Nonideal Mixing of Se−Te in Aqueous Micellar Phase for Nanoalloys Over the Whole Mole Mixing Range with Morphology Control from Nanoparticles to Nanoribbons
Bibliographic record
Abstract
Se−Te alloy nanocrystals (NCs) were synthesized in aqueous micellar phase at 85 °C by using Na 2 SeO 3 /Na 2 TeO 3 as Se/Te source in the presence of different amounts of hydrazine (i.e., 0.1−3.6 M) as reducing agent over the entire mole fraction range of Se ( x Se ) from x Se = 0 to 1. The shape, structure, and composition of alloy NCs were characterized by scanning electron microscopic (SEM), transmission electron microscopic (TEM), and energy dispersive X-ray spectroscopic (EDS) measurements. Despite many similar characteristic features of elemental Se and Te, a drastic change in the morphology of NCs was observed from x Se = 0 to 1. Amorphous water-soluble Se nanoparticles were obtained at x Se = 1 in the presence of excess of hydrazine, whereas long nanoribbons of several micrometers of Te were produced at x Se = 0. In the intermediate mole fractions, a smooth transition in the morphology from predominantly rhombohedral in the Se rich-region to long nanoribbons in the Te rich-region of the mixtures was observed. At relatively much lower hydrazine concentration, large plate-like morphologies were obtained at x Se = 1, while the morphologies at other mole fractions were slightly distorted with greater effect in the Se rich-region of the mixtures. A careful EDS analysis on each kind of morphology revealed a homogeneous mixing between elemental Se and Te but with a significantly nonideal behavior over the whole mole fraction range. All results were summarized in a phase diagram depicting the relationship between stochiometric amounts of Se and Te with their atomic percent in crystalline phase. NCs were always highly rich in the Te contents even in the Se rich-region of the mixtures and the overall growth was predominantly driven by metalloid (crystalline) nature of Te rather than nonmetallic (amorphous) nature of Se.
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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) | 0.001 | 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".