Combination of linear solvent strength model and quantitative structure–retention relationships as a comprehensive procedure of approximate prediction of retention in gradient liquid chromatography-Reference-Cited by-同舟云学术

Combination of linear solvent strength model and quantitative structure–retention relationships as a comprehensive procedure of approximate prediction of retention in gradient liquid chromatography

Published:2002-07 Issue:1-2 Volume:962 Page:41-55
ISSN:0021-9673
Container-title:Journal of Chromatography A
language:en
Short-container-title:Journal of Chromatography A

Author:

Ba̧czek Tomasz,Kaliszan Roman

Publisher

Elsevier BV

Subject

Organic Chemistry,General Medicine,Biochemistry,Analytical Chemistry

Reference38 articles.

1. Self-consistent molecular orbital methods. 21. Small split-valence basis sets for first-row elements

2. Self-consistent molecular orbital methods. 24. Supplemented small split-valence basis sets for second-row elements

3. Chemia supramolekularna;Lehn,1998

4. Quantitative Structure–Chromatographic Retention Relationships;Kaliszan,1987

5. Chemometrics: A Textbook;Massart,1988

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