Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 164

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Anti-Fouling Behavior of Hyperbranched Polyglycerol-Grafted Poly(ether sulfone) Hollow Fiber Membranes for Osmotic Power Generation

Li, Xue, Cai, Tao, Chung, Tai-Shung·2014·Environmental Science & Technology

Abstract

To sustain high performance of osmotic power generation by pressure-retarded osmosis (PRO) processes, fouling on PRO membranes must be mitigated. This is especially true for the porous support of PRO membranes because its porous structure is very prone to fouling by feeding river water. For the first time, we have successfully designed antifouling PRO thin-film composite (TFC) membranes by synthesizing a dendritic hydrophilic polymer with well-controlled grafting sites, hyperbranched polyglycerol (HPG), and then grafting it on poly(ether sulfone) (PES) hollow fiber membrane supports. Compared to the pristine PES membranes, polydopamine modified membranes, and conventional poly(ethylene glycol) (PEG)-grafted membranes, the HPG grafted membranes show much superior fouling resistance against bovine serum albumin (BSA) adsorption, E. coli adhesion, and S. aureus attachment. In high-pressure PRO tests, the PES TFC membranes are badly fouled by model protein foulants, causing a water flux decline of 31%. In comparison, the PES TFC membrane grafted by HPG not only has an inherently higher water flux and a higher power density but also exhibits better flux recovery up to 94% after cleaning and hydraulic pressure impulsion. Clearly, by grafting the properly designed dendritic polymers to the membrane support, one may substantially sustain PRO hollow fiber membranes for power generation.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/es5017262

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 164

Metadata

Authors
Li, X., Cai, T., & Chung, T.-S.
Venue
Environmental Science & Technology
Published
2014-07-24
Volume
48
Issue
16
Pages
9898-9907
ISSN
  • 0013-936X (ISSN-L)
  • 1520-5851
OpenAlex
W2328396510
Cited by
164
References
4
FWCI
17.59
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.