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Cited by 349

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Generation of Impossible Cross-Peaks Between Bulk Water and Biomolecules in Solution NMR

Warren, Warren S., Richter, Wolfgang, Andreotti, Amy Hamilton, Farmer, Bennett T.·1993·Science

Abstract

Intermolecular multiple-quantum coherences between bulk water and a glycoprotein fragment at modest concentration (20 mM) have been experimentally produced and detected, although such coherences are inconceivable in the normal theoretical framework of nuclear magnetic resonance. A density matrix treatment explains these results by including the long-range dipolar interaction between spins and by discarding the high-temperature approximation. These results imply that peak intensities (critical for structural determinations) can be distorted in many gradient experiments, and show that magic-angle gradients provide substantial improvements with reduced gradient strengths. They also suggest methods for contrast enhancement in magnetic resonance imaging.

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Crossref · 10.1126/science.8266096

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Metadata

Authors
Warren, W. S., Richter, W., Andreotti, A. H., & Farmer, B. T.
Venue
Science
Published
1993-12-24
Volume
262
Issue
5142
Pages
2005-2009
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2034996484
Cited by
349
References
9
FWCI
4.78
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Generation of Impossible Cross-Peaks Between Bulk Water and Biomolecules in Solution NMR. https://app.cochat.ai/papers/10.1126/science.8266096
The paper
Warren, W. S., Richter, W., Andreotti, A. H., & Farmer, B. T. (1993). Generation of Impossible Cross-Peaks Between Bulk Water and Biomolecules in Solution NMR. Science. https://doi.org/10.1126/science.8266096

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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.