Ultrashort Peptides as Stabilizing Agents for Colloidal Nanogold
Bibliographic record
Abstract
Ultrashort peptides hold immense potential as structural tools for enhancing the colloidal stability of nanomaterials, such as nanogold. However, such applications have been largely unexplored in part due to the inherent complexity in designing, synthesizing, and testing short peptides as colloidal nanoparticle stabilizers. In this work, we use a motif-function-guided process for peptide synthesis and high throughput screening to evaluate the colloidal stability of spherical nanogold solutions and pentapeptides. We have successfully built a library of peptides capable of stabilizing colloidal nanogold at peptide concentrations of ≤1.0 μM. This represents a 50-100-fold reduction in the concentration required for stability compared to other small molecules used as capping agents, which illustrates the potential of using short peptide sequences as colloidal nanogold stabilizers. Our findings could significantly impact the future development of high-affinity surface modifiers for the custom engineering of nanogold by providing a deeper understanding of the complex interactions between nanoparticles and peptides.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".