Determination of the Chemical Composition of Lithium Niobate Powders
Bibliographic record
Abstract
Existent methods for determining the composition of lithium niobate single crystals are mainly based on their variations due to changes in their electronic structure, which accounts for the fact that most of these methods rely on experimental techniques using light as the probe. Nevertheless, these methods used for single crystals fail in accurately predicting the chemical composition of lithium niobate powders due to strong scattering effects and randomness. In this work, an innovative method for determining the chemical composition of lithium niobate powders, based mainly on the probing of secondary thermodynamic phases by X-ray diffraction analysis and structure refinement, is employed. Its validation is supported by the characterization of several samples synthesized by the standard and inexpensive method of mechanosynthesis. Furthermore, new linear equations are proposed to accurately describe and determine the chemical composition of this type of powdered material. The composition can now be determined by using any of four standard characterization techniques: X-Ray Diffraction (XRD), Raman Spectroscopy (RS), UV-vis Diffuse Reflectance (DR), and Differential Thermal Analysis (DTA). In the case of the existence of a previous equivalent description for single crystals, a brief analysis of the literature is made.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".