journal article·2023-10

Effective solar-driven interfacial water evaporation-assisted adsorption of organic pollutants by a activated porous carbon material

Ning Li, Yong Ma, Qing Chang, Chao-Rui Xue, Ying Li, Wen-Jing Zheng, Lei Liu, Xiang-Qian Fan, Sheng-Liang Hu

New Carbon Materials· Volume 38· Issue 5
Publisher page

Abstract

Recently, solar-driven interfacial water evaporation (SDIWE) has attracted worldwide attention owing to its potential use in seawater desalination and wastewater purification. Nevertheless, how to effectively use the inevitable conduction heat loss and eliminate organic pollutants are still challenging. We report the SDIWE- assisted adsorption of organic pollutants by using the conduction heat loss to improve the total energy efficiency of the SDIWE system. Porous carbon (PC) and activated PC were prepared by a simple recrystallizing salt template-assisted carbonization and KOH activation method. After activation, the activated PC sample with a PC : KOH mass ratio of 1 : 4 (PC-A4) has a hierarchical porous structure, a better absorption capacity in the spectral region of 200-2500 nm, a high specific surface area of 1 867.71 m2 g−1 and a large pore volume of 1.04 cm3 g−1. Based on this, PC-A4 has a high evaporation rate and energy efficiency, which can be further increased by regulating the mass of the water body. Subsequently, the conduction heat generated by the SDIWE system was used for SDIWE-assisted adsorption. Notably, the maximum amount of rhodamine B adsorbed by PC-A4 is 1 610 mg g−1 at a conduction temperature of 309 K, which is higher than that of the same sample at 298 K. Consequently, this work offers a promising approach for effectively using the conduction heat loss of the SDIWE system and developing it for water purification.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Solar-Powered Water Purification Methods · Membrane Separation Technologies · Solar Thermal and Photovoltaic Systems

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

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