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Cited by 275

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The advantages of ESI‐MS detection in conjunction with HILIC mode separations: Fundamentals and applications

Nguyen, Hien P., Schug, Kevin A.·2008·Journal of Separation Science

Abstract

The analysis of highly hydrophilic, ionic, and polar compounds has been performed by HILIC‐ESI‐MS for the last few years. The use of low aqueous/high polar organic solvent content in HILIC separation mobile phase is almost ideal for ESI‐MS detection in many cases, resulting in increased sensitivity. Although the addition of modifiers such as acids or salts is necessary in some circumstances for a good separation, the optimum concentrations used are still highly amenable for ESI‐MS analysis, showing few deleterious effects. In this review, the mechanism of HILIC separation and ESI ion generation will be briefly discussed, followed by a summary of method development and applications in several fields of research including pharmaceutical, biomolecular, food, metabolic, and environmental analysis.

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Crossref · 10.1002/jssc.200700630

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Metadata

Authors
Nguyen, H. P., & Schug, K. A.
Venue
Journal of Separation Science
Publisher
Wiley
Published
2008-05
Volume
31
Issue
9
Pages
1465-1480
ISSN
  • 1615-9306 (ISSN-L)
  • 1615-9314
OpenAlex
W2093664208
Cited by
275
References
2
FWCI
10.05
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The advantages of ESI‐MS detection in conjunction with HILIC mode separations: Fundamentals and applications. https://app.cochat.ai/papers/10.1002/jssc.200700630
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Nguyen, H. P., & Schug, K. A. (2008). The advantages of ESI‐MS detection in conjunction with HILIC mode separations: Fundamentals and applications. Journal of Separation Science. https://doi.org/10.1002/jssc.200700630

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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.