Real-time optimization of nuclear magnetic resonance experiments

Y-Q Song; Yiqiao Tang; M D Hürlimann; D G Cory

doi:10.1016/j.jmr.2018.02.009

Real-time optimization of nuclear magnetic resonance experiments

J Magn Reson. 2018 Apr:289:72-78. doi: 10.1016/j.jmr.2018.02.009. Epub 2018 Feb 13.

Authors

Y-Q Song¹, Yiqiao Tang², M D Hürlimann², D G Cory³

Affiliations

¹ Schlumberger-Doll Research, Cambridge, MA 02139, USA. Electronic address: ysong@slb.com.
² Schlumberger-Doll Research, Cambridge, MA 02139, USA.
³ Institute for Quantum Computing, University of Waterloo, Waterloo, ON N2L 3G1, Canada; Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada; Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5, Canada; Canadian Institute for Advanced Research, Toronto, ON M5G 1Z8, Canada.

PMID: 29476928
DOI: 10.1016/j.jmr.2018.02.009

Abstract

Nuclear Magnetic Resonance (NMR) experiments are typically performed with predetermined pulse sequences and acquisition parameters, and are oftentimes sub-optimal for individual samples under investigation. Here we explore a class of real-time optimization methods that conducts stochastic analyses on the acquired data and in turn updates and optimizes the subsequent measurements. We show superiority of the method to static approaches, both in the efficiency and quality of data acquisition, for a wide range of experiments.

Keywords: NMR; Real-time optimization; Stochastic analysis.