Characterization of kerogen using solid-state nuclear magnetic resonance spectroscopy: A review

Cao, Xiaoyan; Yang, John; Mao, Jingdong

doi:10.1016/j.coal.2011.12.001

Cited by 46 publications

(41 citation statements)

References 73 publications

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“…Excellent reviews of early applications of NMR techniques to solid fuels can be found elsewhere [30,66]. Recent work has also summarized structural studies of oil shale and kerogen using solid-state NMR [287], and has discussed, in a historical perspective, how solidstate NMR has been used for coal characterization [11]. This section is intended to summarize the various advanced NMR techniques that have been employed to elucidate structures of fossil fuels.…”

Section: Fossil Fuelsmentioning

confidence: 99%

“…More recently, Mao and Schmidt-Rohr developed and applied a systematic approach to characterize fossil solids such as kerogen and coal, which includes identification and quantification of specific functional groups, their connectivities or proximities, heterogeneities and domains [178,287,[290][291][292]. In addition to spectral editing of CH-only, CH 2 -only, and CH 3 and nonprotonated carbons, a CSA filtering technique can be used to selectively suppress the signal of sp 2 aromatic carbons in the 90-120 ppm region and retain the signal from sp 3 carbons.…”

Section: Fossil Fuelsmentioning

confidence: 99%

See 1 more Smart Citation

Advanced solid-state NMR spectroscopy of natural organic matter

Mao

Cao

Olk

et al. 2017

Progress in Nuclear Magnetic Resonance Spectroscopy

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127

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Solid-state NMR is essential for the characterization of natural organic matter (NOM) and is gaining importance in geosciences and environmental sciences. This review is intended to highlight advanced solid-state NMR techniques, especially a systematic approach to NOM characterization, and their applications to the study of NOM. We discuss some basics of how to acquire high-quality and quantitative solid-state C NMR spectra, and address some common technical mistakes that lead to unreliable spectra of NOM. The identification of specific functional groups in NOM, primarily based onC spectral-editing techniques, is described and the theoretical background of some recently-developed spectral-editing techniques is provided. Applications of solid-state NMR to investigating nitrogen (N) in NOM are described, focusing on limitations of the widely used N CP/MAS experiment and the potential of improved advanced NMR techniques for characterizing N forms in NOM. Then techniques used for identifying proximities, heterogeneities and domains are reviewed, and some examples provided. In addition, NMR techniques for studying segmental dynamics in NOM are reviewed. We also briefly discuss applications of solid-state NMR to NOM from various sources, including soil organic matter, aquatic organic matter, organic matter in atmospheric particulate matter, carbonaceous meteoritic organic matter, and fossil fuels. Finally, examples of NMR-based structural models and an outlook are provided.

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Section: Fossil Fuelsmentioning

confidence: 99%

Section: Fossil Fuelsmentioning

confidence: 99%

Advanced solid-state NMR spectroscopy of natural organic matter

Mao

Cao

Olk

et al. 2017

Progress in Nuclear Magnetic Resonance Spectroscopy

Self Cite

127

View full text Add to dashboard Cite

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“…These generalities are inherently useful in rapid appraisals of source facies on regional scales. However, understanding structure-catagenesis relationships for predicting hydrocarbon yields requires statistical models that describe the relationships between pyrolysis assay observations and kerogen molecular structure (Cao et al, 2013). Prior correlations between structural parameters as determined by CPMAS 13 C NMR and hydrocarbon yields, such as those established in Qin et al (1991), could suffer from inaccuracy introduced by the broad signal from aliphatic C, characteristic of aquatic kerogens (Cao et al, 2013).…”

Section: -Nmr-based Kerogen Typingmentioning

confidence: 99%

“…Solid-state spectroscopy techniques, such as, 13 C nuclear magnetic resonance (NMR) are powerful for elucidating the structure of kerogens without bond cleavage (Cao et al, 2013).…”

Section: -Introductionmentioning

confidence: 99%

Organic structural properties of kerogen as predictors of source rock type and hydrocarbon potential

Longbottom

Hockaday

Boling

et al. 2016

Fuel

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This study improves upon previously identified correlations between the chemical structure of kerogen and potential hydrocarbon (oil and gas) yields assayed by Rock-Eval pyrolysis. We propose a quantitative structure-catagenesis relationship that predicts the hydrocarbon generation potential of source rocks and of lacustrine, marine, and terrestrial origin (types I, II, and humic coals). We used one-dimensional solid-state 13 C Nuclear Magnetic Resonance (13 C NMR) spectroscopy with 1 H spectral editing to determine the abundance of carbon functional groups, including non-protonated and mobile groups. An NMR-based van Krevelen analysis readily separated the kerogen types. Single regression matrices of NMR-based structure parameters against Rock-Eval hydrocarbon yield revealed distinct dynamics of the kerogen types upon pyrolysis. Multiple regression showed that alkyl, oxygen-substituted alkyl, and carbonyl groups were strong contributors to hydrocarbon production, while oxygen-substituted aromatic carbons were strongly counterproductive. Catagenetic relationships established for kerogen provide insight into kerogen structure evolution upon pyrolysis, and can more closely constrain the mechanisms of hydrocarbon generation for use in sedimentary basin modelling.

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“…13 C NMR spectroscopy has been utilized for coal research since the late 1970s [8][9][10]. Cao et al worked on chemical structure changes in kerogen from bituminous coal and reviewed the characterization of kerogen using advanced 13 C NMR spectroscopy [11,12]. FTIR spectroscopy was used to determine the absorptivity of aromatic and aliphatic C H bonds [13], aromaticity, and to analyze the carboxyl groups [14].…”

Section: Introductionmentioning

confidence: 99%