Interrelated Roles of Multiple Key Structure‐Directing Agents in Seed‐Mediated Growth of Monodisperse and Multibranched Au Nanoparticles
Bibliographic record
Abstract
Abstract Detailed mechanistic investigations of the interrelated roles of multiple key structure‐directing agents in the growth solution of Au nanoparticles (AuNPs) is required for the optimization of synthetic protocols. Here, we report a robust seed‐mediated growth strategy for synthesizing multibranched NPs (MB‐AuNPs) with monodispersed size distribution, and investigate the roles of Ag ions and 4‐(2‐hydroxyethyl)piperazine‐1‐ethanesulfonic acid (HEPES) based on an overgrowth synthesis approach. The intertwining roles of Ag + , surface‐capping stabilizers, and reducing agents were elucidated, and used to control the morphology of MB‐AuNPs. The overgrowth of MB‐AuNPs involves two distinct underlying pathways, namely, directional and anisotropic growth of Au branches on specific facets of Au seeds as well as an aggregation and growth mechanism governed by HEPES. In addition to Ag ions and HEPES, morphology tunability can also be achieved by pre‐modification of the Au seeds with molecular probes. Optimized probe‐containing MB‐AuNPs prove to be excellent surface‐enhanced Raman scattering (SERS) substrates and nanozymes. Taken together, the results of this work reveal the mechanistic evolution of nanocrystal growth which should stimulate the development of new synthetic strategies, improve the capabilities of tuning the optical, catalytic, and electronic properties of NPs, and further advance their applications in biolabeling, imaging, biosensing, and therapy.
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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".