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.
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 | 0.000 | 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".