Peptide-functionalized iron oxide magnetic nanoparticle for gold mining
Bibliographic record
Abstract
Here, we present our work on preparing a novel nanomaterial composed of inorganic binding peptides and magnetic nanoparticles for inorganic mining. Two previously selected and well-characterized gold-binding peptides from cell surface display, AuBP1 and AuBP2, were exploited. This nanomaterial (AuBP-MNP) was designed to fulfill the following two significant functions: the surface conjugated gold-binding peptide will recognize and selectively bind to gold, while the magnetic nano-sized core will respond and migrate according to the applied external magnetic field. This will allow the smart nanomaterial to mine an individual material (gold) from a pool of mixture, without excessive solvent extraction, filtration, and concentration steps. The working efficiency of AuBP-MNP was determined by showing a dramatic reduction of gold nanoparticle colloid concentration, monitored by spectroscopy. The binding kinetics of AuBP-MNP onto the gold surface was determined using surface plasmon resonance (SPR) spectroscopy, which exhibits around 100 times higher binding kinetics than peptides alone. The binding capacity of AuBP-MNP was demonstrated by a bench-top mining test with gold microparticles. Graphical abstract.
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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 | 0.001 | 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".