All-in-one Transparent Cellulose-Based Composite Membrane for Simultaneous Colorimetric Detection and Photocatalytic Degradation of Amine VOCs
SEPARATION AND PURIFICATION TECHNOLOGY（2025）
Abstract
Developing high-performance multifunctional catalyst that adept at both sensitive detecting and efficient degrading of volatile organic compounds (VOCs) is of significant importance for human and eco safety. This work presented a bifunctional nonmetal composite membrane by functionalizing transparent cellulose membrane with double responsive bromocresol green and catalytic g-C3N4/COFTAPB-PDA (CNC) heterojunctions for simultaneous detection and degradation of amine VOCs. Bromocresol green possesses both intrinsic visualized acid-base responsiveness and wide light adsorption range. Hierarchical CNC with high specific surface area (up to 1256.60 m(2) g(-1)) can effectively promote the absorption and diffusion of substrates and the separation of photo-generated carriers. A flexible macroporous transparent cellulose membrane was applied as carrier to alleviate the "self-masking effect" of the catalyst inside to improve light utilization. As a result, the composite membrane showing a visually reversible color change from yellow to blue for amine VOCs and achieving rapid quantitative detection of representative triethylamine (detection time of 5 s with a LOD of 0.32 ppm) on the aid of a smart phone. The degradation and mineralization efficiency of triethylamine reached 97.3 % and 73.5 % within 180 min, respectively. The ingenious design of integrating bifunctional cellulose composite material provides feasibility for the rapid identification and efficient removal of VOCs.
MoreTranslated text
Key words
Bifunctional composite membrane,g-C3N4/COFTAPB-PDA heterojunction,Colorimetric detection,Photocatalytic degradation,Volatile organic compounds
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