Evidence map›Paper›PMID 37231050›Full record

ArticleScientific reports2023

Exchange biased surface acoustic wave magnetic field sensors.

Viktor Schell, Elizaveta Spetzler, Niklas Wolff, Lars Bumke, Lorenz Kienle, Jeffrey McCord, Eckhard Quandt, Dirk Meyners

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 citing papers in PubMed.

  1. The 2026 guided acoustic waves roadmap.Journal of physics D: Applied physics · 2026
    Review
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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

8 authors.

Viktor SchellInorganic Functional Materials, Institute for Materials Science, Christian-Albrechts-Universität zu Kiel, 24143, Kiel, Germany.
Elizaveta SpetzlerNanoscale Magnetic Materials - Magnetic Domains, Institute for Materials Science, Christian-Albrechts-Universität zu Kiel, 24143, Kiel, Germany.
Niklas WolffSynthesis and Real Structure, Institute for Materials Science, Christian-Albrechts-Universität zu Kiel, 24143, Kiel, Germany.
Lars BumkeInorganic Functional Materials, Institute for Materials Science, Christian-Albrechts-Universität zu Kiel, 24143, Kiel, Germany.
Lorenz KienleSynthesis and Real Structure, Institute for Materials Science, Christian-Albrechts-Universität zu Kiel, 24143, Kiel, Germany.
Jeffrey McCordNanoscale Magnetic Materials - Magnetic Domains, Institute for Materials Science, Christian-Albrechts-Universität zu Kiel, 24143, Kiel, Germany.
Eckhard QuandtInorganic Functional Materials, Institute for Materials Science, Christian-Albrechts-Universität zu Kiel, 24143, Kiel, Germany.
Dirk MeynersInorganic Functional Materials, Institute for Materials Science, Christian-Albrechts-Universität zu Kiel, 24143, Kiel, Germany. dm@tf.uni-kiel.de.

Funding

Deutsche Forschungsgemeinschaft CRC1261
6 · The paper itself

Abstract

Magnetoelastic composites which use surface acoustic waves show great potential as sensors of low frequency and very low amplitude magnetic fields. While these sensors already provide adequate frequency bandwidth for most applications, their detectability has found its limitation in the low frequency noise generated by the magnetoelastic film. Amongst other contributions, this noise is closely connected to domain wall activity evoked by the strain from the acoustic waves propagating through the film. A successful method to reduce the presence of domain walls is to couple the ferromagnetic material with an antiferromagnetic material across their interface and therefore induce an exchange bias. In this work we demonstrate the application of a top pinning exchange bias stack consisting of ferromagnetic layers of (Fe

Identifiers

PMID37231050
PMCPMC10212973

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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.