Bibliographic record
Abstract
In this thesis, the focus is on the interaction between biological materials, like skin, and non-living materials, particularly in the context of medical implants, prostheses, and orthoses. These interactions are crucial for the functionality and durability of such devices. The type of contact varies depending on whether the device is inside or outside the body.For internal applications, like knee or hip prostheses, the device interfaces with bone tissue. In some cases, such as replacing a damaged meniscus, the device interacts with joint cartilage. In external devices like orthoses and skin-contacting prostheses, soft liners made of materials like polyurethane or silicone rubber help distribute pressure and minimize skin issues caused by contact pressures and friction.The thesis explores five distinct contact-related issues:1. Lubrication of polyurethane rubber with synthetic synovial fluid, highlighting the importance of proteins in lubrication behavior and differences in friction between plastic and metal surfaces.2. Influence of implant roughness in knee prosthesis compression, revealing that bone properties have minimal impact on friction, and numerical analysis accurately predicts friction based on implant roughness.3. Micro-displacement in the contact between bone and rough implant surfaces, crucial for implant fixation and revision arthroplasty, showing that bone quality and implant roughness affect limiting displacement and contact stiffness.4. Influence of adhesion on skin contact under very low pressures, where surface free energy and tension of skin and fluids significantly impact friction.5. The effect of liner roughness on friction during prostheses and orthoses use, emphasizing the role of friction in device functionality and pressure ulcer prevention.Overall, this study contributes to our understanding of tribological behavior in biomechanical interfaces, providing valuable insights into friction mechanisms in medical devices, ultimately contributing to their effectiveness and longevity.
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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 | 0.000 | 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 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".