Evidence map›Paper›PMID 38238376›Full record

ArticleScientific reports2024

Broadband stripline Lenz lens achieves 11 × NMR signal enhancement.

Jianyi Liang, Hossein Davoodi, Sagar Wadhwa, Vlad Badilita, Jan G Korvink

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jianyi Liang *Institute of Microstructure Technology (IMT), Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany.
Hossein Davoodi *Littelfuse Inc., 68623, Lampertheim, Germany.
Sagar Wadhwa *Voxalytic GmbH, 76228, Karlsruhe, Germany.
Vlad BadilitaInstitute of Microstructure Technology (IMT), Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany. vlad.badilita@kit.edu.
Jan G KorvinkInstitute of Microstructure Technology (IMT), Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany. jan.korvink@kit.edu.

Funding

European Research Council 951459
6 · The paper itself

Abstract

A Lenz lens is an electrically passive conductive element that, when placed in a time-varying magnetic field, acts as a magnetic flux concentrator or a magnetic lens. In the realm of nuclear magnetic resonance (NMR), Lenz lenses have been exploited as electrically passive metallic radiofrequency interposers placed between a sample and a tuned or untuned NMR detector in order to focus the [Formula: see text]-field of the detector onto a smaller sample space. Here we explore a novel embodiment of the Lenz lens, which acts as a non-resonant stripline interposer, i.e., the [Formula: see text]-field acts along the longitudinal volume of a sample container, such as a capillary or other microfluidic channel that is coincident with the axis of the stripline. The almost vanishing self-resonance of the stripline Lenz lens, at frequencies relevant for NMR, leads to a desirable [Formula: see text]-field amplitude that is nearly perfectly uniform across the sample and hence lacking a characteristic sinusoidal modal shape. The action of Lenz' law ensures that no stray [Formula: see text]-field is found outside of the stripline's active volume. Because the stripline Lenz lens does not rely on its own geometry to achieve resonance, its frequency response is thus widely broadband for field enhancements up to a factor of 11, with only the external driving resonator properties governing the overall resonant behaviour. We explore the use of the stripline Lenz lens with a sub-nanolitre sample volume, readily detecting 4 isotopes with resonances ranging from 125.76 to 500 MHz. The concept holds potential for the NMR study of thin films, small biological samples, as well as the in situ study of battery materials.

Identifiers

PMID38238376
PMCPMC10796323

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.