Thermodynamic Equilibrium-Driven Formation of Single-Sized Nanocrystals: Reaction Media Tuning CdSe Magic-Sized versus Regular Quantum Dots
Bibliographic record
Abstract
A concept for the fundamental science of nanoparticle synthesis, thermodynamic equilibrium-driven formation of colloidal single-sized nanoparticle ensembles, is proposed and demonstrated in this manuscript, which addresses the controlled formation of CdSe magic-sized and regular quantum dots (MSQDs and RQDs). During formation, the former are magic-sized nuclei without further growth in size, while the latter experience nucleation and growth. Both MSQDs and RQDs exhibit bandgap emission, while the former have homogeneous spectra broadening only and the latter both homogeneous and inhomogeneous spectra broadening. The former are single-sized and the latter have size distribution. With continuous and homogeneous nucleation, the thermodynamically driven formation of MSQDs was realized via our one-pot noninjection approach, which features highly synthetic reproducibility and large-scale capability. With the proper tuning of the synthetic parameters, such as the nature of the reaction medium, that affect the thermodynamic equilibria, various CdSe MSQDs and RQDs were synthesized discriminately under otherwise identical synthetic formulation and reaction conditions; the reaction media were noncoordinating 1-octadecene, coordinating trioctylphosphine, and mixtures of the two. The nature of Cd precursors, affected also by the reaction media, plays a major role in the formation of MSQDs versus RQDs. The present investigation on the thermodynamically driven formation of CdSe single-sized nanoparticles via tuning of the reaction medium, mainly, brings novel insights into the formation mechanism and into the surface ligands of the resulting colloidal nanocrystals. More importantly, the present study provides novel experimental design and approaches to single-sized nanoparticles desired for various applications.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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".