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Cited by 337

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Fast analysis in liquid chromatography using small particle size and high pressure

Open access
Nguyen, Dao T.‐T., Guillarme, Davy, Rudaz, Serge, Veuthey, Jean‐Luc·2006·Journal of Separation Science

Abstract

In order to enhance chromatographic performances in terms of efficiency and rapidity, LC has recently evolved in the development of short columns packed with small particles (sub‐2 μm) working at high pressures (>400 bar). This approach has been described 30 years ago according to the fundamental chromatographic equations. However, systems and columns compatible with such high pressures have been introduced in the market in 2004 only. Advantages of small particles working at high pressure will be discussed in terms of sensitivity, efficiency, resolution, and analysis time. Potential problems encountered with high pressure in terms of frictional heating and solvent compressibility will also be discussed even if systems working at a maximum pressure of 1000 bar are not influenced by these parameters and give reliable and reproducible results. Several applications will highlight the potential and interest of this new technology.

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Crossref · 10.1002/jssc.200600189

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Metadata

Authors
Nguyen, D. T. ‐., Guillarme, D., Rudaz, S., & Veuthey, J.
Venue
Journal of Separation Science
Publisher
Wiley
Published
2006-08
Volume
29
Issue
12
Pages
1836-1848
ISSN
  • 1615-9306 (ISSN-L)
  • 1615-9314
OpenAlex
W1990112228
Cited by
337
References
41
FWCI
10.21
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Fast analysis in liquid chromatography using small particle size and high pressure. https://app.cochat.ai/papers/10.1002/jssc.200600189
The paper
Nguyen, D. T. ‐., Guillarme, D., Rudaz, S., & Veuthey, J. (2006). Fast analysis in liquid chromatography using small particle size and high pressure. Journal of Separation Science. https://doi.org/10.1002/jssc.200600189

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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.