Artificial Muscle Membranes Fabricated Using Ultra-Short Pulse Laser Ablation
Bibliographic record
Abstract
An Ultra Short Pulse (USP) laser has been used for surface milling of dielectric elastomer actuator (DEA) polymer (3M VHB tape). DEA’s, artificial analogues to natural muscle, act as flexible capacitors and consist of a polymeric dielectric membrane material sandwiched between flexible electrodes. Actuation is in part determined by the voltage difference across the electrodes and the dielectric membrane thickness. Prior to the present report, pre-stretching the membrane provided the sole means for controlling membrane thickness. Good quality ablation, without grossly observable thermal damage, was achieved at removal rates of 0.0047 mm3/s using a 250 kHz repetition rate and an energy delivery of 2 μjoules/pulse. The surface milling operation was performed as a set of parallel passes produced a series of small shallow trenches, the result of overlap between subsequent passes of the laser. These features did not significantly compromise performance of the test actuators and the USP machined polymer could be pre-stretched at least five times the original size without rupture. When placed in a support frame and subjected to high electric field strength the stretched membrane also maintained its relatively high breakdown strength. A proof of concept two-thickness actuator, one half of it milled, was produced and successfully actuated. USP laser processing has provided a design freedom, beyond pre-stretching, and has opened the way to novel variable thickness micro-actuators.
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".