Evidence map›Paper›PMID 42395863›Full record

ArticleJournal of physics D: Applied physics2026

Reassessing magnetic tunnel junction detectability for ultrasensitive sensing using small-field sensitivity and Jiles-Atherton modeling.

Benjamin J Brown, Liam K Mitchell, Hongzhou Yu, Gang Xiao

Abstract read
In one paragraph

Article in Journal of physics D: Applied physics, 2026. 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

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

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

4 authors.

Benjamin J BrownDepartment of Physics, Brown University, Providence, RI 02912, United States of America.ORCID https://orcid.org/0000-0001-6347-9598
Liam K MitchellDepartment of Physics, Brown University, Providence, RI 02912, United States of America.ORCID https://orcid.org/0009-0006-9208-3852
Hongzhou YuDepartment of Physics, Brown University, Providence, RI 02912, United States of America.ORCID https://orcid.org/0009-0003-6488-233X
Gang XiaoDepartment of Physics, Brown University, Providence, RI 02912, United States of America.ORCID https://orcid.org/0000-0002-3440-3946

Funding

Development of Quantum Magnetic Tunneling Junction Sensor Arrays for Brain Magnetoencephalography (MEG) under Natural SettingsUG3EB034695 · NIBIB · BROWN UNIVERSITY · PI SANES, JEROME N, XIAO, GANG · 2023 to 2025
$1.0M
NIBIB NIH HHS UG3 EB034695
6 · The paper itself

Abstract

Magnetic tunnel junction (MTJ) sensors are attractive for detecting extremely small magnetic fields, yet their performance is often quantified using sensitivities derived from major loops or larger-field minor loops which inherently include hysteretic contributions. As a result, these conventional metrics significantly overestimate detectability by including irreversible processes that vanish in the small-field limit. Here, we systematically measure the low-field response of uniaxial MTJs under both ac and dc magnetic excitation and demonstrate a distinct transition from hysteretic to fully reversible behavior as the field amplitude is reduced. By adapting the Jiles-Atherton model to analyze MTJ conductance, we establish a unified framework that captures magnetization processes down to nanotesla range, yielding quantitative parameters that resolve reversible and irreversible dynamics with excellent fidelity. In the reversible regime, the sensitivity converges to a constant value of 0.55%Oe

Indexed as

Jiles–Atherton modelmagnetic hysteresismagnetic sensormagnetic tunnel junctionmagnetometrysmall-field sensitivity

Identifiers

PMID42395863
PMCPMC13324885

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.