Evidence map›Paper›PMID 39459091›Full record

ArticleMicromachines2024

Research on Spatial Localization Method of Magnetic Nanoparticle Samples Based on Second Harmonic Waves.

Zheyan Wang, Ping Huang, Fuyin Zheng, Hongli Yu, Yue Li, Zhichuan Qiu, Lingke Gai, Zhiyao Liu, Shi Bai

Abstract read
In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Zheyan WangSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.
Ping HuangSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.ORCID 0000-0002-9301-5775
Fuyin ZhengSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.ORCID 0000-0003-4489-4547
Hongli YuSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.
Yue LiSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.ORCID 0009-0006-2985-3527
Zhichuan QiuSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.ORCID 0009-0006-8477-2893
Lingke GaiSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.
Zhiyao LiuSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.
Shi BaiSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.ORCID 0000-0001-5297-6353

Funding

Basic Scientific Study Project for Institutes of Higher Learning of Liaoning Province LJKMZ20220477National Key Research and Development Program of China 2023YFB3407803National Natural Science Foundation of China 62301338Natural Science Foundation of Liaoning Province 2023-MSLH-261
6 · The paper itself

Abstract

Existing magnetic tracer detection systems primarily rely on fundamental wave signal acquisition using non-differential sensor configurations. These sensors are highly susceptible to external interference and lack tomographic localization capabilities, hindering their clinical application. To address these limitations, this paper presents a novel method for achieving the deep spatial localization of tracers. The method exploits second harmonic signal detection at non-zero field points. By considering the combined nonlinear characteristics of the coil's axial spatial magnetic field distribution and the Langevin function, a correlation model linking the signal peak and bias field is established. This model enables the determination of the tracer's precise spatial location. Building on this framework, a handheld device for localizing magnetic nanoparticle tracers was developed. The device harnesses the second harmonic response generated by coupling an AC excitation field with a DC bias field. Our findings demonstrate that under conditions of reduced coil turns and weak excitation fields, the DC bias field exhibits exclusive dependence on the axial distance of the detection point, independent of particle concentration. This implies that the saturated DC bias field corresponding to the second harmonic signal can be used to determine the magnetic nanoparticle sample detection depth. The experimental results validated the method's high accuracy, with axial detection distance and concentration reduction errors of only 4.8% and 4.1%, respectively. This research paves the way for handheld probes capable of tomographic tracer detection, offering a novel approach for advancing magnetically sensitive biomedical detection technologies.

Indexed as

DC bias fieldLangevin functionmagnetic nanoparticlesecond harmonictomographic positioning

Identifiers

PMID39459091
PMCPMC11509944

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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.