Evidence map›Paper›PMID 33191443›Full record

ReviewNano convergence2020

Heavy metal removal applications using adsorptive membranes.

Thi Sinh Vo, Muhammad Mohsin Hossain, Hyung Mo Jeong, Kyunghoon Kim

Abstract readReview
In one paragraph

Review in Nano convergence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
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  7. ProcessingInternational journal of molecular sciences · 2025
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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

4 authors.

Thi Sinh VoSchool of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Muhammad Mohsin HossainSchool of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Hyung Mo JeongSchool of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Kyunghoon KimSchool of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea. kenkim@skku.edu.ORCID http://orcid.org/0000-0001-8034-947X

Funding

National Research Foundation of Korea NRF-2020R1A2C4002557
6 · The paper itself

Abstract

Water is a significant natural resource for humans. As such, wastewater containing heavy metals is seen as a grave problem for the environment. Currently, adsorption is one of the common methods used for both water purification and wastewater treatment. Adsorption relies on the physical and chemical interactions between heavy metal ions and adsorbents. Adsorptive membranes (AMs) have demonstrated high effectiveness in heavy metal removal from wastewater owing to their exclusive structural properties. This article examines the applications of adsorptive membranes such as polymeric membranes (PMs), polymer-ceramic membranes (PCMs), electrospinning nanofiber membranes (ENMs), and nano-enhanced membranes (NEMs), which demonstrate high selectivity and adsorption capacity for heavy metal ions, as well as both advantages and disadvantages of each one all, are summarized and compared shortly. Moreover, the general theories for both adsorption isotherms and adsorption kinetics are described briefly to comprehend the adsorption process. This work will be valuable to readers in understanding the current applications of various AMs and their mechanisms in heavy metal ion adsorption, as well as the recycling methods in heavy ions desorption process are summarized and described clearly. Besides, the influences of morphological and chemical structures of AMs are presented and described in detail as well.

Indexed as

AdsorptionAdsorptive membraneElectrospinning membraneHeavy metal removalNano-enhanced membranePolymer-ceramic membranePolymeric membrane

Identifiers

PMID33191443
PMCPMC7667210

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.