Mechanistic insights into silver-gold nanoalloy formation by two-dimensional population balance modeling
Bibliographic record
Abstract
This is the raw data for the manuscript: Mechanistic insights into silver-gold nanoalloy formation by two-dimensional population balance modelingAbstract:The large-scale synthesis of nanoparticles (NPs) with defined properties requires detailed understanding of the underlying formation mechanisms and kinetics. The formation mechanisms of bimetallic NPs are still not sufficiently understood due to the complex reaction chemistry, which makes the control of the supersaturation within the reactor, as the thermodynamic driving force, challenging. Particle size, chemical composition, and the distribution of the elements within the particles change dynamically during particle formation. In this work, we propose a mechanism for the formation of bimetallic silver-gold alloy NPs via a green liquid-phase co-reduction synthesis and develop a two-dimensional population balance model to quantitatively describe the evolution of particle size, composition, and optical properties. We shed light on the complex multi-stage formation mechanism of a highly relevant bimetallic NP system and lay the foundation for tailoring the process conditions to achieve desired optical particle properties and to develop predictive property-process relationships. 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".