Stable ligand-free stellated polyhedral gold nanoparticles for sensitive plasmonic detection
Bibliographic record
Abstract
Ligand-free stellated gold nanoparticles (AuStNPs) with well-defined octahedral (O(h)) and icosahedral (I(h)) core symmetries were prepared using hydrogen peroxide as a reducing agent. Only three reagents: gold precursor (HAuCl4), H2O2 and NaOH were required to form colloidally and chemically stable AuStNPs with a zeta-potential between -55 and -40 mV indicative of excellent charge stabilization. The size and degree of stellation of AuStNPs can be controlled by several synthetic parameters so that the localized surface plasmon resonance (LSPR) can be varied from ca. 850 nm in near-infrared (NIR) to ca. 530 nm. In particular, AuStNP size and LSPR tuning can be conveniently accomplished by iodide variation. The size distribution of AuStNPs was improved by nucleation with ascorbic acid, and the AuStNP size and degree of branching could be readily modified using arginine. AuStNPs are advantageous for SPR sensing, as it was demonstrated in the sensitive detection of not only thiols, such as ampicillin, but also iodide with the detection limit of 3.2 pM (0.4 ng L(-1)). The reported ligand-free stable AuStNPs thus should be very useful for biodiagnostics based on SPR sensing and potentially for SERS and hyperthermia therapy.
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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".