In‐Situ Observation of Nucleation and Growth of PbSe Magic‐Sized Nanoclusters and Regular Nanocrystals
Bibliographic record
Abstract
In-situ observation of the temporal evolution of the absorption of PbSe nanocrystals (NCs) via a low-temperature noninjection approach is presented. Based on a model reaction of lead oleate (Pb(OA)(2) ) and n-trioctylphosphine selenide (TOPSe) in 1-octadecene at 35-80 °C, the use of commercially available TOP (90 or 97%) in affecting the formation of the NCs is explored. TOPSe solutions made from TOP 90% exhibited higher reactivity than those made from TOP 97%. (31)P NMR spectroscopy detected no dioctylphosphine selenide (DOPSe) but some DOP in ≈1.0 M TOPSe/TOP solution (made from TOP 90%), as well as no diphenylphosphine selenide (DPPSe) when DPP was added to the ≈1.0 M solution. Hence, it is proposed that, for the formation of PbSe monomers, an indirect pathway dominates with the formation of a Pb-P complex/intermediate, which results from the activation of Pb(OA)(2) by a phosphine compound (such as DPP, DOP, or TOP) and in turn reacts with TOPSe. With the use of TOP 90% and the addition of secondary phosphine DPP, the formation of PbSe magic-sized nanoclusters (MSNCs) and regular NCs (RNCs) is investigated. With proper tuning of the synthesis conditions, the formation of various PbSe MSNCs versus RNCs is monitored in situ with versus without the addition of DPP, or at different reaction temperatures but otherwise identical synthetic formulation and reaction parameters. Accordingly, the degree of supersaturation (DS) of the PbSe monomer affecting the development of these PbSe MSNCs versus RNCs is proposed; the higher the DS, the more the MSNCs are favored. Also, surface-determined cluster-cluster aggregation is proposed to be the growth mechanism for both the RNCs and MSNCs. For the former, quantized growth is followed by continuous growth. For the latter, the sizes of the magic-sized families are calculated.
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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 | 0.000 |
| 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) | 0.001 | 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".