Synthesis of Lithium Niobate Nanocrystals with Size Focusing through an Ostwald Ripening Process
Bibliographic record
Abstract
A simple surfactant assisted solution-phase approach is demonstrated here for the preparation of lithium niobate (LiNbO 3 ) nanoparticles with an average size of 30 nm. This solution-phase process results in the formation of crystalline, uniform nanoparticles of LiNbO 3 at 220 °C with an optimal reaction time of 36 h. Advantages of this method also include the preparation of crystalline nanoparticles of LiNbO 3 without the need for further heat treatment or the use of an inert atmosphere. The growth of these nanoparticles began with a controlled agglomeration of nuclei. The reaction subsequently underwent a process of oriented attachment and Ostwald ripening, which dominated and controlled the further growth of the nanoparticles. These processes produced single-crystalline nanoparticles of LiNbO 3 . The average dimensions of the nanoparticles were tuned from 30 to 95 nm by increasing the reaction time of the solvothermal process. The LiNbO 3 nanoparticles were characterized using transmission electron microscopy (TEM), selected area electron diffraction (SAED), high resolution TEM, X-ray diffraction, and Raman spectroscopy techniques. The nanoparticles were also confirmed to be optically active for second harmonic generation (SHG). These particles could enable further development of SHG based microscopy techniques.
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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".