Evidence map›Paper›PMID 42582887›Full record

ArticleNature sensors2026

Clarifying the landscape of mechanical sensors from stress to strain.

Xinkai Xu, Rui Guo, Xiujun Fan, Zhaoqi Duan, Jun Chen

Abstract read
In one paragraph

Article in Nature sensors, 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

5 authors.

Xinkai XuDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
Rui GuoDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
Xiujun FanDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
Zhaoqi DuanDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0001-8883-1025
Jun ChenDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-3439-0495

Funding

Magnetoelastic Vascular GraftsR01HL175135 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Jun Chen · 2025 to 2026
$1.1M
A soft magnetoelastic microneedle patch for rapid skin cancer screeningR01CA287326 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Jun Chen · 2024 to 2026
$1.1M
NCI NIH HHS R01 CA287326NHLBI NIH HHS R01 HL175135
6 · The paper itself

Abstract

Stress and strain sensors are core measurement technologies used across a wide range of fields, including healthcare, environmental monitoring, aerospace and intelligent manufacturing. Although both capture aspects of mechanical behaviour, they are governed by distinct physical quantities: stress sensors measure internal mechanical stress, whereas strain sensors detect geometric deformation. Here, within the broader landscape of mechanical sensing, we discuss the differences between stress and strain sensors to clarify the common misconceptions that can obscure their distinctions, lead to misinterpretation in performance evaluation and affect sensor design. This Review establishes a unifying, mechanics-oriented framework that aligns sensing mechanisms, material systems and structural designs with stress-driven and strain-driven sensing pathways. It facilitates the mechanism-specific interpretation of performance metrics and integrates system-level considerations with artificial intelligence-assisted signal processing. The framework enables rational device design, reliable signal analysis and application-specific sensor selection, with relevance that extends beyond mechanical sensing.

Identifiers

PMID42582887
PMCPMC13459086

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.