One droplet reaction for synthesis of multi-sized nanoparticles
Bibliographic record
Abstract
Reaction kinetics of nanoparticles can be controlled by tuning the Peclet number (Pe) as it is an essential parameter in synthesis of multi-sized nanoparticles. Herein, we propose to implement a self-driven multi-dimension microchannels reactor (MMR) for the one droplet synthesis of multi-sized nanoparticles. By carefully controlling the Pe at the gas—liquid interface, the newly formed seed crystals selectively accumulate and grow to a specific size. By the combination of microchannels of different widths and lengths, one droplet reaction in the same apparatus achieves the synchronous synthesis of diverse nanoparticles. MMR enables precise control of nanoparticle diameter at 5 nm precision in the range of 10–110 nm. The use of MMR can be extended to the synthesis of uniform Ag, Au, Pt, and Pd nanoparticles, opening towards the production and engineering of nanostructured materials. This approach gives the chance to regulate the accumulation probability for precise synthesis of nanoparticles with different diameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".