Literature DB >> 12236530

Atypical silica-based column packings for high-performance liquid chromatography.

J J Kirkland1, F A Truszkowski, R D Ricker.   

Abstract

Column packings widely used for high-performance liquid chromatography (HPLC) mostly are based on porous silica microspheres with certain pore sizes and pore size distributions. Such materials have the most desirable compromise of properties that provide for effective and reproducible separations over a wide range of operating conditions. To provide desired separation characteristics, several manufacturers specially synthesize the silica particles for these packings. While such column packing materials have general utility for a wide range of needs, special silica-based particles have been synthesized with different physical conformations for special separation goals. This presentation describes some atypical types of silica-based particles with unique separation properties that enlarge the capabilities of HPLC methods.

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Year:  2002        PMID: 12236530     DOI: 10.1016/s0021-9673(01)01339-5

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  3 in total

1.  Design of Experiment (DOE) Utilization to Develop a Simple and Robust Reversed-Phase HPLC Technique for Related Substances' Estimation of Omeprazole Formulations.

Authors:  Vayeda Chintan Manranjan; Devendra Singh Yadav; Hitesh Amrutlal Jogia; Praful Lalitkumar Chauhan
Journal:  Sci Pharm       Date:  2013-08-12

2.  Reversed-phase fused-core HPLC modeling of peptides.

Authors:  Matthias D'Hondt; Bert Gevaert; Sofie Stalmans; Sylvia Van Dorpe; Evelien Wynendaele; Kathelijne Peremans; Christian Burvenich; Bart De Spiegeleer
Journal:  J Pharm Anal       Date:  2012-11-30

3.  Mesocellular Silica Foams (MCFs) with Tunable Pore Size as a Support for Lysozyme Immobilization: Adsorption Equilibrium and Kinetics, Biocomposite Properties.

Authors:  Agnieszka Chrzanowska; Anna Derylo-Marczewska; Malgorzata Wasilewska
Journal:  Int J Mol Sci       Date:  2020-07-31       Impact factor: 5.923

  3 in total

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