The adsorptive removal of lead ions in aquatic media: Performance comparison between advanced functional materials and conventional materials

Abstract

The low permissible concentration of lead ions (Pb²⁺) in potable water (15 ppb) makes it even more challenging to develop highly efficient water purification technologies. Adsorption has become one of the preferred water treatment techniques for removing various metal species including Pb²⁺ ions. However, assessment of adsorbent capacities between the tested materials was often made in a biased manner (e.g., derivation of the maximum capacities under unrealistically high initial feeding conditions). In this work, such comparison was made in terms of partition coefficient (PC) as a key metric for the least biased evaluation to explore the best sorbents among materials with a broad range of breakthrough capacities. Nanoscale sorbents derived from carbon-based nanomaterials and MOFs are highly advantageous, as they can offer highly sensitive and selective adsorption of Pb²⁺ ions. In general, advanced functional materials showcased better adsorption-desorption efficiency for Pb²⁺ when compared to conventional materials (e.g., over three regeneration cycles). Continuous research and development for the functional materials are further required to scale down the production cost so that cost-effective technologies may be applied for environmental remediation. The future scope for Pb²⁺ removal techniques is discussed further with respect to the development of novel functional materials.

Graphical Abstract

Keywords:

• Adsorption
• Pb²⁺ ions
• aqueous system
• conventional materials
• novel materials

Additional information

Funding

This research acknowledges the support made by the R&D Center for Green Patrol Technologies through the R&D for Global Top Environmental Technologies funded by the Ministry of Environment (Grant No: 2018001850001) as well as by a grant from the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant No: 2016R1E1A1A01940995).