Functional polymer brushes for highly efficient extraction of uranium from seawater
Source abstract
Abstract
Uranium extraction from seawater provides the potential for a long-term green fuel supply for nuclear energy. However, the successful extraction of uranium from seawater is challenging because of the low uranium concentrations in seawater. Herein, we report poly(acrylamidoxime-co-acrylic acid) brush (PAO-co-AA)-based adsorbents that can highly efficiently extract and recover uranium from seawater. The PAO-co-AA-grafted fibers were fabricated by grafting of acrylonitrile and tert-butyl acrylate on poly(vinyl chloride) fibers via ARGET–ATRP method, followed by amidoximation and hydrolysis. The ARGET–ATRP method allowed for the synthesis of controllable polymer brushes with various grafting degrees (665–5908%) and thicknesses (15.1–56.3 µm) by varying the monomer concentrations during grafting. The uranium adsorption capacities of the grafted fibers were strongly affected by the grafting degree but independent of the surface area. The uranium adsorption capacities increased from 79 to 370 mg g−1 as the grafting degree increased from 665 to 3749%. The high adsorption performance can be attributed to the stretching of the polymer chain in the polymer brushes, which allows for full access of uranium ion to uranium-binding groups. The adsorbents demonstrated high uranium adsorption capacity of 5.4 mg g−1 using natural seawater in a batch adsorption mode after contact of 27 days, which is higher than that of most other adsorbents. The findings provide a new insight into the relationship between the structure and adsorption property and foreshadow the potential to use polymer brush-based adsorbents for uranium extraction from seawater.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Radioactive element chemistry and processing · Chemical Synthesis and Characterization · Nuclear Materials and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (48)
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