Automated Synthesis of Photovoltaic-Quality Colloidal Quantum Dots Using Separate Nucleation and Growth Stages
Bibliographic record
Abstract
As colloidal quantum dot (CQD) optoelectronic devices continue to improve, interest grows in the scaled-up and automated synthesis of high-quality materials. Unfortunately, all reports of record-performance CQD photovoltaics have been based on small-scale batch syntheses. Here we report a strategy for flow reactor synthesis of PbS CQDs and prove that it leads to solar cells having performance similar to that of comparable batch-synthesized nanoparticles. Specifically, we find that, only when using a dual-temperature-stage flow reactor synthesis reported herein, are the CQDs of sufficient quality to achieve high performance. We use a kinetic model to explain and optimize the nucleation and growth processes in the reactor. Compared to conventional single-stage flow-synthesized CQDs, we achieve superior quality nanocrystals via the optimized dual-stage reactor, with high photoluminescence quantum yield (50%) and narrow full width-half-maximum. The dual-stage flow reactor approach, with its versatility and rapid screening of multiple parameters, combined with its efficient materials utilization, offers an attractive path to automated synthesis of CQDs for photovoltaics and, more broadly, active optoelectronics.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".