Evidence map›Paper›PMID 29510538›Full record

ArticleSensors (Basel, Switzerland)2018

Miniaturized Sample Preparation and Rapid Detection of Arsenite in Contaminated Soil Using a Smartphone.

Mohd Farhan Siddiqui, Soocheol Kim, Hyoil Jeon, Taeho Kim, Chulmin Joo, Seungkyung Park

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
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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

6 authors.

Mohd Farhan SiddiquiSchool of Mechanical Engineering, Korea University of Technology and Education, Cheonan 31253, Korea. mohdfarhan@koreatech.ac.kr.
Soocheol KimSchool of Mechanical Engineering, Yonsei University, Seoul 03722, Korea. improvely@naver.com.
Hyoil JeonSchool of Mechanical Engineering, Korea University of Technology and Education, Cheonan 31253, Korea. wjsgydlf@koreatech.ac.kr.
Taeho KimSchool of Mechanical Engineering, Korea University of Technology and Education, Cheonan 31253, Korea. taeho4111@koreatech.ac.kr.
Chulmin JooSchool of Mechanical Engineering, Yonsei University, Seoul 03722, Korea. cjoo@yonsei.ac.kr.
Seungkyung ParkSchool of Mechanical Engineering, Korea University of Technology and Education, Cheonan 31253, Korea. spark@koreatech.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional methods for analyzing heavy metal contamination in soil and water generally require laboratory equipped instruments, complex procedures, skilled personnel and a significant amount of time. With the advancement in computing and multitasking performances, smartphone-based sensors potentially allow the transition of the laboratory-based analytical processes to field applicable, simple methods. In the present work, we demonstrate the novel miniaturized setup for simultaneous sample preparation and smartphone-based optical sensing of arsenic As(III) in the contaminated soil. Colorimetric detection protocol utilizing aptamers, gold nanoparticles and NaCl have been optimized and tested on the PDMS-chip to obtain the high sensitivity with the limit of detection of 0.71 ppm (in the sample) and a correlation coefficient of 0.98. The performance of the device is further demonstrated through the comparative analysis of arsenic-spiked soil samples with standard laboratory method, and a good agreement with a correlation coefficient of 0.9917 and the average difference of 0.37 ppm, are experimentally achieved. With the android application on the device to run the experiment, the whole process from sample preparation to detection is completed within 3 hours without the necessity of skilled personnel. The approximate cost of setup is estimated around 1 USD, weight 55 g. Therefore, the presented method offers the simple, rapid, portable and cost-effective means for onsite sensing of arsenic in soil. Combined with the geometric information inside the smartphones, the system will allow the monitoring of the contamination status of soils in a nation-wide manner.

Indexed as

biosensorcolorimetric detectionminiaturizedsmartphonesoil arsenic monitoring

Identifiers

PMID29510538
PMCPMC5877113

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LicenceCC BY
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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.