Large-Scale Synthesis of Metal Nanocrystals in Aqueous Suspensions
Bibliographic record
Abstract
Fundamental studies and practical use of metal nanoparticles (NPs) frequently depend on the ability to reproducibly synthesize large quantities of shape-specific NPs. For this reason, facile synthetic procedures are desired that will lead to large quantities of uniformly sized metal NPs exhibiting specific shapes. Here, we report a general approach to the large-scale synthesis of noble metal nanocrystals having well-defined shapes and a narrow size distribution. This method utilizes seed-mediated NP growth in aqueous suspensions of cationic surfactants and metal salts. It leads to a ∼60-fold increase in NP volumetric production capacity, compared to the most widely used solution-based synthetic methods. In addition, it uses up to 100 times less cationic surfactant than conventional solution-based methods. The applicability of the method is demonstrated in the synthesis of Pd nanocubes, rhombic dodecahedra, and polyhedrons with low index facets; branched Pd nanocrystals; alloy Pt/Pd nanocubes; Ag nanocubes. The advantages and limitations of the approach are discussed, including accessible shapes, growth kinetics, and the capability to scale up the synthetic procedure.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".