Enhancing Porous Alumina Ceramics for Bioapplications Through Targeted Surface Modification Techniques
Bibliographic record
Abstract
Porous bioceramics are commonly used as support structures for bone growth and repair.However, their mechanical properties have been limited by high macroporosity and microporosity.Alumina ceramics (Al2O3) are preferred for bone implants due to their mechanical reliability, chemical stability, and biological compatibility.However, the bioinert nature of aluminum oxide makes it challenging for bone ingrowth and implant anchorage.To address this, ceramic scaffold samples with concentrated porosity have been developed.This study aimed to create porous Al2O3 and explore surface modifications using UV exposure, amino acid treatment (L-lysine), and alkaline treatment (NaOH) for applications in orthopedics and dentistry.The porous alumina samples underwent sintering at 1400℃ and drying at 100℃.They were then analyzed for mechanical, morphological, and structural characteristics through various tests and microscopy techniques.The results revealed increased surface roughness after 16 hours of alkaline treatment and one hour of UV treatment, and decreased roughness with amino acid treatment for samples pre-treated with NaOH for 16 hours.Our results showed an increase in porosity with Alkaline and UV treatment and decrease with amino acid treatment, also results shows decrease in mechanical properties for alkali and UV treatments (Hardness and Compressive Strength) as it is in the range of cancellous bone strength.According to the current work, the contact angle between the untreated and treated samples with UV, amino acid, and NaOH was zero, which indicates that these surfaces are hyper hydrophilic.High porosity and surface roughness may be the cause of this behavior.Biological test evaluated by XRD and FESEM exhibited formation of Hydroxyapatite film when immersed in SBF for 7 days.
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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 | 0.001 |
| 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 | 0.000 |
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 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".