Evidence map›Paper›PMID 35789839›Full record

ReviewiScience2022

Recent progress in nanomaterial-functionalized membranes for removal of pollutants.

Amit Nain, Arumugam Sangili, Shun-Ruei Hu, Chun-Hsien Chen, Yen-Ling Chen, Huan-Tsung Chang

Open access · goldAbstract readReview
In one paragraph

Review in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
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

9 citing papers in PubMed, 59 citations in OpenAlex.

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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

6 authors at 3 institutions in 1 country.

Amit NainDepartment of Chemistry, National Taiwan University, Taipei 10617, Taiwan.
Arumugam SangiliDepartment of Chemistry, National Taiwan University, Taipei 10617, Taiwan.
Shun-Ruei HuDepartment of Chemistry, National Taiwan University, Taipei 10617, Taiwan.
Chun-Hsien ChenSchool of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 807378, Taiwan.
Yen-Ling ChenSchool of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 807378, Taiwan.
Huan-Tsung ChangDepartment of Chemistry, National Taiwan University, Taipei 10617, Taiwan.
National Taiwan University · TWKaohsiung Medical University · TWNational Chung Cheng University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Membrane technology has gained tremendous attention for removing pollutants from wastewater, mainly due to their affordable capital cost, miniature equipment size, low energy consumption, and high efficiency even for the pollutants present in lower concentrations. In this paper, we review the literature to summarize the progress of nanomaterial-modified membranes for wastewater treatment applications. Introduction of nanomaterial in the polymeric matrix influences membrane properties such as surface roughness, hydrophobicity, porosity, and fouling resistance. This review also covers the importance of functionalization strategies to prepare thin-film nanocomposite hybrid membranes and their effect on eliminating pollutants. Systematic discussion regarding the impact of the nanomaterials incorporated within membrane, toward the recovery of various pollutants such as metal ions, organic compounds, dyes, and microbes. Successful examples are provided to show the potential of nanomaterial-functionalized membranes for regeneration of wastewater. In the end, future prospects are discussed to develop nanomaterial-based membrane technology.

Indexed as

Environmental scienceMembranesNanomaterials

Identifiers

PMID35789839
PMCPMC9250028
OpenAlexW4282976101

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.