Tailoring the reaction pathway for control of size and composition of silver-gold alloy nanoparticles
Bibliographic record
Abstract
This is the raw data for the manuscript: Tailoring the reaction pathway for control of size and composition of silver-gold alloy nanoparticlesAbstract:In this work, we focus on tuning both the particle size and chemical composition of bimetallic silver-gold alloy nanoparticles (NPs) by carefully controlling the reaction pathway. NP synthesis involves the control of the supersaturation profile in time and space. For the reaction-controlled case, this supersaturation profile is determined by a network of reactions leading to the build-up of the monomer concentration. Using a variety of characterization tools, we show how the process conditions influence the coupled reactions in the complex formation mechanism of silver-gold alloy NPs. Applying a simple mass balance allows for the independent control of size and chemical composition of these NPs, yielding particles with constant composition and varying size in the range of 20 to 40 nm. A series of constant sizes and varying chemical compositions in the range of 10 % to 100 % molar gold content is also possible. Our new methodology shows an example of how reaction networks can be tailored to achieve targeted NP properties and paves the way for better control in the synthesis of multicomponent NPs. All data are sorted according to their appearance in the figures of the main manuscript.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.026 |
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".