Evidence map›Paper›PMID 41657214›Full record

ReviewNanoscale2026

Non-traditional granular magnetoresistive devices: a short review.

Shahriar Mostufa, Minxiang Zeng, Kai Wu

Abstract readReview
In one paragraph

Review in Nanoscale, 2026. 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
–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

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

3 authors.

Shahriar MostufaDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA. kai.wu@ttu.edu.ORCID http://orcid.org/0000-0002-3326-4817
Minxiang ZengDepartment of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA.ORCID http://orcid.org/0000-0002-3513-9200
Kai WuDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA. kai.wu@ttu.edu.ORCID http://orcid.org/0000-0002-9444-6112

Funding

Additive Manufacturing Wearable Magnetic Sensors: Revolutionizing Cardiac Health Monitoring with Machine Learning for Arrhythmia ClassificationR16GM158539 · NIGMS · TEXAS TECH UNIVERSITY · PI Kai Wu · 2025 to 2026
$276k
NIGMS NIH HHS R16 GM158539
6 · The paper itself

Abstract

Over the past decade, thin-film-based magnetoresistive (MR) sensors have undergone significant advancements. These conventional rigid thin-film MR sensors provided high sensitivity and reliability but were limited by their mechanical rigidity and incompatibility with curved or wearable surfaces. To address this flexibility issue, later flexible MR sensors were achieved by thinning or transferring the active layers of rigid sensors onto compliant substrates, which enabled strain-tolerant operation and integration with soft electronic platforms. However, these devices still relied on conventional film-based geometries that were restricted by the high fabrication costs, complexity, and poor scalability. Non-traditional granular MR sensors have emerged as a promising direction in spintronics research owing to their cost-effective fabrication, structural simplicity, and compatibility with flexible and wearable electronics. Despite these advantages, several challenges remain, particularly the need to enhance their sensitivity for low magnetic field detection, such as biomagnetic fields. This review aims to provide a comprehensive overview of to-date reported non-traditional granular MR devices, their physical mechanisms, material configurations, fabrication strategies, and MR device performance evolution over the past two decades. We highlighted how advances in nanoparticle engineering, insulating matrices, and polymeric binders have expanded the potential of granular MR systems for next-generation flexible and printable magnetoelectronic applications.

Identifiers

PMID41657214
PMCPMC13105805

What OpenQuestion holds

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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.