Gecko-inspired dry adhesives for heritage conservation – tackling the surface roughness with empirical testing and finite element modelling
Bibliographic record
Abstract
Gecko-inspired dry adhesives (GDAs) have been developed in an attempt to replicate in polymer material the natural ability of some gecko lizards to attach to nearly any surface. Geckos achieve this with nano-sized structures on their feet that facilitate van der Waals's interactions with the surfaces. The conservation of cultural heritage is an area that could benefit greatly from the introduction of a versatile and easily reversible adhesive. However, the multitude of surface types and various surface textures encountered in this field make the adaptation of GDAs difficult. In this research two types of GDAs, with flat tips and with mushroom-shaped tips have been assessed using pull-off tests on three substrate materials. These are based on real heritage objects’ surfaces (copper and ceramic) with different levels of surface roughness from The Hunterian collection. Adhesive strength varied between different GDAs and as expected adhesive strength reduced with increased substrate roughness. The finite Element Modelling (FEM) of the pull-off tests closely matched empirical results and showed how different behaviours on the microlevel can affect the GDA behaviour on rough surfaces. It helped to understand the microscale behaviour of two different types of GDAs tested. The research has shown the necessary direction for experimental and theoretical research on GDAs which will enable them to be adopted more widely in heritage conservation.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Materials testing of gecko-inspired dry adhesives for heritage conservation.
The study tests adhesives for heritage conservation rather than research methods or systems.
Materials testing of gecko-inspired adhesives for heritage conservation, domain applied science.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".