Evidence map›Paper›PMID 39408520›Full record

ArticlePolymers2024

Robust Strain Sensor with High Sensitivity Based on Polymer-Encapsulated Microfiber Mach-Zehnder Interferometer.

Bin Xiao, Funa Zhuang, Jing Wang, Zhongyu Yao, Shanshan Wang

Abstract read
In one paragraph

Article in Polymers, 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
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0citing papers in PubMed
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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

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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

5 authors.

Bin XiaoOptics and Optoelectronics Laboratory, College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao 266100, China.
Funa ZhuangOptics and Optoelectronics Laboratory, College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao 266100, China.
Jing WangOptics and Optoelectronics Laboratory, College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao 266100, China.
Zhongyu YaoOptics and Optoelectronics Laboratory, College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao 266100, China.
Shanshan WangOptics and Optoelectronics Laboratory, College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao 266100, China.ORCID 0000-0002-5324-6050

Funding

National Natural Science Foundation of China 62375248Shandong Province Undergraduate Teaching Reform Research M2021120Shandong Provincial Natural Science Foundation ZR202102180842Shandong Provincial Natural Science Foundation ZR202209220006
6 · The paper itself

Abstract

A robust strain sensor is demonstrated based on a microfiber Mach-Zehnder interferometer (MMZI) encapsulated by the polymer polydimethylsiloxane (PDMS). Benefiting from the low Young's modulus of PDMS, both a robust structure and high sensitivity can be realized based on three different encapsulations. In the experiment, the proposed sensors are fabricated and tested with strain sensitivities ranging from -20.95 pm/με to 127.00 pm/με within the wavelength range of 1200-1650 nm. Compared with the bare MMZI sensor, at least one order of magnitude higher sensitivity is reached. To further evaluate the performance of the sensor, the dependences of sensitivity on probing wavelength and the different types and quantities of polymers used in encapsulation are discussed. Results show that the sensitivity of the sensor will increase with the probing wavelength. The type and quantity of polymer used are also very critical to sensitivity. Additionally, a response time of 24.72 ms can be reached. Good recoverability and repeatability of the sensor are also demonstrated by repeated experiments. The strain sensor demonstrated here shows the advantages of simple fabrication, robust structure, high and tunable sensitivity, fast response, good recoverability and repeatability.

Indexed as

microfiber Mach–Zehnder interferometerpolymerstrain sensor

Identifiers

PMID39408520
PMCPMC11478986

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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.