Estimation of Average Molecular Structural Parameters of Heavy Hydrocarbons Using Infrared Spectroscopy
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- FUMOTO Eri
- Energy Process Research Institute, National Institute of Advanced Industrial Science and Technology (AIST)
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- SATO Shinya
- Energy Process Research Institute, National Institute of Advanced Industrial Science and Technology (AIST)
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- MORIMOTO Masato
- Energy Process Research Institute, National Institute of Advanced Industrial Science and Technology (AIST)
Bibliographic Information
- Other Title
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- 赤外分光法を用いた重質炭化水素の平均分子構造パラメーターの推算
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Abstract
<p>Heavy hydrocarbons, such as coal tars and petroleum residues are inexpensive raw materials for the manufacture of carbon fiber. Average molecular structural analysis is a useful technique to derive average structural models of these heavy hydrocarbons. These models are conventionally determined based on elemental composition, average molecular weight, and nuclear magnetic resonance (NMR) spectroscopy. However, laboratory preparations sometimes fail to provide sufficient sample quantities for NMR analysis, and some samples may not dissolve in solvents. We have developed an alternative approach for estimation of the average molecular structural parameters of heavy hydrocarbons using infrared (IR) spectroscopy which requires only small sample quantities (1-2 mg) and can accommodate insoluble materials. The equations that predict the parameters, i.e., the carbon aromaticity, molar ratios of terminal methyl groups to aromatic carbon, molar ratios of methyl groups to the sum of the methyl and methylene groups, and oxygen contents of ketones and carboxylic acids, were determined using IR spectra and hydrogen-to-carbon atomic ratios of various samples such as coal, asphaltene, and/or model compounds.</p>
Journal
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- Journal of the Japan Petroleum Institute
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Journal of the Japan Petroleum Institute 66 (6), 231-237, 2023-11-01
The Japan Petroleum Institute
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Keywords
Details 詳細情報について
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- CRID
- 1390016504861709568
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- NII Book ID
- AA11590615
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- ISSN
- 1349273X
- 13468804
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- NDL BIB ID
- 033160973
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
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- Abstract License Flag
- Disallowed