Determination of the g-, hyperfine coupling- and zero-field splitting tensors in EPR and ENDOR using extended Matlab codes
Autor: | Anders Lund, Freddy Callens, Einar Sagstuen |
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Rok vydání: | 2020 |
Předmět: |
Nuclear and High Energy Physics
Biophysics Zero field splitting 010402 general chemistry 01 natural sciences Biochemistry 030218 nuclear medicine & medical imaging law.invention Crystal (programming language) 03 medical and health sciences symbols.namesake 0302 clinical medicine Software law Linear regression Electron paramagnetic resonance MATLAB computer.programming_language Physics Zeeman effect business.industry Plane (geometry) EPR ENDOR Single crystals Data analysis MatLab open code Coupling tensors Medicinsk bildbehandling Condensed Matter Physics 0104 chemical sciences Computational physics Medical Image Processing symbols business computer |
Zdroj: | Journal of magnetic resonance (San Diego, Calif. : 1997). 325 |
ISSN: | 1096-0856 1090-7807 |
Popis: | The analysis of single crystal electron magnetic resonance (EMR) data has traditionally been performed using software in programming languages that are difficult to update, are not easily available, or are obsolete. By using a modern script-language with tools for the analysis and graphical display of the data, three MatLab (R) codes were prepared to compute the g, zero-field splitting (zfs) and hyperfine coupling (hfc) tensors from roadmaps obtained by EPR or ENDOR measurements in three crystal planes. Schonlands original method was used to compute the g- and hfc-tensors by a least-squares fit to the experimental data in each plane. The modifications required for the analysis of the zfs of radical pairs with S = 1 were accounted for. A non-linear fit was employed in a second code to obtain the hfc-tensor from EPR measurements, taking the nuclear Zeeman interaction of an I = 1/2 nucleus into account. A previously developed method to calculate the g- and hfc -tensors by a simultaneous linear fit to all data was used in the third code. The validity of the methods was examined by comparison with results obtained experimentally, and by roadmaps computed by exact diagonalization. The probable errors were estimated using functions for regression analysis available in MatLab. The software will be published at https://doi.org/10.17632/ps24sw95gz.1, Input and output examples presented in this work can also be downloaded from https://old.liu.se/simarc/downloads?l=en. (C) 2021 The Author(s). Published by Elsevier Inc. Funding Agencies|Linkoping University; Ghent UniversityGhent University; University of Oslo |
Databáze: | OpenAIRE |
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