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Ultrathin, ultralight dual-scale fibrous networks with high-infrared transmittance for high-performance, comfortable and sustainable PM0.3 filter

Yuchen Yang, Xiangshun Li, Zhiyong Zhou, Qiaohua Qiu, Wenjing Chen, Jianying Huang, Weilong Cai, Xiaohong Qin, Yuekun Lai

2024Nature Communications98 citationsDOIOpen Access PDF

Abstract

Abstract Highly permeable particulate matter (PM) can carry various bacteria, viruses and toxics and pose a serious threat to public health. Nevertheless, current respirators typically sacrifice their thickness and base weight for high-performance filtration, which inevitably causes wearing discomfort and significant consumption of raw materials. Here, we show a facile yet massive splitting eletrospinning strategy to prepare an ultrathin, ultralight and radiative cooling dual-scale fiber membrane with about 80% infrared transmittance for high-protective, comfortable and sustainable air filter. By tailoring antibacterial surfactant-triggered splitting of charged jets, the dual-scale fibrous filter consisting of continuous nanofibers (44 ± 12 nm) and submicron-fibers (159 ± 32 nm) is formed. It presents ultralow thickness (1.49 μm) and base weight (0.57 g m −2 ) but superior protective performances (about 99.95% PM 0.3 removal, durable antibacterial ability) and wearing comfort of low air resistance, high heat dissipation and moisture permeability. Moreover, the ultralight filter can save over 97% polymers than commercial N95 respirator, enabling itself to be sustainable and economical. This work paves the way for designing advanced and sustainable protective materials.

Topics & Concepts

RespiratorMaterials scienceTransmittanceNanofiberAerogelFiltration (mathematics)Thermal management of electronic devices and systemsComposite materialNanotechnologyFilter (signal processing)OptoelectronicsMechanical engineeringComputer scienceComputer visionStatisticsEngineeringMathematicsAerosol Filtration and Electrostatic PrecipitationInfection Control and VentilationAerodynamics and Fluid Dynamics Research