Noise model of the cryogenic nuclear magnetic resonance spectroscopy system's receiving chain

Scientific paper - Original scientific paper

• Author: Petar Kolar
• Author: Lovro Blažok
• Author: Dario Bojanjac
• Author's institution: University of Zagreb; Faculty of Science; (Department of Physics)
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Physics

Abstract (english)

One of the challenges in modern nuclear magnetic resonance (NMR) is achieving its highest possible measuring sensitivity. This is because modern NMR samples' response signals decrease, so background noise of the used NMR spectroscopy system causes bigger problems. As a solution, both software and hardware interventions were applied. However, these improvements were obtained experimentally, so the reason for their proper operation and upper limit is usually unknown. Recently, a noise model of the NMR spectroscopy system, which shows both the reason for proper operation and the upper limit of the applied improvements, was introduced. A Javascript-based calculator, based on the introduced model, has been developed and made available online as a user-friendly website that can be run on the most commonly used Internet browsers. To the authors' knowledge, this calculator is the first of its kind that analyses noise properties in NMR. Using it, one can a priori make both sensitivity prediction of practical NMR systems in physics and material science and quantitative analysis of its noise properties. Consequently, overall measurement duration can be shortened down to one half of the current duration. This is an immense improvement, as some modern NMR measurements consume more than ten hours per measurement.

• Keywords (english): NMR spectroscopy; noise figure; model; sensitivity enhancement; Python; Javascript
• Language: english
• Publication type: Scientific paper - Original scientific paper
• Publication status: Published
• Peer review: Peer review - international
• Publication version: Published version
• Journal title: Automatika
• Numbering: vol. 63, no. 3, pp. 474-481
• p-ISSN: 0005-1144
• e-ISSN: 1848-3380
• DOI: https://doi.org/10.1080/00051144.2022.2052649
• URN:NBN: urn:nbn:hr:217:835277
• Publication: 2022-03-18
• Document URL: https://www.tandfonline.com/doi/full/10.1080/00051144.2022.2052649
• Type of resource: Text
• Access conditions: Open access
• Public note: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Received 23 June 2020; Accepted 7 March 2022; Published online: 18 Mar 2022.
• Created on: 2024-08-09 19:18:53