Evidence map›Paper›PMID 40862935›Full record

ReviewBiosensors2025

Microfluidic Sensors for Micropollutant Detection in Environmental Matrices: Recent Advances and Prospects.

Mohamed A A Abdelhamid, Mi-Ran Ki, Hyo Jik Yoon, Seung Pil Pack

Abstract readReview
In one paragraph

Review in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Review
  8. 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

4 authors.

Mohamed A A AbdelhamidFaculty of Education and Arts, Sohar University, Sohar 311, Oman.ORCID 0000-0001-5751-0923
Mi-Ran KiDepartment of Biotechnology and Bioinformatics, Korea University, Sejong-ro 2511, Sejong 30019, Republic of Korea.ORCID 0000-0003-1501-7166
Hyo Jik YoonInstitute of Natural Science, Korea University, Sejong-ro 2511, Sejong 30019, Republic of Korea.
Seung Pil PackDepartment of Biotechnology and Bioinformatics, Korea University, Sejong-ro 2511, Sejong 30019, Republic of Korea.

Funding

This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) RS-2021-NR059450This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) RS-2021-NR060107
6 · The paper itself

Abstract

The widespread and persistent occurrence of micropollutants-such as pesticides, pharmaceuticals, heavy metals, personal care products, microplastics, and per- and polyfluoroalkyl substances (PFAS)-has emerged as a critical environmental and public health concern, necessitating the development of highly sensitive, selective, and field-deployable detection technologies. Microfluidic sensors, including biosensors, have gained prominence as versatile and transformative tools for real-time environmental monitoring, enabling precise and rapid detection of trace-level contaminants in complex environmental matrices. Their miniaturized design, low reagent consumption, and compatibility with portable and smartphone-assisted platforms make them particularly suited for on-site applications. Recent breakthroughs in nanomaterials, synthetic recognition elements (e.g., aptamers and molecularly imprinted polymers), and enzyme-free detection strategies have significantly enhanced the performance of these biosensors in terms of sensitivity, specificity, and multiplexing capabilities. Moreover, the integration of artificial intelligence (AI) and machine learning algorithms into microfluidic platforms has opened new frontiers in data analysis, enabling automated signal processing, anomaly detection, and adaptive calibration for improved diagnostic accuracy and reliability. This review presents a comprehensive overview of cutting-edge microfluidic sensor technologies for micropollutant detection, emphasizing fabrication strategies, sensing mechanisms, and their application across diverse pollutant categories. We also address current challenges, such as device robustness, scalability, and potential signal interference, while highlighting emerging solutions including biodegradable substrates, modular integration, and AI-driven interpretive frameworks. Collectively, these innovations underscore the potential of microfluidic sensors to redefine environmental diagnostics and advance sustainable pollution monitoring and management strategies.

Indexed as

Biosensing TechniquesEnvironmental MonitoringEnvironmental PollutantsMicrofluidic Analytical TechniquesMicrofluidicsPesticidesWater Pollutants, ChemicalEnvironmental PollutantsPesticidesWater Pollutants, Chemicalartificial intelligenceenvironmental monitoringlab-on-a-chipmachine learningmicrofluidic sensorsmicropollutantsnanomaterialspoint-of-care diagnostics

Identifiers

PMID40862935
PMCPMC12384914

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.