Yb3+-Mediated Luminescence Enhancement in Er3+-Doped 3D-Printed ZrO2 Microarchitectures
Bibliographic record
Abstract
• Photoluminescence and cathodoluminescence reveal dopant effects on emission properties. • Yb 3+ enhances Er 3+ emissions in 3D ZrO 2 architectures. • Thermal treatments stabilize t-ZrO 2 and induce m-ZrO 2 phase transitions. • Optical characterization reveals reduced defect emissions in thermally treated architectures. Lanthanide-doped ZrO 2 ceramics are promising materials for optics due to their high refractive index and tunable luminescent properties. In this study, we investigated the impact of Yb 3+ and Er 3+ dopant concentrations on the emission behavior of lanthanide-doped 3D ZrO 2 microarchitectures fabricated using two-photon lithography. Thermal treatments have been carried out at 600°C and 750°C to promote the stabilization of the ZrO 2 tetragonal phase ( t -ZrO 2 ) and at 1000°C to induce phase transition in ZrO 2 to the monoclinic ( m -ZrO 2 ) phase in the 3D microarchitectures. Scanning transmission electron microscopy confirmed the crystallinity changes across the thermal treatments. Photoluminescence (PL) and cathodoluminescence (CL) measurements confirm emission bands of Yb 3+ and Er 3+ single dopants and Yb 3+ :Er 3+ co-dopants. Variations in Yb 3+ content reveal that the PL emission of Er 3+ increases (e.g., 4 S 3/2 → 4 I 15/2 ), which is attributed to the interplay between the dopant concentrations, defect structures and the ZrO 2 host. The results highlight the importance of ZrO 2 microarchitectures' crystallinity and co-doping relationship, which enable the promotion of Er 3+ emissions. We expect our research will find applications in 3D optical systems.
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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".