The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Application of quantitative 31P NMR in biomass lignin and biofuel precursors characterization
Abstract
The last decade has seen tremendous growth and interest in renewable energy and fuels aimed primarily at addressing issues of climate change, energy security, and rising energy costs. These efforts coupled with the demand for efficient utilization of biomass place a premium on the detailed analysis of the fundamental chemical structures of biomass, especially in light of the ever-increasing efforts to generate transgenic plants with reduced recalcitrance and altered lignin structure. This review examines the growing application of phosphitylation followed by 31P NMR to quantitatively analyze biomass lignin structures including guaiacyl, syringyl, guaiacyl with carbon substituents at the C5 position, catechol, p–hydroxyphenyl, aliphatic and carboxylic hydroxyl groups. The application of this methodology to provide a rapid analytical tool for lignin/biomass derived bio-oils and biodiesel precursors is also discussed. Utilizing lignin isolated from native and transgenic plants as well as from pretreatment and biological/thermal deconstruction processes, researchers have demonstrated that this technique has unique characterization capabilities which have broad applicability in the biofuels research community.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Features of promising technologies for pretreatment of lignocellulosic biomass
MOSIER, N. (2005) · Bioresource Technology · doi:10.1016/j.biortech.2004.06.025 · Cited by 6,251
Pretreatment: the key to unlocking low‐cost cellulosic ethanol
Yang, B., & Wyman, C. E. (2008) · Biofuels, Bioproducts and Biorefining · doi:10.1002/bbb.49 · Cited by 1,524
Ethanol for a Sustainable Energy Future
Goldemberg, J. (2007) · Science · doi:10.1126/science.1137013 · Cited by 1,423
Sustainable Development of the Agricultural Bio-Economy
Jordan, N., Boody, G., Broussard, W., Glover, J. D., Keeney, D., McCown, B. H., McIsaac, G., Muller, M., Murray, H., Neal, J., et al. (2007) · Science · doi:10.1126/science.1141700 · Cited by 321
From wood to fuels: Integrating biofuels and pulp production
Ragauskas, A. J., Nagy, M., Kim, D. H., Eckert, C. A., Hallett, J. P., & Liotta, C. L. (2006) · Industrial Biotechnology · doi:10.1089/ind.2006.2.55 · Cited by 256
Self-citationChemical structure of residual lignin from kraft pulp
Froass, P. M., Ragauskas, A. J., & Jiang, J.-E. (1996) · Journal of Wood Chemistry and Technology · doi:10.1080/02773819608545820 · Cited by 129
Self-citation
Cited by
Sturgeon, M. R., Kim, S., Lawrence, K., Paton, R. S., Chmely, S. C., Nimlos, M., Foust, T. D., & Beckham, G. T. (2014) · ACS Sustainable Chemistry & Engineering · doi:10.1021/sc400384w · Cited by 414
Yuan, Y., Jiang, B., Chen, H., Wu, W., Wu, S., Jin, Y., & Xiao, H. (2021) · Biotechnology for Biofuels · doi:10.1186/s13068-021-02054-1 · Cited by 344
Zhu, J., Yan, C., Zhang, X., Yang, C., Jiang, M., & Zhang, X. (2020) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2019.100788 · Cited by 303
Advances in lignin valorization towards bio-based chemicals and fuels: lignin biorefinery
Cao, Y., Chen, S. S., Zhang, S., Ok, Y. S., Matsagar, B. M., Wu, K. C.-W., & Tsang, D. C. W. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.121878 · Cited by 267
Structural elucidation of whole lignin from Eucalyptus based on preswelling and enzymatic hydrolysis
Wen, J.-L., Sun, S.-L., Yuan, T.-Q., & Sun, R.-C. (2015) · Green Chemistry · doi:10.1039/c4gc01889c · Cited by 207
Metadata
- Authors
- Pu, Y., Cao, S., & Ragauskas, A. J.
- Venue
- Energy & Environmental Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2011
- Volume
- 4
- Issue
- 9
- Pages
- 3154
- ISSN
- 1754-5692 (ISSN-L)
- 1754-5706
- OpenAlex
- W2155878184
- Cited by
- 543
- References
- 6
- FWCI
- 6.20
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Application of quantitative 31P NMR in biomass lignin and biofuel precursors characterization. https://app.cochat.ai/papers/10.1039/c1ee01201k
- The paper
- Pu, Y., Cao, S., & Ragauskas, A. J. (2011). Application of quantitative 31P NMR in biomass lignin and biofuel precursors characterization. Energy & Environmental Science. https://doi.org/10.1039/c1ee01201k
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.