Evidence map›Paper›PMID 32629862›Full record

ReviewPolymers2020

Fabrication, Properties, Performances, and Separation Application of Polymeric Pervaporation Membranes: A Review.

Luchen Wang, Yan Wang, Lianying Wu, Gang Wei

Abstract readReview
In one paragraph

Review in Polymers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
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.

Luchen WangCollege of Applied Technology, Qingdao University, Qingdao 266100, China.
Yan WangCollege of Chemistry and Chemical Engineering, Qingdao University, Qingdao 266071, China.
Lianying WuCollege of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China.
Gang WeiCollege of Chemistry and Chemical Engineering, Qingdao University, Qingdao 266071, China.ORCID 0000-0002-3838-8659

Funding

National Natural Science Foundation of China 21808116National Natural Science Foundation of China 51873225
6 · The paper itself

Abstract

Membrane separation technologies have attracted great attentions in chemical engineering, food science, analytical science, and environmental science. Compared to traditional membrane separation techniques like reverse osmosis (RO), ultrafiltration (UF), electrodialysis (ED) and others, pervaporation (PV)-based membrane separation shows not only mutual advantages such as small floor area, simplicity, and flexibility, but also unique characteristics including low cost as well as high energy and separation efficiency. Recently, different polymer, ceramic and composite membranes have shown promising separation applications through the PV-based techniques. To show the importance of PV for membrane separation applications, we present recent advances in the fabrication, properties and performances of polymeric membranes for PV separation of various chemicals in petrochemical, desalination, medicine, food, environmental protection, and other industrial fields. To promote the easy understanding of readers, the preparation methods and the PV separation mechanisms of various polymer membranes are introduced and discussed in detail. This work will be helpful for developing novel functional polymer-based membranes and facile techniques to promote the applications of PV techniques in different fields.

Indexed as

environmental sciencehybrid materialspervaporationpolymeric membraneseparation techniques

Identifiers

PMID32629862
PMCPMC7408584

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.