Evidence map›Paper›PMID 37688195›Full record

ArticlePolymers2023

The Optimized Preparation Conditions of Cellulose Triacetate Hollow Fiber Reverse Osmosis Membrane with Response Surface Methodology.

Shu Yang, Kaikai Chen, Hongming Xiang, Yingwen Wang, Chenyan Huang

Abstract read
In one paragraph

Article in Polymers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Shu YangSchool of Textiles and Fashion, Shanghai University of Engineering and Science, Shanghai 201620, China.
Kaikai ChenSchool of Textiles and Fashion, Shanghai University of Engineering and Science, Shanghai 201620, China.
Hongming XiangSchool of Textiles and Fashion, Shanghai University of Engineering and Science, Shanghai 201620, China.
Yingwen WangSchool of Textiles and Fashion, Shanghai University of Engineering and Science, Shanghai 201620, China.
Chenyan HuangSchool of Textiles and Fashion, Shanghai University of Engineering and Science, Shanghai 201620, China.

Funding

China Scholarship Council Grant No. 201508330017National Natural Science Foundation of China Grant Nos. 51403078 and 52103035Natural Science Foundation of Zhejiang Province LY20E030009
6 · The paper itself

Abstract

Reverse osmosis (RO) membrane materials play a key role in determining energy consumption. Currently, CTA is regarded as having one of the highest degrees of chlorine resistance among materials in the RO process. The hollow fiber membrane has the advantages of a large membrane surface area and a preparation process without any redundant processes. Herein, response surface methodology with Box-Behnken Design (BBD) was applied for optimizing the preparation conditions of the cellulose triacetate (CTA) hollow fiber RO membrane. There were four preparation parameters, including solid content, spinning temperature, post-treatment temperature, and post-treatment time, which could affect the permeability of the membrane significantly. In this study, the interaction between preparation parameters and permeability (permeate flux and salt rejection) was evaluated by regression equations. Regression equations can be applied to obtain the optimized preparation parameters of hollow fiber RO membranes and reasonably predict and optimize the permeability of the RO membranes. Finally, the optimized preparation conditions were solid content (44%), spinning temperature (167 °C), post-treatment temperature (79 °C), and post-treatment time (23 min), leading to a permeability of 12.029 (L·m

Indexed as

cellulose triacetatemembraneoptimizationresponse surface methodologyreverse osmosis

Identifiers

PMID37688195
PMCPMC10490516

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.