Evidence map›Paper›PMID 35633818›Full record

ArticleSensors international2022

Graphene-based temperature, humidity, and strain sensor: A review on progress, characterization, and potential applications during Covid-19 pandemic.

Zulhelmi Ismail, Wan Farhana W Idris, Abu Hannifa Abdullah

Open access · goldAbstract read
In one paragraph

Article in Sensors international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

7 citing papers in PubMed, 21 citations in OpenAlex.

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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 at 1 institution in 1 country.

Zulhelmi IsmailCollege of Engineering (COE), Universiti Malaysia Pahang, Gambang, 26300, Malaysia.
Wan Farhana W IdrisCollege of Engineering (COE), Universiti Malaysia Pahang, Gambang, 26300, Malaysia.
Abu Hannifa AbdullahCentre for Advanced Intelligent Materials, Universiti Malaysia Pahang, Gambang, 26300, Malaysia.
Universiti Malaysia Pahang Al-Sultan Abdullah · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Graphene's potential as material for wearable, highly sensitive and robust sensor in various fields of technology has been widely investigated until now in order to capitalize on its unique intrinsic physical and chemical properties. In the wake of Covid-19 pandemic, it has been noticed that there are various potentials roles that can be fulfilled by graphene-based temperature, humidity and strain sensor, whose roles has not been widely explored to date. This paper takes the liberty to mainly highlight the progress layout and characterization technique for graphene-based sensor while including a brief discussion on the possible strategy of sensing data analysis that can be employed to minimize and prevent the risk of Covid-19 infection within a living community. While majority of the reported sensor is still in the in-progress status, its highlighted role in this work may provide a brief idea on how the ongoing research in graphene-based sensor may lead to the future implementation of the device for routine healthcare check-up and diagnostic point-care during and post-pandemic era. On the other hand, the sensitivity and response time data against working temperature, humidity and strain range that are provided could serve as a reference for benchmarking purpose, which certainly would help enthusiast in the development of a graphene-based sensor with a better performance for the future.

Indexed as

Covid-19GrapheneHumidityStrainTemperature

Identifiers

PMID35633818
PMCPMC9126002
OpenAlexW4281296834

What OpenQuestion holds

Textmetadata
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.