Evidence map›Paper›PMID 42245990›Full record

ReviewRSC advances2026

Continuous sample drop flow microextraction: a versatile and green microextraction tool in analytical chemistry.

Anwar Rasheed Yaqoub, Sameera Sh Mohammed Ameen, Anwar H Abdullah, Khalid M Omer

Erratum issuedAbstract readReview
In one paragraph

Review in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Anwar Rasheed YaqoubDepartment of Pharmacy, College of Pharmacy, Nawroz University 42001 Duhok Kurdistan Region Iraq.
Sameera Sh Mohammed AmeenDepartment of Chemistry, College of Science, University of Zakho 42002 Zakho Kurdistan Region Iraq.ORCID https://orcid.org/0009-0002-3990-5328
Anwar H AbdullahDepartment of Chemistry, College of Science, Duhok University 42001 Duhok Kurdistan Region Iraq.
Khalid M OmerDepartment of Chemistry, College of Science, University of Sulaimani 46002 Sulaymaniyah Kurdistan Region Iraq khalid.omer@univsul.edu.iq.ORCID https://orcid.org/0000-0002-6866-4116

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Continuous sample drop-flow microextraction (CSDF-ME) has become a powerful and versatile liquid-phase microextraction methodology that overcomes several of the analytical and environmental shortcomings of traditional sample preparation procedures. In CSDF-ME, the aqueous sample is continuously added to a small, stagnant volume of extraction solvent, resulting in large renewed interfacial areas that favor efficient mass transfer, high enrichment factors, and good reproducibility, using only microliter volumes of solvent. Despite its growing importance and increasing range of applications, an independent and comprehensive review dedicated exclusively to CSDF-ME is still lacking; therefore, a critical and focused overview of this technique is highly necessary. This review provides a comprehensive overview of CSDF-ME, covering its working principles, method development and optimization strategies, analytical performance and validation, as well as its applications in environmental, food, biological, and pharmaceutical analyses. Special focus is given to recent advances in green extraction methods, including the use of fatty acids and deep eutectic solvents. The existing constraints, such as automation and solid-sample processing, are addressed, and the prospects for further evolution and broader use of CSDF-ME are outlined. In general, CSDF-ME has proven to be an effective and environmentally friendly sample preparation method in modern analytical chemistry.

Identifiers

PMID42245990
PMCPMC13231437

What OpenQuestion holds

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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.