Synthesis and luminescent properties of Ca_8MgY(PO_4)_7:Re~(3+)(Re~(3+)=Eu~(3+),Ce~(3+),Tb~(3+)) phosphors
Bibliographic record
Abstract
采用固相法成功合成了具有β-Ca3（PO4）2结构的发光材料Ca8MgY（PO4）7∶Re3＋（Re3＋=Eu3＋,Ce3＋,Tb3＋）。XRD、FT-IR及TG-DSC的测试结果表明,该发光材料的最佳烧结温度为1 200℃。PL测试结果表明,在252nm紫外光激发下,Ca8MgY（PO4）7∶Eu3＋呈现Eu3＋的特征发射,其中以位于612nm红光发射为主（5D0-7F2）,Eu3＋的最佳掺杂浓度为5.0%（摩尔分数）。在295nm紫外光激发下,Ce3＋激活的Ca8MgY（PO4）7由峰值位于363nm的带状5d1-4f1发射为主,Ce3＋的最佳掺杂浓度为1.0%（摩尔分数）。在228nm紫外光激发下,低掺杂浓度的Ca8MgY（PO4）7∶Tb3＋以位于5D3-7FJ的蓝光发射为主,高掺杂浓度的Ca8MgY（PO4）7∶Tb3＋以5D4-7FJ绿光发射为主,这是由于Tb3＋的交叉弛豫造成的。Tb3＋的最佳掺杂浓度为7.0%（摩尔分数）。
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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".