Evidence map›Paper›PMID 38006125›Full record

ArticlePolymers2023

Cognizant Fiber-Reinforced Polymer Composites Incorporating Seamlessly Integrated Sensing and Computing Circuitry.

Mohammed Jaradat, Jorge Loredo Duran, Daniel Heras Murcia, Leah Buechley, Yu-Lin Shen, Christos Christodoulou, Mahmoud Reda Taha

Open access · goldAbstract read
In one paragraph

Article in Polymers, 2023. 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
0.2field-weighted citation impact, top 50% of its field
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 citations in OpenAlex.

  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

7 authors at 2 institutions in 2 countries.

Mohammed JaradatDepartment of Civil and Infrastructure Engineering, Al-Zaytoonah University of Jordan, Amman 11733, Jordan.ORCID 0000-0002-9069-8603
Jorge Loredo DuranDepartment of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA.
Daniel Heras MurciaGerlad May Department of Civil, Construction & Environmental Engineering, University of New Mexico, Albuquerque, NM 87131, USA.ORCID 0000-0001-5301-1510
Leah BuechleyDepartment of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA.
Yu-Lin ShenDepartment of Mechanical Engineering, University of New Mexico, Albuquerque, NM 87131, USA.ORCID 0000-0002-6373-9094
Christos ChristodoulouDepartment of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
Mahmoud Reda TahaGerlad May Department of Civil, Construction & Environmental Engineering, University of New Mexico, Albuquerque, NM 87131, USA.ORCID 0000-0002-3707-9336
University of New Mexico · USAl-Zaytoonah University of Jordan · JO

Funding

United States Air Force Research Laboratory N/A
6 · The paper itself

Abstract

Structural fiber-reinforced polymer (FRP) composite materials consisting of a polymer matrix reinforced with layers of high-strength fibers are used in numerous applications, including but not limited to spacecraft, vehicles, buildings, and bridges. Researchers in the past few decades have suggested the necessary integration of sensors (e.g., fiber optic sensors) in polymer composites to enable health monitoring of composites' performance over their service lives. This work introduces an innovative cognizant composite that can self-sense, compute, and implement decisions based on sensed values. It is a critical step towards smart, resilient infrastructure. We describe a method to fabricate textile sensors with flexible circuitry and a microcontroller within the polymer composite, enabling computational operations to take place in the composite without impacting its integrity. A microstructural investigation of the sensors showed that the amount of oxidative agent and soaking time of the fabric play a major role in the adsorption of polypyrrole (PPy) on fiberglass (FG). XPS results showed that the 10 g ferric chloride solution with 6 h of soaking time had the highest degree of protonation (28%) and, therefore, higher adsorption of PPy on FG. A strain range of 30% was achieved by examining different circuitry and sensor designs for their resistance and strain resolution under mechanical loading. A microcontroller was added to the circuit and then embedded within a composite material. This composite system was tested under flexural loading to demonstrate its self-sensing, computing, and actuation capabilities. The resulting cognizant composite demonstrated the ability to read resistance values and measure strain using the embedded microcontroller and autonomously actuate an LED light when the strain exceeds a predefined limit of 2000 µε. The application of the proposed FRP system would provide in situ monitoring of structural composite components with autonomous response capabilities, as well as reduce manufacturing, production, and maintenance costs.

Indexed as

cognizant compositesfiber-reinforced polymerspiezoresistive sensorsresilient infrastructureself-sensingsmart composites

Identifiers

PMID38006125
PMCPMC10674995
OpenAlexW4388654722

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.