journal article·2017-12

Bifunctional MoS2 coated melamine-formaldehyde sponges for efficient oil–water separation and water-soluble dye removal

Zuteng Wan, De Li, Yanli Jiao, Xiaoping Ouyang, Lingqian Chang, Xiufeng Wang

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Abstract

Abstract Removal of pollutants from water is an important global challenge for energy conservation and environmental protection. Advanced macro-porous sorbent materials with three-dimensional wettability surfaces have been considered traditional candidates for wastewater treatment. Although the real water environment always co-exists with two pollution sources, the major functions of existing macro-porous materials are limited to either water-insoluble organics separation or water-soluble dyes removal. Bifunctional macro-porous materials for simultaneous oil–water separation and soluble elements removal are currently lacking. Here we report the novel MoS2-coated melamine-formaldehyde (MF@MoS2) sponges with different wettabilities, for example, superhydrophobicity and superhydrophilicity, exhibiting both high selectivity and excellent absorption capacities for a wide range of oils/organic solvents with different densities and water-soluble dyes. The functional MF@MoS2 sponges perform high absorption capacity for different organic solvents up to 66 and 157 times of its own weight respectively, while showing high discoloration efficiency of 98% methyl orange within 10 min. For the first time, we report these functional macro-porous materials used for simultaneous cleaning of oil and soluble pollutants in water, which extended the potential of MoS2 for environmental application. Furthermore, a continuous and simultaneous removal of insoluble oils and soluble dyes in situ from water was achieved by simply applying external pumping on the functional macro-porous MF@MoS2 sponges.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Surface Modification and Superhydrophobicity · Advanced Photocatalysis Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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