Unravelling the shell growth pathways of Au–Ag core–shell nanoparticles by <i>in situ</i> liquid cell transmission electron microscopy
Bibliographic record
Abstract
Controlling the growth, structure and morphology of core-shell nanoparticles (NPs) is significant for catalytic applications and it can be achieved by adding chemical additives to the synthesis reaction mixture. However, achieving precise control over NP synthesis would require a comprehensive understanding of the mechanisms of NP formation under different chemical conditions, which is quite challenging. Here, using in situ liquid cell transmission electron microscopy (TEM), the overgrowth mechanisms of Ag on Au nanobipyramids (NBPs) are studied in AgNO3 aqueous solution with ascorbic acid as the reducing agent. Au-Ag core-shell NPs are formed via two mechanistic modes: (1) atom deposition during which the Ag atoms are deposited directly onto Au NBPs without the addition of poly(vinyl)pyrrolidone (PVP) and (2) nuclei coalescence during which the Ag nanocrystals (NCs) adsorb onto Au NBPs in the presence of PVP. High-resolution imaging reveals the dynamics of the coalescence process of Ag NCs upon addition of PVP. This study helps us to understand the effect of chemical additives during the evolution of a core seed into core-shell NPs with a well-defined composition and shape. It is useful for synthesizing NPs with greater design flexibility and expanding their various technological applications.
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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".