Advances in next gen aerogel materials for radionuclides cleanup: From functional design to computational insights

Gutha Yuvaraja, Chun Ta Wen, Munagapati Venkata Subbaiah, Jet Chau Wen, Sada Venkateswarlu, Vijaya Yarramuthi, Kun Yi Andrew Lin, Dong Su Kim, K. Keerthi, N. V.V. Jyothi

Research output: Contribution to journalReview articlepeer-review

Abstract

Radionuclide pollution from both anthropogenic and natural sources presents a serious threat to human health and environmental safety. Aerogel-based adsorbents have become promising options for cleaning up radionuclides due to huge surface area, with different pore sizes, lightweight, and can be chemically adjusted. However, so far, there hasn't been a complete review that brings together and examines how aerogel materials especially modified and mixed types can specifically capture radionuclides like uranium (U), thorium (Th), strontium (Sr), cesium (Cs), and iodine-131 [131I]. This review highlights recent advances in the development of aerogels, including silica, carbon, polymeric, metal oxide, hybrid, MXene, and porous framework-derived materials such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) for efficient capture of hazardous radionuclides. The influence of critical parameters such as pH, temperature, and surface characteristics on adsorption performance is systematically discussed. Advanced characterization methods such as X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS), and Brunauer-Emmett-Teller (BET) surface area analysis to explain how aerogel radionuclide complexes interact at the electronic level, how they bond, and how stable their structure is explored. Additional insights from density functional theory (DFT) and new machine learning (ML) models help predict binding energies, charge transfer, and thermodynamic feasibility, speeding up the smart design of effective adsorbents. This review provides a detailed resource for researchers in coordination chemistry, environmental cleanup, materials science, and nanotechnology, and it aims to encourage new ideas in radionuclide adsorption technologies while offering a full guide for making new aerogel materials that can capture radionuclides.

Original languageEnglish
Article number217047
JournalCoordination Chemistry Reviews
Volume545
DOIs
StatePublished - 15 Dec 2025

Bibliographical note

Publisher Copyright:
© 2025 Elsevier B.V.

Keywords

  • Adsorption
  • Aerogel compounds
  • DFT studies
  • Machine learning model
  • Radionuclides

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