Analysis of a microreactor for synthesizing nanocrystals by computational fluid dynamics
Bibliographic record
Abstract
Abstract Microreactors eliminate batch‐to‐batch variability and allow better control over nanocrystal synthesis. A serpentine microreactor fabricated by femtosecond laser ablation is presented and characterized by computational fluid dynamics, since the micro channels show a trapezoidal cross‐section mainly due to the relatively high numerical aperture of the focusing lens. Mixing, macro and micro, throughout the device was investigated for inlet flow rates between 10–500 μL min−1 and the injection of an inert tracer with the same transport properties of water. The simulation of the whole microreactor enabled the analysis of the formation and destruction of structures. For instance, secondary flows played a major role in mixing behaviour: small flow rates did not promote mixing of the tracer and a stream of pure water even after 43 curved segments, while they were perfectly mixed after 9 segments for higher flow rates. According to the mixing index, the maximum effect of convective mixing was achieved for an inlet flow rate of 250 μL min−1. Tracer dispersion and the mixing index guided a scale‐up process of the microreactor, optimizing the number of curved segments while increasing total throughput. The upscaled design exhibited mixing saturation at 400 μL min−1 and promoted better control of residence time to allow nanocrystal growth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".