Oscillatory Microprocessor for Growth and in Situ Characterization of Semiconductor Nanocrystals
Bibliographic record
Abstract
An automated two-phase small scale platform based on controlled oscillatory motion of a droplet within a 12 cm long tubular Teflon reactor is designed and developed for high-throughput in situ studies of a solution-phase preparation of semiconductor nanocrystals. The unique oscillatory motion of the droplet within the heated region of the reactor enables temporal single-point spectral characterization of the same nanocrystals with a time resolution of 3 s over the course of the synthesis time without sampling while removing the residence time limitation associated with continuous flow-based strategies. The developed oscillatory microprocessor allows for direct comparison of the high temperature and room temperature spectral characteristics of nanocrystals. Utilizing this automated experimental strategy, we study the effect of temperature on the nucleation and growth of II–VI and III–V semiconductor nanocrystals. The automated droplet preparation and injection of the precursors combined with the oscillatory flow technique allows 7500 spectral data within a parameter space of 10 min reaction time at ten different temperatures and five different precursor ratios to be obtained automatically using only 250 μL of each precursor solution. The oscillatory microprocessor platform provides real-time in situ spectral information at the synthesis temperature, vital for fundamental studies of different mechanisms involved during the nucleation and growth stages of different types of nanomaterials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".