Bioinspired Physico‐Chemical Surface Modifications for the Development of Advanced Retentive Systems
Bibliographic record
Abstract
Abstract A major aspiration in advanced materials is to create artificial adhesive surfaces for wearable medical devices to meet the demands of the body's challenging settings and dynamics. For instance, dentures replace missing teeth and operate within the oral cavity, where an interplay between forces, muscles, saliva, and roughness of mucosa undermine their ability to grip oral tissues. Consequently, the lack of effective retentive strategies represents a source of dissatisfaction for denture wearers globally. Nature is rich in examples that employ physical and chemical adhesive strategies to optimize interfacial forces in dry and wet environments. Here, keratin‐coated octopus‐like suction cups are presented at the micro‐ and macroscale to improve the retention of rigid poly(methyl methacrylate). Microtopographies are obtained using two‐photon polymerization and maskless lithography, while denture prototypes with macrotopographies are derived via digital light processing 3D printing. Results suggest that microtopographies and keratin‐coated surfaces sustain higher maximum adhesion stress than the non‐topographical and non‐coated surfaces in moist environments, where retention is typically lacking. Proof‐of‐concept dentures demonstrate higher maximum detachment forces than conventional dentures with and without denture adhesive within dry and wet environments. This interdisciplinary research highlights the potential application of a nature‐inspired physico‐chemical approach in the next generation of complete dentures.
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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) | 0.000 | 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".