Controlled Synthesis of Ternary II−II‘−VI Nanoclusters and the Effects of Metal Ion Distribution on Their Spectral Properties
Bibliographic record
Abstract
The reaction of [(3,5-Me(2)-C(5)H(3)N)(2)Zn(ESiMe(3))(2)] (E = Se, Te) with cadmium(II) acetate in the presence of PhESiMe(3) and P(n)Pr(3) at low temperature leads to the formation of single crystals of the ternary nanoclusters [Zn(x)()Cd(10)(-)(x)()E(4)-(EPh)(12)(P(n)()Pr(3))(4)] [E = Se, x = 1.8 (2a), 2.6 (2b); Te, x = 1.8 (3a), 2.6 (3b)] in good yield. The clusters [Zn(3)Hg(7)Se(4)(SePh)(12)(P(n)()Pr(3))(4)] (4) and [Cd(3.7)Hg(6.3)Se(4)(SePh)(12)(P(n)()Pr(3))(4)] (5) can be accessed by similar reactions involving [(3,5-Me(2)-C(5)H(3)N)(2)Zn(SeSiMe(3))(2)] or [(N,N'-tmeda)Cd(SeSiMe(3))(2)] (1) and mercury(II) chloride. The metal silylchalcogenolate reagents are efficient delivery sources of {ME(2)} in cluster synthesis, and thus, the metal ion content of these clusters can be readily moderated by controlling the reaction stoichiometry. The reaction of cadmium acetate with [(3,5-Me(2)-C(5)H(3)N)(2)Zn(SSiMe(3))(2)], PhSSiMe(3), and P(n)()Pr(3) affords the larger nanocluster [Zn(2.3)Cd(14.7)S(4)(SPh)(26)(P(n)()Pr(3))(2)] (6). The incorporation of Zn(II) into {Cd(10)E} (E = Se, Te) and Zn(II) or Cd(II) into {Hg(10)Se} nanoclusters results in a significant blue shift in the energy of the first "excitonic" transition. Solid-state thermolysis of complexes 2 and 3 reveals that these clusters can be used as single-source precursors to bulk ternary Zn(x)Cd(1)(-)(x)E materials as well as larger intermediate clusters and that the metal ion ratio is retained during these reactions.
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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".