High‐Performance Polymer‐derived Ceramics in LCD 3D Printing
Bibliographic record
Abstract
Abstract This study demonstrates the fabrication of high‐strength, lightweight polymer‐derived ceramics (PDCs) using silicon oxycarbide (SiOC)‐precursor formulations with liquid crystal display (LCD) vat photopolymerization (VPP) technology. Complex geometries, such as gyroids and stochastic lattices, are successfully 3D‐printed and evaluated under varying feature thicknesses and pyrolysis temperatures (800 °C and 1200 °C). Photorheology and thermogravimetric analysis (TGA) validated the efficient curing and pyrolysis characteristics of a printable precursor formulation based on vinyl methoxysiloxane homopolymer (VMM‐010), which demonstrated rapid curing, low viscosity, and compatibility with LCD 3D printing, ensuring precise layering and efficient resin removal. Micro‐CT scans confirmed its structural integrity and absence of voids, even in relatively thick components (≈3 mm). The VMM‐based PDC lattices achieved specific compressive strengths up to 9.4 MPa g⁻¹ cm 3 , a 50‐fold improvement over comparable lattices produced with a high‐porosity SiOC PDC, and exceptional high‐temperature stability, maintaining structural integrity after 2 h at 1500 °C. Compositional analysis revealed lower free carbon content and improved ceramic phase formation, driving the enhanced mechanical and thermal performance of the VMM‐based ceramic. These findings underscore the scalability, reliability, and superior performance of VMM formulations for LCD 3D printing, offering new possibilities for high‐performance ceramic applications in aerospace, automotive, and biomedical industries.
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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 | 0.001 |
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; 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".