Evidence map›Paper›PMID 34677340›Full record

ReviewBiosensors2021

Application of Functionalized Graphene Oxide Based Biosensors for Health Monitoring: Simple Graphene Derivatives to 3D Printed Platforms.

Agnivo Gosai, Kamil Reza Khondakar, Xiao Ma, Md Azahar Ali

Abstract readReview
In one paragraph

Review in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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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

4 authors.

Agnivo GosaiCorning Inc., Science & Technology, Painted Post, NY 14870, USA.ORCID 0000-0002-5146-509X
Kamil Reza KhondakarDepartment of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA.
Xiao MaDepartment of Biomedical Engineering, New York University, Brooklyn, NY 11201, USA.
Md Azahar AliDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15235, USA.ORCID 0000-0001-5752-8808

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biosensors hold great potential for revolutionizing personalized medicine and environmental monitoring. Their construction is the key factor which depends on either manufacturing techniques or robust sensing materials to improve efficacy of the device. Functional graphene is an attractive choice for transducing material due to its various advantages in interfacing with biorecognition elements. Graphene and its derivatives such as graphene oxide (GO) are thus being used extensively for biosensors for monitoring of diseases. In addition, graphene can be patterned to a variety of structures and is incorporated into biosensor devices such as microfluidic devices and electrochemical and plasmonic sensors. Among biosensing materials, GO is gaining much attention due to its easy synthesis process and patternable features, high functionality, and high electron transfer properties with a large surface area leading to sensitive point-of-use applications. Considering demand and recent challenges, this perspective review is an attempt to describe state-of-the-art biosensors based on functional graphene. Special emphasis is given to elucidating the mechanism of sensing while discussing different applications. Further, we describe the future prospects of functional GO-based biosensors for health care and environmental monitoring with a focus on additive manufacturing such as 3D printing.

Indexed as

Biosensing TechniquesGraphitePrinting, Three-DimensionalElectrochemical Techniquesgraphene oxideGraphite3D Printingbiosensorfunctionalized graphenegraphenegraphene oxide

Identifiers

PMID34677340
PMCPMC8533804

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.