Phage display screening for highly specific nickel- and cobalt-binding peptides for bio-recovery of metals
Bibliographic record
Abstract
Electronic waste is a valuable source of critical metals like nickel and cobalt, but their recovery is challenging. Current recycling processes use harsh conditions and toxic chemicals, which is why environmentally friendly alternatives are crucial. Metal-binding peptides offer high selectivity and durability, making them promising for sustainable metal separation. Here, phage display was successfully applied to screen a combinatorial peptide library with specific affinities to nickel or cobalt. Identified peptides with the amino acid sequences FWPLHHH, GPHKHHA, HNYHHRH, and HMNHHHH revealed improved binding affinities of up to 20.000-fold to immobilized metal ions compared to the unspecific binding of the phage backbone. Furthermore, low micromolar dissociation constants e.g., 6.2 µM for peptide Co_02 (HMNHHHH) to Co 2+ and 29.0 µM for peptide Ni_01 (GPHKHHA) to Ni 2+ , determined by Isothermal Titration Calorimetry (ITC) measurements confirmed the intrinsic metal binding properties. These peptides offer a high potential for future recycling of nickel and cobalt from mixed metal waste like batteries.
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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) | 0.001 | 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 | 0.000 |
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 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".