Evidence map›Paper›PMID 35448251›Full record

ReviewBiosensors2022

Lab-on-a-Chip Platforms for Airborne Particulate Matter Applications: A Review of Current Perspectives.

Sharon Ezrre, Marco A Reyna, Citlalli Anguiano, Roberto L Avitia, Heriberto Márquez

Abstract readReview
In one paragraph

Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
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.

Sharon EzrreInstituto de Ingeniería, Universidad Autónoma de Baja California (UABC), Mexicali 21100, Mexico.ORCID 0000-0002-3609-4470
Marco A ReynaInstituto de Ingeniería, Universidad Autónoma de Baja California (UABC), Mexicali 21100, Mexico.
Citlalli AnguianoFacultad de Ingeniería, Universidad Autónoma de Baja California (UABC), Mexicali 21280, Mexico.
Roberto L AvitiaFacultad de Ingeniería, Universidad Autónoma de Baja California (UABC), Mexicali 21280, Mexico.ORCID 0000-0003-3615-6560
Heriberto MárquezDepartamento de Óptica, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada 22860, Mexico.ORCID 0000-0002-1831-4759

Funding

Consejo Nacional de Ciencia y Tecnología 764723
6 · The paper itself

Abstract

Lab-on-a-Chip (LoC) devices are described as versatile, fast, accurate, and low-cost platforms for the handling, detection, characterization, and analysis of a wide range of suspended particles in water-based environments. However, for gas-based applications, particularly in atmospheric aerosols science, LoC platforms are rarely developed. This review summarizes emerging LoC devices for the classification, measurement, and identification of airborne particles, especially those known as Particulate Matter (PM), which are linked to increased morbidity and mortality levels from cardiovascular and respiratory diseases. For these devices, their operating principles and performance parameters are introduced and compared while highlighting their advantages and disadvantages. Discussing the current applications will allow us to identify challenges and determine future directions for developing more robust LoC devices to monitor and analyze airborne PM.

Indexed as

Lab-On-A-Chip DevicesParticulate MatterEnvironmental MonitoringParticle SizeParticulate Matterairborne particulate matterLab-on-a-Chipparticle analysisparticle manipulationparticle monitoring

Identifiers

PMID35448251
PMCPMC9024784

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.