A Superwetting Rough Structured Nanofibrous Membrane with Enhancing Anti-Fouling Performance for Oil–water Separation
SEPARATION AND PURIFICATION TECHNOLOGY(2025)
Zhejiang Sci Tech Univ
Abstract
Oily wastewater generated from many industries has become a tremendous potential threat to ecological balance and human health. However, severely hindered by interfacial wettability, the present oil/water separation membranes are subjected to a serious oil fouling problem during long-term oily wastewater separation. Herein, we construct superwetting rough structured nanofibrous membrane (SRSNM) composed of superhydrophilic PVA/SA bead-on-string nanofibrous functional layer and PVA nanofibrous substrate by two step electrospinning together with chemical crosslinking. Benefitting from the inherent hydrophilic properties of SA and PVA combined with the rough structure endowed by the bead-on-string nanofibers, the SRSNMs exhibited fascinating superhydrophilicity and underwater superoleophobicity. Meanwhile, thanks to the submicron pores and high porosity, the SRSNM could separate submicron oil-in-water emulsions with robust permeation flux of 1319 L m(-2)h(-1) and high separation efficiency of 99.9 % under the gravity driving (similar to 1 kPa). More importantly, this SRSNM showed excellent anti-oil-fouling performance with high flux recovery of 99 %, negligible irreversible oil fouling rate of 1 %, and intriguing reusability for the long-term separation. The design of such superwetting rough structured nanofibrous membrane may provide an efficacious strategy for synthesizing high-performance separation membranes.
MoreTranslated text
Key words
Bead-on-string structure,Nanofiber,Superwetting surface,Anti-fouling,Oil-water separation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined