Robust Peptide‐Functionalized Gold Nanoparticles via Ethynyl Bonding for High‐Fidelity Bioanalytical Applications
Bibliographic record
Abstract
While Au-S bonds have been widely applied in preparing gold nanoparticle (AuNP) bioconjugates for biosensing, cell imaging, and biomedical research, biothiols in complex biological environments can seriously interfere with the stability of the conjugates due to ligand exchange. Herein, we communicate a robust and fast strategy for constructing peptide-functionalized AuNP conjugates (PFCs) using the Au-C≡C bond, which can be completed within two minutes. The resulting Au-C≡C PFCs exhibited better stability and resistance to biothiols than the corresponding Au-S PFCs, and also demonstrated excellent stability in high salt concentration, a wide range of pH values, and varying temperatures. The mechanism of Au-C≡C conjugation was confirmed using molecular dynamics simulation and X-ray photoelectron spectroscopy (XPS). The Au-C≡C PFCs significantly improved the signal fidelity in an intracellular caspase imaging assay. Overall, the developed strategy provides a promising approach for constructing AuNP nanoprobes, allowing reliable detection and broadening the potential for diverse biological applications.
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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 | 0.000 |
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".