Evidence map›Paper›PMID 39820249›Full record

ArticleMicrosystems & nanoengineering2025

Optofluidic paper-based analytical device for discriminative detection of organic substances via digital color coding.

Jinsol Choi, Chi Yeung Oh, Gong Qian, Tae Soup Shim, Heon-Ho Jeong

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2025. 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
–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. Review
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Jinsol Choi *Department of Chemical and Biomolecular Engineering, Chonnam National University, 50 Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea.
Chi Yeung Oh *Department of Energy Systems Research, Ajou University, 206 World cup-ro, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.
Gong QianDepartment of Chemical and Biomolecular Engineering, Chonnam National University, 50 Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea.
Tae Soup ShimDepartment of Energy Systems Research, Ajou University, 206 World cup-ro, Suwon-si, Gyeonggi-do, 16499, Republic of Korea. tsshim@ajou.ac.kr.ORCID http://orcid.org/0000-0003-2414-1599
Heon-Ho JeongDepartment of Chemical and Biomolecular Engineering, Chonnam National University, 50 Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea. jeonghh29@jnu.ac.kr.ORCID http://orcid.org/0000-0003-0475-3483

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developing a portable yet affordable method for the discrimination of chemical substances with good sensitivity and selectivity is essential for on-site visual detection of unknown substances. Herein, we propose an optofluidic paper-based analytical device (PAD) that consists of a macromolecule-driven flow (MDF) gate and photonic crystal (PhC) coding units, enabling portable and scalable detection and discrimination of various organic chemical, mimicking the olfactory system. The MDF gate is designed for precise flow control of liquid analytes, which depends on intermolecular interactions between the polymer at the MDF gate and the liquid analytes. Subsequently, the PhC coding unit allows for visualizing the result obtained from the MDF gate and generating differential optical patterns. We fabricate an optofluidic PAD by integrating two coding units into a three-dimensional (3D) microfluidic paper within a 3D-printed cartridge. The optofluidic PADs clearly distinguish 11 organic chemicals with digital readout of pattern recognition from colorimetric signals. We believe that our optofluidic coding strategy mimicking the olfactory system opens up a wide range of potential applications in colorimetric monitoring of chemicals observed in environment.

Identifiers

PMID39820249
PMCPMC11739424

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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.