Additive manufacturing of polymer derived ceramics: Materials, methods, and applications
Bibliographic record
Abstract
Owing to freedom of design, simplicity, and ability to handle complex structures, additive manufacturing (AM) or 3D printing of ceramics represents a promising enabling technology and has already been used to produce geometrically complex ceramic components and ceramic metamaterials. Consequently, novel applications for additively manufactured ceramics, which leverage their structural, high temperature, and chemical-resistant properties, have been proposed in areas ranging from electrical engineering and micro/nanoelectronics to chemical engineering to biology. Polymer derived ceramics (PDCs) represent a relatively new class of materials within additive manufacturing. PDCs enable the development of ceramic parts patterned via low-cost polymer 3D printing methods followed by pyrolysis in a high temperature process in which the polymer itself forms a ceramic often in the absence of any ceramic filler. PDCs have served as a feedstock for various 3D printing techniques for which a wide range of physiochemical factors can be tailored to optimize the ceramic manufacturing processes. In particular, the silicon and carbon-rich polymeric microstructure of PDCs offers a high degree of tunability and potential to achieve a closely defined combination of functional, thermomechanical, and chemical properties. In this review, we cover mechanisms underlying the design and manufacture of ceramics via 3D printing and pyrolysis of preceramic polymers, focusing on chemical formulations, printing technologies, and the mechanical performance of the ceramic network from microscale to scale. We also summarize experimental data from the literature and present qualitative and quantitative comparisons between different AM routes to provide a comprehensive review for 3D printing of PDCs and to highlight potential future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".