Pt–S Bond‐Mediated Nanoflares for High‐Fidelity Intracellular Applications by Avoiding Thiol Cleavage
Bibliographic record
Abstract
The Au-S bond is the classic way to functionalize gold nanoparticles (AuNPs). However, cleavage of the bond by biothiols and other chemicals is a long-standing problem hindering practical applications, especially in cells. Instead of replacing the thiol by a carbene or selenol for stronger adsorption, it is now shown that the Pt-S bond is much more stable, fully avoiding cleavage by biothiols. AuNPs were deposited with a thin layer of platinum, and an AuNP@Pt-S nanoflare was constructed to detect the miRNA-21 microRNA in living cells. This design retained the optical and cellular uptake properties of DNA-functionalized AuNPs, while showing high-fidelity signaling. It discriminated target cancer cells even in a mixed-cell culture system, where the Au-S based nanoflare was less sensitive. Compared to previous methods of changing the ligand chemistry, coating a Pt shell is more accessible, and previously developed methods for AuNPs can be directly adapted.
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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) | 0.002 | 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".