base oil properties such as API gravity, pour point, aniline point and viscosity. The method and some results are reported.
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| language | eng |
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| source | Taylor & Francis Journals Complete |
| subjects | Condensed matter: electronic structure, electrical, magnetic, and optical properties Correlation Exact sciences and technology Magnetic resonances and relaxations in condensed matter, mössbauer effect Mineral Base Oil NMR Nmr imaging Nuclear magnetic resonance and relaxation Physics |
| title | Quantitative NMR Spectroscopy for the Prediction of Base Oil Properties |
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