A Modular Self-Driving Laboratory for Automated Synthesis of CsPb(Cl/Br/I) <sub>3</sub> Perovskite Nanocrystals
Bibliographic record
Abstract
Self-driving laboratories (SDLs) integrating automation and data-driven control are increasingly used in materials synthesis. Existing SDLs still face challenges in hardware integration and stable flow operation. Here, we report a modular fluidic–microwave SDL for the automated synthesis of perovskite nanocrystals (PNCs) with programmable thermal profiles and inline photoluminescence monitoring. Its constant-pressure fluidics and batch microwave design decouple reaction temperature from residence time, suppress flow pulsation, and yield reproducible low-noise data sets. Using CsPbX 3 (X = Cl, Br, I), the platform achieves relative standard deviations of 1.53% in fwhm, 0.06% in peak wavelength, and 1.85% in PL intensity across runs. Automated parameter screening identifies an optimal synthesis window (120–140 °C, 28 °C min –1, 120–180 s, Pb/Cs = 2–3) that produces phase-pure nanocrystals with narrow emission line widths (∼18–19 nm). This SDL provides a reproducible, programmable basis for closed-loop optimization and AI-guided nanomaterials synthesis.
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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".