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Extracellular electron transfer-dependent bioremediation of uranium-contaminated groundwater: Advancements and challenges

  • School of Environment, Harbin Institute of Technology
  • Harbin Institute of Technology Shenzhen
  • Northwestern Polytechnical University Xian

Research output: Contribution to journalReview articlepeer-review

Abstract

Efficient and sustainable remediation of uranium-contaminated groundwater is critical for groundwater safety and the sustainable development of nuclear energy, particularly in the context of global carbon neutrality goals. This review explores the potential of microbial reduction processes that utilize extracellular electron transfer (EET) to convert soluble uranium (U(VI)) into its insoluble form (U(IV)), presenting a promising approach to groundwater remediation. The review first outlines the key processes and factors influencing the effectiveness of dissimilatory metal-reducing bacteria (DMRB), such as Geobacter and Shewanella, during uranium bioremediation and recovery. The cutting-edge progress on the molecular mechanism of EET-driven U(VI) reduction mediated by c-type cytochromes, conductive pili, and electron mediators, is critically reviewed. Additionally, advanced strategies such as optimizing electron transfer, leveraging synthetic biology approach, and integration with machine learning are discussed to enhance the efficiency of EET-driven processes. The review also considers the integration of EET processes into practical engineering applications, highlighting the need for optimization and innovation in bioremediation technologies. By providing a comprehensive overview of current progress and challenges, this review aims to inspire novel research and practical advancements in the field of uranium-contaminated groundwater remediation.

Original languageEnglish
Article number122957
JournalWater Research
Volume272
DOIs
StatePublished - 15 Mar 2025
Externally publishedYes

Keywords

  • Dissimilatory metal-reducing bacteria
  • Enhanced bioremediation
  • Extracellular electron transfer
  • Machine learning
  • Process configuration
  • Uranium-contaminated groundwater

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