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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".