Adhesion Force Studies of Nanofibers and Nanoparticles
Bibliographic record
Abstract
Surface adhesion between nanofibers and nanoparticles has attracted attention for potential biomedical applications, but the measurement has not been reported. Adhesion forces were measured using a polystyrene (PS) nanoparticle attached to an atomic force microscopy (AFM) tip/probe. Electrospun PS nanofibers of different diameters were tapped with the probe to study the effect of fiber diameters on adhesion force. Both AFM experiments and numerical models suggest that the adhesion force increases with increased fiber diameters. Numerical models further demonstrated that local deformation of the fiber surface, including the flattening of surface asperities and the nanofiber wrapping around the particle during contact, may have a significant impact on the adhesion force. The adhesion forces are in the order of 100 nN, much smaller than the adhesion forces of the gecko foot hair, but much larger than that of the receptor-ligand pair, antibody-antigen pair, and single-stranded DNA from a substrate. Adhesion forces of nanofibers with roughness were predicted by numerical analysis. This study is expected to provide approaches and information useful in the design of nanomedicine and scaffold based on nanofibers for tissue engineering and regenerative medicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".