Engineering ceramics for biomedical applications through nanofiller integration and 3D printing
Bibliographic record
Abstract
Abstract Known for their strength and durability, ceramic materials are often limited by their brittleness. Polymer-derived ceramics (PDCs) offer a promising alternative, enabling the fabrication of complex shapes that traditional ceramics struggle to achieve. This study introduces a cost-effective method for producing robust PDCs using low-cost liquid crystal display (LCD) 3D printing combined with strategic nanofiller integration. By incorporating nanofillers such as silicon nitride and alumina into a silicon oxycarbide precursor (SPR-684) matrix, we significantly enhanced the mechanical properties of the resultant ceramics. Optimized formulations, including a photoinitiator for vat photopolymerization, were 3D printed into complex geometries, such as gyroids and lattices, and subsequently converted to ceramics through pyrolysis. We systematically investigated the effects of varying nanofiller concentrations (0.2 to 1 wt%) on the density, microstructure, and mechanical performance of the PDC lattices. The results showed remarkable improvements, with increases of up to 2060% in toughness, 20% in stiffness, and 900% in compressive strength attributed to nanofiller integration. In terms of biocompatibility, cytotoxicity assays revealed high cell viability and proliferation on the fabricated PDC scaffolds, indicating minimal cytotoxicity and supporting cell adhesion—key attributes for tissue integration in biomedical applications. Moreover, the compressive properties of the nanofiller-enhanced ceramics closely matched those of human trabecular bone, underscoring their suitability as load-bearing bio-implants. This LCD 3D printing method offers versatility, precision, and cost-effectiveness for bioceramic fabrication, positioning these materials as promising candidates for future biomedical devices where both mechanical performance and biocompatibility are critical.
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 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".