Pµsl Fabricates Ultra-Thin And Tough Zirconia Veneers
Bibliographic record
Abstract
To evaluate the feasibility of PµSL technology in fabricating ultra-thin (80 μm) zirconia veneers, optimizing printing parameters for enhanced precision, mechanical strength, and clinical applicability, while minimizing tooth preparation. A PµSL system with oxygen-permeable films was employed to mitigate light scattering and improve curing accuracy. Zirconia slurry (80 wt%) was used, with parameters optimized (exposure intensity: 50 mW/cm², layer thickness: 20 μm). Post-processing included debinding and sintering. Comparative analyses with CNC-milled veneers (500 μm) assessed accuracy, marginal fit, and mechanical performance. PµSL-produced 80 μm thick zirconia veneers achieved superior accuracy (RMS: 20.4 ± 1.3 μm vs. 31.8 ± 2.2 μm for milled) and marginal fit (27.28 ± 6.22 μm vs. 72.80 ± 46.59 μm). Compression tests showed comparable strength (31.2 ± 2.6 N vs. 34.9 ± 4.7 N for 500 μm milled lithium disilicate veneers). Sintered zirconia exhibited uniform microstructure (grain size: ∼0.55 μm) and high density. PµSL enables high-precision, ultra-thin zirconia veneers with clinical-grade mechanical properties, reducing tooth preparation and enhancing biocompatibility. This technology offers a minimally invasive solution for aesthetic and functional dental restoration.
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 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 | 0.000 | 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 | 0.001 | 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 teacher head, 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".