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A computationally efficient discrete pseudomodulation algorithm for real-time magnetic resonance measurements.
Manning, Brian R; Sharov, Fedor V; Lenahan, Patrick M.
Afiliação
  • Manning BR; Keysight Technologies, Inc., Santa Rosa, California 95403, USA.
  • Sharov FV; The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
  • Lenahan PM; The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
Rev Sci Instrum ; 93(1): 015104, 2022 Jan 01.
Article em En | MEDLINE | ID: mdl-35104942
ABSTRACT
Rapid-scan electron paramagnetic resonance (RSEPR) results in a significant improvement in signal-to-noise over magnetic field modulated continuous wave EPR (CWEPR). However, the RSEPR raw absorption spectra can make the real-time comparison of CWEPR spectra difficult, especially in systems where the total number of paramagnetic spins is low. In this paper, we illustrate a method of applying pseudomodulation within RSEPR data collection software in real-time. Pseudomodulation is generally carried out in post-processing to increase signal-to-noise and simulate the effects of modulation on the spectra observed in traditional magnetic field modulated CWEPR. By applying the pseudomodulation method on a discrete computational basis, the technique can be utilized in parallel with data collection due to the significantly reduced computational power of the discretized pseudomodulation calculation. This allows for the live alteration of modulation parameters, such as the modulation amplitude and modulation harmonic. This real-time simulation allows for the comparison of the accumulated non-adiabatic rapid-sweep EPR spectra with the known CWEPR spectra available in the literature and has the ability to view smaller and less sensitive resonance features for various harmonics during high-frequency experiments while retaining all signal-to-noise improvements.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article