How to control interactions of cellulose-based biomaterials with skin: the role of acidity in the contact area
Bibliographic record
Abstract
Being able to control the interactions of biomaterials with living tissues and skin is highly desirable for many biomedical applications. This is particularly the case for cellulose-based materials which provide one of the most versatile platforms for tissue engineering due to their strength, biocompatibility and abundance. Achieving such control, however, requires detailed molecular-level knowledge of the dominant interaction mechanisms. Here, we employed both biased and unbiased atomic-scale molecular dynamics simulations to explore how cellulose crystals interact with model stratum corneum bilayers, ternary mixtures of ceramides, cholesterol, and free fatty acids. Our findings show that acidity in the contact area directly affects binding between cellulose and the stratum corneum bilayer: Protonation of free fatty acids in the bilayer promotes attractive cellulose-bilayer interactions. We identified two major factors that control the cellulose-skin interactions: (i) the electrostatic repulsion between a cellulose crystal and the charged (anionic due to deprotonated fatty acids) surface of a stratum corneum bilayer and (ii) the cellulose-stratum corneum hydrogen bonding. When less than half of the fatty acids in the bilayer are protonated, the first factor dominates and there is no binding to skin. At a larger degree of fatty acid protonation the cellulose-stratum corneum hydrogen bonding prevails yielding a tight binding. Remarkably, we found that ceramide molecules are the key component in hydrogen bonding with cellulose. Overall, our findings highlight the critical role of fatty acid protonation in biomaterial-stratum corneum interactions and can be used for optimizing the surface properties of cellulose-based materials aimed at biomedical applications such as wound dressings.
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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) | 0.002 | 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".