Synthesis of Multiphase Rare-Earth Phosphate Borosilicate Glass-Ceramic Composite Materials Using a One-Step Coprecipitation Method
Bibliographic record
Abstract
Composite materials consisting of multiple ceramic crystallites dispersed within a glass matrix were synthesized using the coprecipitation method. The coprecipitation technique enhances mixing of the precursors, leading to a decrease in the diffusion distance of the ions. Multiphase composites are expected to sequester substantial amounts of different nuclear waste elements when used as a nuclear waste form. The crystalline phases can incorporate substantial quantities of actinides based on the crystal structures of the crystalline phases, while glasses can accommodate significant amounts of small fission products such as Cs, Xe, Sr, etc. YPO 4 and LaPO 4 were dispersed in a borosilicate glass (BG) matrix. Modifications were made to the composition of the glass matrix to prevent the formation of undesired secondary phases. Powder X-ray diffraction (XRD), X-ray absorption near-edge spectroscopy (XANES), and scanning electron microscopy (SEM) were used to study the long-range order, the local chemical environment, and the morphology of the synthesized materials. Analysis of the X-ray diffraction (XRD) patterns indicated the presence of just the LaPO 4 and YPO 4 phases in the composite material following modification of the glass matrix, thus confirming that only these two crystalline phases were present in this composite material, which was further confirmed by XANES and SEM analyses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".