Evidence map›Paper›PMID 29152029›Full record

ArticleBiomicrofluidics2017

Portable detection of trace metals in airborne particulates and sediments

Yuan Jia, Hui Dong, Jianping Zheng, Hao Sun

Open access · greenAbstract read
In one paragraph

Article in Biomicrofluidics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

4 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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

4 authors at 3 institutions in 1 country.

Yuan JiaSchool of Mechanical Engineering, Southeast University, Nanjing 210096, China.
Hui Dong
Jianping ZhengDepartment of Medical Oncology, Fujian Provincial Hospital, Fujian 350001, China.
Hao Sun
Fuzhou University · CNFujian Provincial Hospital · CNSoutheast University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Particulate matter (PM), a key indicator of air pollution by natural and anthropogenic activities, contributes to a wide spectrum of diseases that lead to a shortening of life expectancy. It has been recognized that trace metals in airborne PM are highly toxic and can be correlated with lesion in respiratory, gastrointestinal, immunological, and hematological systems. Traditional methods for trace metal assay require sophisticated instrumentations and highly trained operators in centralized laboratories. In this work, by integrating the technologies of microfluidic paper-based analytical devices, additive manufacturing, smartphone, and colorimetric sensing, we developed the first smartphone based paper microfluidic platform for portable, disposable, and quantitative measurements of cobalt (Co), copper (Cu), and iron (Fe) in ambient air and street sediments. On a single A4-sized paper, 48 devices were fabricated in under 30 s with a total cost of ∼$1.9. On each device, 12 reaction units were patterned and used for colorimetric tests. Particulate samples from urban ambient air and street sediments were collected, processed, and analyzed. Signals of the on-chip complexation product were recorded using a smartphone camera and processed by a self-developed app on an iOS system. For precisely controlling the object distance, chip position, and luminance, a hand-held 3D cellphone housing was designed and printed. The detection limits of Co, Cu, and Fe were determined to be 8.2, 45.8, and 186.0 ng, while the linear dynamic ranges were calculated to be 8.2-81.6, 45.8-4.58 × 10

Identifiers

PMID29152029
PMCPMC5680047
OpenAlexW2768030298

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.