Design of a MRI-compatible dielectric elastomer powered jet valve
Bibliographic record
Abstract
Binary Pneumatic Air Muscles (PAM) arranged in an elastically-averaged configuration can form a cost effective solution for Magnetic Resonance Imaging (MRI) guided robotic interventions like prostate cancer biopsies and brachytherapies. Such binary pneumatic manipulators require about 10 to 20 MRI-compatible valves to control the pressure state of each PAM. In this perspective, this paper presents the design of a novel dielectric elastomer actuator (DEA) driven jet-valve to control the states of the PAMs. DEAs are MRI compatible actuators that are well suited to the simplicity and cost-effectiveness of the binary manipulation approach. The key feature of the proposed valve design is its 2 stages configuration in which the pilot stage is moved with minimal mechanical friction by a rotary antagonistic DEA made with acrylic polymer films. The prismatic geometry also integrates the jet nozzle within the DEA volume to provide a compact embodiment with a reduced number of parts. The low actuation stretches enabled by the rotary configuration minimize viscoelastic losses, and thus, maximize the frequency response of the actuator while maximizing its reliability potential. The design space of the proposed jet valve is studied using an Ogden hyperelastic model and the valve dynamics is predicted with a 1D Bergstrom-Boyce viscoelastic model. Altogether, the low friction of the pilot stage and optimized DEA dynamics provide an experimental shifting time of the complete assembly in the 200-300ms range. Results from this work suggest that the DEA driven jet valve has great potential for switching a large number of pneumatic circuits in a MRI environment with a compact, low cost and simple embodiment.
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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 | 0.000 | 0.000 |
| 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".