Review of Zirconia (ZrO₂) Biomedical Applications: Advanced Manufacturing Techniques and Materials Properties
Bibliographic record
Abstract
Zirconia (ZrO₂) has emerged as a pivotal biomaterial in bone and dental repair due to its exceptional mechanical properties, including high compressive strength, crack resistance via transition toughening, and an optimal elastic modulus, alongside remarkable biocompatibility and corrosion resistance.This review highlights advancements in zirconia processing techniques-such as sintering, CAD/CAM, 3D printing, and powder processing-that enhance its microstructural integrity and mechanical performance.Clinical applications in dental restorations (crowns, bridges), orthopedic implants, and joint replacements are underscored by zirconia's non-inflammatory, non-allergic nature, ensuring long-term safety in vivo.Despite its strengths, challenges persist in fatigue resistance and wear under cyclic loads.Future directions focus on surface modification strategies and hybrid composites to improve biointegration and durability.By addressing these limitations, zirconia is poised to expand its role in next-generation biomedical implants, balancing innovation with clinical reliability.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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".