Evidence map›Paper›PMID 39927791›Full record

ArticleACS applied materials & interfaces2025

Cationic Nanoparticle Networks (CNNs) with Remarkably Efficient, Simultaneous Adsorption of Microplastics and PFAS.

Shayesteh Tafazoli, Dylan B Shuster, Ali Shahrokhinia, Sahaj Rijal, Dorcas M Ruhamya, Kamryn A Dubray, David J Morefield, James F Reuther

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In one paragraph

Article in ACS applied materials & interfaces, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Shayesteh TafazoliDepartment of Chemistry, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.
Dylan B ShusterDepartment of Chemistry, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.
Ali ShahrokhiniaDepartment of Chemistry, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.
Sahaj RijalDepartment of Chemistry, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.
Dorcas M RuhamyaDepartment of Chemistry, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.
Kamryn A DubrayDepartment of Chemistry, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.
David J MorefieldDepartment of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States.
James F ReutherDepartment of Chemistry, University of Massachusetts Lowell, Lowell, Massachusetts 01854, United States.ORCID 0000-0001-5611-0290

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Of the past decade, micro/nanoplastics (MP/NP) and per- and polyfluoroalkyl substances (PFAS) have become two of the most pervasive persistent organic pollutants leading to significant accumulation within waterways. Various sorbent materials have been evaluated for PFAS and MP/NP removal, but their simultaneous removal has rarely been explored. Herein, we report a library of polymer-based, cationic nanoparticle networks (CNN) with systematic variation in surface charge density, polymer molecular weight, and nanoparticle size for the removal of anionic MP/NP and PFAS from aqueous solutions. These materials are synthesized in three, one-pot steps starting with polymerization-induced self-assembly (PISA) followed by rapid photocuring and quaternary ammonium salt formation resulting in 3D networks consisting solely of cationic polymer nanoparticles. Our best performing CNN material demonstrated record-high MP removal capacities of

Indexed as

adsorptioncationic gelsmicroplasticsPFASpolymerization-induced self-assembly

Identifiers

PMID39927791
PMCPMC11843541

What OpenQuestion holds

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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.