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Particle-reinforced ion exchange resin for selective separation and recovery of cesium from highly acidic water

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Published/Copyright: June 23, 2025

Abstract

The efficient separation of 137Cs from high-level liquid waste (HLLW) is of great significance for the development of nuclear energy. However, for columns commonly used in the industry, ion exchangers need to be loaded on the resin matrix, which is typically not resistant to strong acids. Consequently, a particle-reinforced ion exchange resin (PR AWP-CA) was designed and prepared. In PR AWP-CA, the active component (ammonium phosphotungstate) and particle reinforcer were encapsulated by carrier resin (calcium alginate). The formulation of was optimized by preparing the resin with different content. The results show that PR AWP-CA has great mechanical strength, high acid resistance and excellent selectivity. In 3 mol/L nitric acid, the adsorption equilibrium was reached after only 2 h. Cs could be selectively separated, and the recovery of cesium was greater than 98 % when the extraction of interference elements was close to 0. The cesium separation ability of the resin was stable in 3.0 mol/L nitric acid at 25–55 °C. PR AWP-CA exhibited good compressive resistance in column experiment. After five cycles, the resin could still present high adsorption capacity. A particle reinforced ion exchange resin, suitable for the separation of cesium in high level radioactive waste liquid, was successfully obtained.


Corresponding author: Suwen Chen, Frontiers Science Center for Rare Isotopes, Lanzhou University, Lanzhou 730000, Gansu Province, People’s Republic of China; and School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, Gansu Province, People’s Republic of China, E-mail:
Huilian Mo and Xiangjian Meng: Co-first authors. Present address: Suwen Chen, Lanzhou University, Lanzhou, Gansu Province, People’s Republic of China.

Funding source: ience and Technology Program of Gansu Province, China

Award Identifier / Grant number: 21ZD8JA006H

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work was financially supported by the Science and Technology Program of Gansu Province, China (21ZD8JA006).

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/ract-2025-0033).


Received: 2025-03-02
Accepted: 2025-05-31
Published Online: 2025-06-23
Published in Print: 2025-09-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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