WeChat Mini Program
Old Version Features

Ultralight Electrospun Composite Filters with Vertical Ternary Spatial Network for High-Performance PM0.3 Purification

ADVANCED MATERIALS(2025)

Donghua Univ

Cited 0|Views11
Abstract
Air pollutants, particularly highly permeable particulate matter (PM), threaten public health and environmental sustainability due to extensive filter media consumption. Existing melt-blown nonwoven filters struggle with PM0.3 removal, energy consumption, and disposal burdens. Here, an ultralight composite filter with a vertical ternary spatial network (TSN) structure that saves approximate to 98% of raw material usage and reduces fabrication time by 99.4%, while simultaneously achieving high-efficiency PM0.3 removal (>= 99.92%), eco-friendly regeneration (near-zero energy consumption), and enhanced wearing comfort (breathability >80 mm s(-)(1), infrared transmittance >85%), is reported. The TSN filter consists of a hybrid layer of microspheres (average diameter approximate to 1 mu m)/superfine nanofibers (approximate to 20 nm) sandwiched between two nanofiber scaffolds (diameter approximate to 400 nm and approximate to 100 nm). This arrangement offers high porosity (approximate to 85%), ultralow areal density (<1 g m(-2)), alow airflow resistance (<90 Pa), guaranteeing superb thermal comfort. Notably, utilizing scalable one-step free surface electrospinning technology, TSN mats can be mass-produced at a rate of 60 meters per hour (width of 1.6 meters), which is critical and verified for various applications including window screens, individual respiratory protectors, and dust collectors. This work provides a viable strategy for designing high-performance nanofiber filter media through structural regulation in a scalable, cost-effective, and sustainable way.
More
Translated text
Key words
air purification,free surface electrospinning,nanofiber filters,personal protection,ternary spatial network structure
求助PDF
上传PDF
Bibtex
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