Evidence map›Paper›PMID 42042990›Full record

ReviewMembranes2026

Mini Review on Nanomaterial-Driven Substrate Optimization of Polyamide Thin-Film Nanocomposite Membranes for FO, RO and NF Applications.

Shabin Mohammed, Ahmed Elmekawy, Ranwen Ou, Hanaa M Hegab

Abstract readReview
In one paragraph

Review in Membranes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

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.

Shabin MohammedFaculty of Engineering, Higher Colleges of Technology, Abu Dhabi 25035, United Arab Emirates.
Ahmed ElmekawyFaculty of Engineering, Higher Colleges of Technology, Abu Dhabi 25035, United Arab Emirates.
Ranwen OuCollege of the Environment & Ecology, Xiamen University, Xiamen 361102, China.
Hanaa M HegabFaculty of Engineering, Higher Colleges of Technology, Abu Dhabi 25035, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rising demand for clean water has reinforced the importance of thin-film composite TFC polyamide membranes in desalination and wastewater treatment. While improvements often target the selective layer, these can sometimes reduce stability or selectivity. An alternative approach is to tailor the porous support, particularly through the incorporation of nanomaterials such as metal oxides, carbon-based nanomaterials, metal-organic frameworks (MOFs), zeolites, and cellulose-based materials, to improve overall membrane performance. The modification of membrane substrates through the incorporation of nanofillers has demonstrated notable advantages, including enhanced hydrophilicity, improved mechanical stability, and increased porosity. These improvements collectively contribute to higher permeability, reduced internal concentration polarization and enhanced separation performance in FO, NF, and RO applications. The review starts by clearly distinguishing substrate modification, in which nanomaterials are localized in the porous support, from interlayer modification, which involves constructing a distinct layer between the support and selective layer. This concise review highlights current developments in the nanomaterial-based support modification of polyamide TFC membranes; it summarizes nanomaterials selections, incorporation techniques, and resulting property changes. Current challenges and potential research opportunities are also discussed.

Indexed as

FOmembranenanomaterialsNFpolyamideROTFC

Identifiers

PMID42042990
PMCPMC13117665

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.