Identification of lanthanum‐specific peptides for future recycling of rare earth elements from compact fluorescent lamps
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Bibliographic record
Abstract
ABSTRACT As components of electronic scrap, rare earth minerals are an interesting but little used source of raw materials that are highly important for the recycling industry. Currently, there exists no cost‐efficient technology to separate rare earth minerals from an electronic scrap mixture. In this study, phage surface display has been used as a key method to develop peptides with high specificity for particular inorganic targets in electronic scrap. Lanthanum phosphate doped with cerium and terbium as part of the fluorescent phosphors of spent compact fluorescent lamps (CFL) was used as a target material of economic interest to test the suitability of the phage display method to the separation of rare earth minerals. One random pVIII phage library was screened for peptide sequences that bind specifically to the fluorescent phosphor LaPO 4 :Ce 3+ ,Tb 3+ (LAP). The library contained at least 100 binding pVIII peptides per phage particle with a diversity of 1 × 10 9 different phage per library. After three rounds of enrichment, a phage clone containing the surface peptide loop RCQYPLCS was found to bind specifically to LAP. Specificity and affinity of the identified phage bound peptide was confirmed by using binding and competition assays, immunofluorescence assays, and zeta potential measurements. Binding and immunofluorescence assays identified the peptide's affinity for the fluorescent phosphor components CAT (CeMgAl 11 O 19 :Tb 3+ ) and BAM (BaMgAl 10 O 17 :Eu 2+ ). No affinity was found for other fluorescent phosphor components such as YOX (Y 2 O 3 :Eu 3+ ). The binding specificity of the RCQYPLCS peptide loop was improved 3–51‐fold by using alanine scanning mutagenesis. The identification of peptides with high specificity and affinity for special components in the fluorescent phosphor in CFLs provides a potentially new strategic approach to rare earth recycling. Biotechnol. Bioeng. 2017;114: 1016–1024. © 2016 Wiley Periodicals, Inc.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 it