Mechanical Behavior of Oil-Saturated Silicone Membranes for Adipose Tissue Synthesis in Clinical and Theatrical Prosthesis
Bibliographic record
Abstract
Emulating very soft tissues with synthetic materials is important for clinical prosthetists who want to improve compliance in maxillofacial and breast prosthesis. It is equally important for theatrical prosthetists wanting to model bariatric conditions and soft organs for surgical or palpation training. Polydimethylsiloxane (PDMS) gels, which are often used in medical model construction, are stiff and highly elastic compared to the friable soft tissues found in the body. Silicone oil is known to soften PDMS gels, but it is not known precisely how oil dispersal affects these gels and what proportion of oil is needed to simulate very soft tissue membranes like adipose tissue. In this work, internationally agreed test standards were used to mechanically characterize a range of PDMS gel membranes saturated with different amounts of silicone oil to determine whether materials with behavioral similarities to adipose tissue could be created. Mechanical properties like hardness, elasticity, strength, viscoelastic behavior and cure-time are presented in this study, which are all key factors required by the creators of such membranes. Results were compared to identical tests on porcine fat and data in the literature for porcine and human fat. The data revealed a strong correlation between increases in oil content and decreases in membrane hardness, strength and elastic modulus. It was also found that increases in oil content caused proportional increases in cure time, while membranes with equal amounts of oil and gel were best at mimicking characteristics of human and porcine fat, like hardness and elasticity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".