Controllable Preparation and Filtration Performance of New Composite Materials for Mining Masks
Year: 2026
Type: article
Abstract: The effective isolation of high-concentration dust during mining and transportation processes is a hot issue of occupational health concern for workers. Based on the characteristics of graphene and its derivatives, a new type of mining mask made of graphene oxide polypropylene composite material (GO polypropylene composite material) and reduced graphene oxide polypropylene composite material (rGO polypropylene composite material) was prepared using the direct impregnation method. Moreover, particle filtration performance tests were conducted under different gas flow conditions. The results showed that, at the same concentration, the reduction group (rGO polypropylene composite material) had more compliance indicators, and the comprehensive performance ranking was as follows: reduction group (rGO polypropylene composite material) > oxidation group (GO polypropylene composite material) > control group (polypropylene composite material). The reduction group with a concentration of 0.3 g/L showed the best overall performance. At a flow rate of 1.0 m3/h, the filtration efficiency of PM10 (95.61%) and PM2.5 (95.01%) met the relevant standards, while PM1.0 (94.88%) was close to the standard threshold (with a difference of only −0.12%), significantly better than the control (PM10, 93.39%) and the oxidation (PM10, 95.01%) groups. Moreover, at various flow rates, its particulate matter concentration was significantly lower than that for the oxidation and control groups. Overall, it meets the requirements of providing ideal filtration effects under different work intensities (low, medium, and high flow rates), thus providing strong technical support for individual protection of mine workers and a theoretical basis and practical guidance for reducing occupational diseases caused by dust exposure in mines.
Source: Materials
DOI: 10.3390/ma19030626
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: 深埋隧道多源信息演化过程监测与智能融合预警研究
OpenAlex ID: W7127990798
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