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Updated: Jun 18, 2026

Preparation, Purification, and Characterization of Lanthanide Complexes for Use as Contrast Agents for Magnetic Resonance Imaging
13:21

Preparation, Purification, and Characterization of Lanthanide Complexes for Use as Contrast Agents for Magnetic Resonance Imaging

Published on: July 21, 2011

Beyond component optimization: systems-level biodesign for lanthanide recovery.

Alexander S Beliaev1, James C Stegen2, Kristin E Burnum-Johnson3

  • 1Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States; ARC Centre of Excellence in Synthetic Biology, Queensland University of Technology, Brisbane, QLD 4000, Australia.

Current Opinion in Biotechnology
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

Engineered proteins can recover lanthanides (Ln) from complex sources. Integrating AI/ML and biodesign advances microbial systems for efficient, end-to-end Ln separation and recovery.

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Published on: July 21, 2011

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13:51

Synthesis of Core-shell Lanthanide-doped Upconversion Nanocrystals for Cellular Applications

Published on: November 10, 2017

Area of Science:

  • Materials Science
  • Biotechnology
  • Chemical Engineering

Background:

  • Global demand for lanthanides (Ln) is increasing, but supply chains are vulnerable.
  • Conventional separation methods struggle with low concentrations and complex matrices in secondary feedstocks.
  • Biological systems offer a sustainable, low-energy alternative for Ln recovery.

Purpose of the Study:

  • To outline strategies for developing engineered microbial chassis for a complete lanthanide separation pipeline.
  • To address the limitations of current bio-based lanthanide recovery methods.
  • To integrate advanced technologies for efficient and selective lanthanide extraction.

Main Methods:

  • Utilizing Artificial Intelligence/Machine Learning (AI/ML) for guided design.
  • Employing genome engineering tools for microbial chassis development.
  • Implementing high-throughput phenotyping and biophysical transport modeling.
  • Applying a Design-Build-Test-Learn cycle for optimization.

Main Results:

  • Engineered Ln-binding proteins show high affinity and selectivity, comparable to synthetic chelators.
  • The study proposes a framework for integrating recognition, transport, accumulation, and release in microbial systems.
  • Identified the need for whole-system optimization beyond protein-level performance.

Conclusions:

  • Biodesign strategies and chassis selection are crucial for advancing lanthanide recovery.
  • Integrating AI/ML and advanced genetic tools can create efficient microbial separation pipelines.
  • This approach moves beyond bioleaching towards a complete, industrially viable lanthanide recovery pathway.