Lanmodulin‐Decorated Microbes for Efficient Lanthanide Recovery
Bibliographic record
Abstract
Abstract Rare earth elements (REEs) are essential for many clean energy technologies. Yet, they are a limited resource currently obtained through carbon‐intensive mining. Here, bio‐scaffolded proteins serve as simple, effective materials for the recovery of REEs. Surface expression of the protein lanmodulin (LanM) on E. coli , followed by freeze‐drying of the microbes, yields a displayed protein material for REE recovery. Four REE cations (Y 3+ , La 3+ , Gd 3+ , and Tb 3+ ) are captured efficiently, with over 80% recovery even in the presence of competitive ions at one‐hundred‐fold excess. Moreover, these materials are readily integrated into a filter with high capture capacity (12 mg g −1 dry cell weight) for the selective isolation and recovery of REEs from complex matrices. Further, the proteins in the filter remain stable over ten bind‐and‐release cycles and a week of storage. To improve the deployability of this filter material, a simple colorimetric assay with the dye alizarin‐3‐methyliminodiacetic acid is incorporated. The assay can be performed in under 5 min, enabling rapid monitoring of REE recovery and filter efficiency. Overall, this low‐cost, robust material will enable environmentally friendly recycling and recovery of critical elements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".