Designing Luminescent Materials and Band Gaps: A Soft X-ray Spectroscopy and Density Functional Theory Study of Li<sub>2</sub>Ca<sub>2</sub>[Mg<sub>2</sub>Si<sub>2</sub>N<sub>6</sub>]:Eu<sup>2+</sup> and Ba[Li<sub>2</sub>(Al<sub>2</sub>Si<sub>2</sub>)N<sub>6</sub>]:Eu<sup>2+</sup>
Bibliographic record
Abstract
A large band gap is a prerequisite for efficient emissions from a rare earth doped phosphor and is consequently a prerequisite for its application in high-quality lighting. We present a detailed characterization of luminescent materials Li 2 Ca 2 [Mg 2 Si 2 N 6 ]:Eu 2+ and Ba[Li 2 (Al 2 Si 2 )N 6 ]:Eu 2+ using soft X-ray spectroscopy and density functional theory calculations, including a rigorous experimental determination, and theory-based elucidation, of their band gaps. The band gap of Li 2 Ca 2 [Mg 2 Si 2 N 6 ]:Eu 2+ is determined to be 4.84 ± 0.20 eV, while that of Ba[Li 2 (Al 2 Si 2 )N 6 ]:Eu 2+ is 4.82 ± 0.20 eV. The origin of the band gaps is discussed in the context of the calculated DOS of each material and compared to benchmark luminescent materials Sr[LiAl 3 N 4 ]:Eu 2+ and Sr[Mg 3 SiN 4 ]:Eu 2+ . Critically, the elements determining the band gaps are identified using the calculated density of states, as well as experimental resonant X-ray emission measurements. This allows for predictive power when searching for new nitridosilicates and related host structures, which upon doping with rare earth elements, may find application in the next-generation of phosphor converted light emitting diodes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.009 | 0.008 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".