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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Influence of rGO/MWCNTs on antifouling properties of nanocomposite membrane: Statistical analysis

Goh, Theng, Ho, Kah, Teow, Yeit and McGarry, Kenneth (2023) Influence of rGO/MWCNTs on antifouling properties of nanocomposite membrane: Statistical analysis. In: INTERNATIONAL CONFERENCE ON SUSTAINABLE ENGINEERING AND ADVANCED TECHNOLOGY: ICSEAT, 16–17 Jun 2022, Kota Damansara, Malaysia.

Item Type: Conference or Workshop Item (Paper)


The research aimed to develop a statistical analysis of the antifouling properties of nanocomposite membrane using response surface methodology (RSM) for methyl orange (MO) dye wastewater treatment. Reduced graphene oxide (rGO) and multi-walled carbon nanotubes (MWCNTs) were blended directly into polyvinylidene difluoride (PVDF) membrane. 13 experiment runs were conducted using central composite design (CCD). Nanomaterials concentration and weight ratio of rGO:MWCNTs were manipulated, while the response was normalized flux. Next, the membranes were characterized by pore size, porosity, surface charge, and hydrophilicity. In general, the increase in membrane pore size was mainly due to the addition of nanomaterials. Lastly, the membrane performance was evaluated using dead-end filtration. The best antifouling was achieved by membrane containing 0.023 wt% nanomaterial concentration and 85.36 wt% weight ratio with the highest normalized flux of 0.9132. The antifouling properties of nanocomposite membrane were then modeled statistically using analysis of variance (ANOVA) and regression analysis to identify the data significance. Lastly, a quadratic model equation was rendered and it is found that normalized flux was significantly affected by nanomaterials concentration. The model was proven to be fitted with moderate accuracy based on the F-value (4.09), p-values (0.0468 <0.05), lack of fit F-value (2.81), R2 value (0.7449), and standard deviation (0.0946).

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More Information

Additional Information: 10.1063/5.0165317
Depositing User: Kenneth McGarry


Item ID: 17173
Official URL:

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD

Catalogue record

Date Deposited: 09 Jan 2024 13:30
Last Modified: 09 Jan 2024 13:30


Author: Kenneth McGarry ORCID iD
Author: Theng Goh
Author: Kah Ho
Author: Yeit Teow

University Divisions

Faculty of Technology > School of Computer Science


Computing > Data Science
Engineering > Mathematics (Engineering)

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