Evidence map›Paper›PMID 32650577›Full record

ReviewMicromachines2020

Recent Developments of PFAS-Detecting Sensors and Future Direction: A Review.

Kelsey L Rodriguez, Jae-Hoon Hwang, Amirsalar R Esfahani, A H M Anwar Sadmani, Woo Hyoung Lee

Abstract readReview
In one paragraph

Review in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 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

5 authors.

Kelsey L RodriguezDepartment of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
Jae-Hoon HwangDepartment of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.ORCID 0000-0002-2010-0864
Amirsalar R EsfahaniDepartment of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.ORCID 0000-0002-2342-4522
A H M Anwar SadmaniDepartment of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
Woo Hyoung LeeDepartment of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.ORCID 0000-0002-5298-2371

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Per- and poly-fluoroalkyl substances (PFASs) have recently been labeled as toxic constituents that exist in many aqueous environments. However, traditional methods used to determine the level of PFASs are often not appropriate for continuous environmental monitoring and management. Based on the current state of research, PFAS-detecting sensors have surfaced as a promising method of determination. These sensors are an innovative solution with characteristics that allow for in situ, low-cost, and easy-to-use capabilities. This paper presents a comprehensive review of the recent developments in PFAS-detecting sensors, and why the literature on determination methods has shifted in this direction compared to the traditional methods used. PFAS-detecting sensors discussed herein are primarily categorized in terms of the detection mechanism used. The topics covered also include the current limitations, as well as insight on the future direction of PFAS analyses. This paper is expected to be useful for the smart sensing technology development of PFAS detection methods and the associated environmental management best practices in smart cities of the future.

Indexed as

per- and poly-fluoroalkyl substances (PFASs)PFAS detection sensorsmart citiessmart sensing technology

Identifiers

PMID32650577
PMCPMC7407801

What OpenQuestion holds

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Registered trials

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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.