Synthesis and sensing properties of D<sub>5h</sub>pentagonal silver star nanoparticles
Bibliographic record
Abstract
In this work, we use silver decahedral nanoparticle (AgDeNP) seeds to synthesize pentagonal silver stars (AgStDeNPs) and study the sensing properties of these nanoparticles. The regrowth process of AgStDeNPs is kinetically-controlled, so the purity of the seed NPs is critical to avoid secondary deposition in the highly non-equilibrium reduction. To control the regrowth process, surface blocking with sodium polyacrylate (PANa) was implemented. PANa moderates rough silver nanostructures typically obtained by reduction with ascorbic acid. To modulate polymer binding to the surface and thus to tune surface blocking, pH served as a key synthetic parameter. Under optimal regrowth conditions, new sliver was deposited on the highest energy sites of the decahedra - the vertices of the rims - to yield pentagonal stars. The universality of this regrowth process was established with several different seed particles. The sharpness and size of the stellated tips are tunable by the amount of added silver. Gold deposition onto AgStDeNPs enables the preparation of diverse structures with enhanced stability. Ease of transformation, e.g. rounding, of star branches opens a promising venue for enhanced SPR sensing. Also, AgStDeNPs enable femtomolar detection of 5,5-dithiobis(2-nitrobenzoic acid) in SERS.
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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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".