Distinguishing enantiomeric amino acids with chiral cyclodextrin adducts and structures for lossless ion manipulations. Issue 24 (17th September 2018)

• Record Type: Journal Article
• Authors: Nagy, Gabe; Chouinard, Christopher D.; Attah, Isaac K.; Webb, Ian K.; Garimella, Sandilya V. B.; Ibrahim, Yehia M.; Baker, Erin S.; Smith, Richard D.
• Other Names: Mechref Y. guestEditor.
• Is Part Of: Electrophoresis. Volume 39:Issue 24(2018)
• Page Start: 3148
• Page End: 3155
• Publication Date: 2018-09-17
• Subjects: Chiral separations -- Cyclodextrins -- Enantiomers -- Ion mobility‐mass spectrometry -- Structures for lossless ion manipulations; Electrophoresis -- Periodicals; Electrophoresis -- Periodicals; 541.372
• DOI: 10.1002/elps.201800294
• Languages: English
• ISSNs: 0173-0835

Abstract

Abstract: Enantiomeric molecular evaluations remain an enormous challenge for current analytical techniques. To date, derivatization strategies and long separation times are generally required in these studies, and the development and implementation of new approaches are needed to increase speed and distinguish currently unresolvable compounds. Herein, we describe a method using chiral cyclodextrin adducts and structures for lossless ion manipulations (SLIM) and serpentine ultralong path with extended routing (SUPER) ion mobility (IM) to achieve rapid, high resolution separations ofd andl enantiomeric amino acids. In the analyses, a chiral cyclodextrin is added to each sample. Two cyclodextrins were found to complex each amino acid molecule (i.e. potentially sandwiching the amino acid in their cavities) and forming host‐guest noncovalent complexes that were distinct for eachd andl amino acid pair studied and thus separable with IM in SLIM devices. The SLIM was also used to accumulate much larger ion populations than previously feasible for evaluation and therefore allow enantiomeric measurements of higher sensitivity, with gains in resolution from our ultralong path separation capabilities, than previously reported by any other IM–based approach.