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Record W4297238220 · doi:10.1139/cjc-2022-0131

Investigation into the photoluminescence and energy levels in bismuth-doped Sr<sub>3</sub>MgSi<sub>2</sub>O<sub>8</sub> through first principles calculations

2022· article· en· W4297238220 on OpenAlexvenueno aff
Ting Song, Meng Zhang, Hancheng Zhu, Xinyang Zhang

Bibliographic record

VenueCanadian Journal of Chemistry · 2022
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryDopingBand gapBismuthPhotoluminescenceSemimetalCrystal structureValence (chemistry)Electronic band structureLuminescenceCrystallographyCondensed matter physicsOptoelectronicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

In this paper, influences of Bi 3+ doping on the crystal structure and electronic band structure of Sr 3 MgSi 2 O 8 , and charge transfer process from the valence band to Bi 3+ (CT) and from Bi 3+ to the conduction band in Sr 3 MgSi 2 O 8 (MMCT) have been investigated through first principles calculations. The total energy calculations make clear the location of the doped Bi 3+ in Sr 3 MgSi 2 O 8 that the doped Bi 3+ preferred to take up the Mg 2+ sites and form BiO 6 . The calculated results of electronic band structure and density of states reveal the constituents that make up the conduction band and valence band of Sr 3 MgSi 2 O 8 and Sr 3 Mg 0.875 Bi 0.125 Si 2 O 8 , respectively. In addition, a defect level appears in the band gap of Sr 3 Mg 0.875 Bi 0.125 Si 2 O 8 , which is originated from the p orbit of the doped Bi 3+ . Also, the calculated absorptions of Sr 3 MgSi 2 O 8 and Sr 3 Mg 0.875 Bi 0.125 Si 2 O 8 show a gradually shifted absorption band from deep UV region to the blue area with increasing absorption intensity. The above results make clear of the fact that first principles calculations can be useful in investigating the properties of Bi 3+ doped luminescent materials and designing new kinds of luminescent host materials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.213
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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