On the modeling and characterization of an interlocked flexible electronic skin
Bibliographic record
Abstract
Development of an electronic skin with ultra-high pressure sensitivity is now of critical importance due its broad range of applications including prosthetic skins and biomimetic robotics. Microstructured conductive composite elastomers can acquire mechanical and electrical properties analogous to those of natural skin. One of the most prominent features of human skin is its tactile sensing property which can be mimicked in an electronic skin. Herein, an electrically conductive composite comprising polydimethylsiloxane and conductive fillers is used as a flexible and stretchable piezoresistive sensor. The electrical conductivity is induced within the elastomer matrix via carbon nanotubes whereas the piezoresistivity is obtained by means of microstructuring the surface of the substrate. An interlocked array of pyramids in micro-scale allows the change in the contact resistance between two thin layers of the composite upon application of an external load. Deformation of the interlocked arrays endows the sensor with an ultra-high sensitivity to the external pressures within the range of human skin perception. Moreover, using finite element analysis, the change in the contact are between the two layers was captured for different geometries. The structure of the sensor can be optimized through an optimization model in order to acquire maximum sensitivity.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".