Determining adhesion of nonuniform arrays of fibrils
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dry adhesives containing nonuniform arrays of fibrils were tested for the uniformity of their adhesion strength. These arrays comprised fibrils with nanometer-scale dimensions and lengths tuned from 150 to 1500 nm. The surfaces of the fibrils were rendered hydrophobic through a vapor phase deposition of silane molecules to further tune the adhesion strength of the fibrillar structure. Adhesion force measurements over micrometer-length scales were obtained using a tipless cantilever controlled by a scanning probe microscope. Maps of the adhesion forces depicted diverse variations in adhesion strength with the nonuniform lateral changes in topography. Through an extensive data analysis, differences observed between samples were correlated to changes in processing conditions and surface chemistry modifications. The methods demonstrated in this paper are useful for identifying variations in the adhesion strength of dry adhesives made of nonuniform arrays of fibrils. These advancements are crucial for understanding the correlation between structure and function within nonuniform fibrillar adhesives.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it