Enhancement of Mechanical Properties of PMMA Reinforced with a Composite of Bi2O3:Fe2O3 for Radiation Application
Bibliographic record
Abstract
This investigation aimed to improve the mechanical and radiation shielding capabilities of polymethyl methacrylate (PMMA) by incorporating a (Bi2O3:Fe2O3) nanoparticulate composite.Doping levels were systematically varied at weight percentages of 0.5%, 1%, 3%, and 5%.Comprehensive analyses, including tensile strength, strain, hardness, structural, and morphological evaluations, were conducted.The structural transformation of PMMA was confirmed by X-ray diffraction, revealing a cubic phase post-doping.Scanning electron microscopy (SEM) images elucidated a range of crystalline sizes upon nanoparticle integration.Mechanical property assessments indicated a significant enhancement in tensile strength, which escalated from 5.45 MPa in the undoped matrix to 14.85 MPa at the highest doping concentration.However, the distribution of stress within the PMMA:(Bi2O3:Fe2O3) composites was observed to be non-uniform.Furthermore, the impact strength demonstrated a marked increase in the specimens containing 0.5% and 1% wt. of (Bi2O3:Fe2O3), suggesting an optimal doping threshold for impact resistance.Shore D hardness measurements also reflected this trend of improvement, with values rising from 71.6 in the pure PMMA to 89 in the composites as the doping ratio increased.Collectively, these findings underscore the potential of (Bi2O3:Fe2O3) nanoparticles to fortify PMMA matrices, offering promising avenues for the development of advanced materials with tailored properties for protective applications against ionizing radiation.
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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 | 0.000 |
| 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 | 0.000 |
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".