Mathematical Modelling for Bone Cement MMA Free Radical Polymerization Process
Bibliographic record
Abstract
For more than 50 years, artificial joints are fastened efficiently by bone cements. Bone cements play an important role in the elastic zone. In human hip joint, about ten to twelve times of the body weight acts upon the hip joint. This gives rise to the need of the bone cement to absorb the forces acting upon the human hip joint. Plexiglas, which is Poly Methyl Methacrylate (PMMA) is the material of choice for obtaining bone cements. Three methods are conducted to produce PMM4; namely, the emulsion polymerization, solution polymerization and bulk polymerization. From these methods; in-situ and in-vivo extremely exothermic reactions of free radical bulk polymerization are used to produce PMM4 bone cements. Radical polymerization gives atactic and amorphous PMM4. Also, aseptic loosening is caused by residual monomer which remains unreacted in the body. Free radical polymerization models can describe the bone cement production effectively and are used for quantitative analysis of its synthesis. In this research, bone cement production is mathematically investigated based on multi-cell reactor. Solubility of the pre-polymer powder in the liquid monomer has shown to be the most important variable during the preparation process and that it should be tuned to control the real operation of bone cement production.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".