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Title: Large-scale ab initio simulations of MAS DNP enhancements using a Monte Carlo optimization strategy

Abstract

Magic-angle-spinning (MAS) dynamic nuclear polarization (DNP) has recently emerged as a powerful technology enabling otherwise unrealistic solid-state NMR experiments. The simulation of DNP processes which might, for example, aid in refining the experimental conditions or the design of better performing polarizing agents, is, however, plagued with significant challenges, often limiting the system size to only 3 spins. Here, we present the first approach to fully ab initio large-scale simulations of MAS DNP enhancements. The Landau-Zener equation is used to treat all interactions concerning electron spins, and the low-order correlations in the Liouville space method is used to accurately treat the spin diffusion, as well as its MAS speed dependence. As the propagator cannot be stored, a Monte Carlo optimization method is used to determine the steady-state enhancement factors. As a result, this new software is employed to investigate the MAS speed dependence of the enhancement factors in large spin systems where spin diffusion is of importance, as well as to investigate the impacts of solvent and polarizing agent deuteration on the performance of MAS DNP.

Authors:
ORCiD logo [1]; ORCiD logo [2]
  1. Ames Lab., Ames, IA (United States)
  2. Ames Lab. and Iowa State Univ., Ames, IA (United States)
Publication Date:
Research Org.:
Ames Laboratory (AMES), Ames, IA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1481877
Report Number(s):
IS-J-9741
Journal ID: ISSN 0021-9606
Grant/Contract Number:  
AC02-07CH11358
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 149; Journal Issue: 15; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Perras, Frédéric A., and Pruski, Marek. Large-scale ab initio simulations of MAS DNP enhancements using a Monte Carlo optimization strategy. United States: N. p., 2018. Web. doi:10.1063/1.5042651.
Perras, Frédéric A., & Pruski, Marek. Large-scale ab initio simulations of MAS DNP enhancements using a Monte Carlo optimization strategy. United States. doi:10.1063/1.5042651.
Perras, Frédéric A., and Pruski, Marek. Fri . "Large-scale ab initio simulations of MAS DNP enhancements using a Monte Carlo optimization strategy". United States. doi:10.1063/1.5042651.
@article{osti_1481877,
title = {Large-scale ab initio simulations of MAS DNP enhancements using a Monte Carlo optimization strategy},
author = {Perras, Frédéric A. and Pruski, Marek},
abstractNote = {Magic-angle-spinning (MAS) dynamic nuclear polarization (DNP) has recently emerged as a powerful technology enabling otherwise unrealistic solid-state NMR experiments. The simulation of DNP processes which might, for example, aid in refining the experimental conditions or the design of better performing polarizing agents, is, however, plagued with significant challenges, often limiting the system size to only 3 spins. Here, we present the first approach to fully ab initio large-scale simulations of MAS DNP enhancements. The Landau-Zener equation is used to treat all interactions concerning electron spins, and the low-order correlations in the Liouville space method is used to accurately treat the spin diffusion, as well as its MAS speed dependence. As the propagator cannot be stored, a Monte Carlo optimization method is used to determine the steady-state enhancement factors. As a result, this new software is employed to investigate the MAS speed dependence of the enhancement factors in large spin systems where spin diffusion is of importance, as well as to investigate the impacts of solvent and polarizing agent deuteration on the performance of MAS DNP.},
doi = {10.1063/1.5042651},
journal = {Journal of Chemical Physics},
issn = {0021-9606},
number = 15,
volume = 149,
place = {United States},
year = {2018},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on October 19, 2019
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