Evidence map›Paper›PMID 39860803›Full record

ArticleSensors (Basel, Switzerland)2025

Bias Calibration of Optically Pumped Magnetometers Based on Variable Sensitivity.

Jieya Chen, Chaofeng Ye, Xingshen Hou, Yaqiong Niu, Limin Sun

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

5 authors.

Jieya ChenShanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.ORCID 0009-0009-2591-9025
Chaofeng YeSchool of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Xingshen HouSchool of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Yaqiong NiuSchool of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Limin SunShanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.ORCID 0000-0002-2004-0777

Funding

National Key RD Program of China: BTIT 2022YFF1202800
6 · The paper itself

Abstract

Optically pumped magnetometers (OPMs) functioning in the spin-exchange relaxation-free (SERF) regime have emerged as attractive options for measuring weak magnetic fields, owing to their portability and remarkable sensitivity. The operation of SERF-OPM critically relies on the ambient magnetic field; thus, a magnetic field compensation device is commonly employed to mitigate the ambient magnetic field to near zero. Nonetheless, the bias of the OPM may influence the compensation impact, a subject that remains unexamined. This paper introduced an innovative bias calibration technique for OPMs. The sensitivity of the OPM was altered by adjusting the cell temperature. The output of the OPM was then documented with varying sensitivity. It is assumed that the signal exhibits a linear correlation with the environmental magnetic field, and the statistical characteristics of the magnetic field are identical for both measurements, upon which the bias of the OPM is assessed. The bias was subsequently considered in the feedback magnetic field compensation mechanism. The results indicate that this technique might successfully reduce environmental magnetic fluctuations and enhance the sensitivity of the OPM. This technique measured the magnetic field produced by the human heart, confirming the viability of the ultra-weak biomagnetic field measurement approach.

Indexed as

bias calibrationbiomagnetic field measurementmagnetic field compensationOPM

Identifiers

PMID39860803
PMCPMC11768902

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Registered trials

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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.