Elucidating the role of precursors in synthesizing single crystalline lithium niobate nanomaterials: a study of effects of lithium precursors on nanoparticle quality
Bibliographic record
Abstract
A number of solution-based procedures have been realized for the synthesis of lithium niobate (LiNbO3) nanoparticles (NPs). Relatively little is, however, known about the influences of the selection of lithium (Li) precursors on the resulting dimensions, shapes, crystallinity, and purity of the products. A comparative study is provided herein on the role of different Li precursors during the synthesis of LiNbO3 NPs. To the best of our knowledge, this study provides the first systematic comparison of the effects of various Li reagents on the preparation of LiNbO3 NPs through solvothermal processes. This solution-phase approach was tuned by the inclusion of Li precursors that either lacked carbon based anions (e.g., F-, Cl-, Br-, I-, OH-, NO3-, or SO42-) or contained carbon-based anions (e.g., C2H5O-, C2H3OO-, C5H7OO-, or CO32-). All other variables were held constant during the synthesis, such as reaction temperature, solvent, niobium precursor, and surfactants. The results of these studies suggest that the type of Li precursor selected plays an important role in nanoparticle formation, such as through controlling the uniformity, crystallinity, and aggregation of LiNbO3 NPs. The average diameter of the resulting NPs can also vary from ∼30 to ∼830 nm as a function of the Li reagent used in the synthesis. The selection of Li precursors also influences the phase purity of the products. This comparative study on the preparation of crystalline LiNbO3 NPs represents a critical step forward to understand the influences and roles of precursors in the design of synthetic processes for the preparation of a variety of alkali metal niobates (e.g., including NaNbO3 and KNbO3) and crystalline metal oxide-based NPs containing other transition metals (e.g., titanium, tantalum).
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".