Exploring mesoamerican pyramidal micro-structures in soft capacitors for positive and negative pressure sensing
Bibliographic record
Abstract
Abstract Dual-range sensors are prevalent in pressure measurement technologies, particularly in applications requiring a single, versatile sensor with good sensitivity. In this work, we introduce a straightforward out-of-cleanroom fabrication process for a dual-range capacitive pressure sensor. Our sensor is based on micro-structured polydimethylsiloxane dielectric surfaces featuring Mesoamerican pyramidal patterns (MAPs), which are easily replicated via the use of a readily available breadboard mold. We conducted a comprehensive comparative analysis of the newly designed MAP-shaped dielectric-patterned capacitive sensors against other commonly used patterned dielectrics, such as tetrahedrons, and pillars. Our results revealed that capacitors constructed with MAP structures exhibited significantly higher sensitivity at higher pressure levels while maintaining comparable sensitivities at lower pressure ranges. Specifically, the sensitivity was approximately 0.135 kPa −1 for pressures below 10 kPa, 0.563 kPa −1 for pressures between 10 kPa and 50 kPa, and 70 kPa −1 for pressures between 50 kPa and 1 MPa, respectively. To gain further insights, we conducted an in-depth characterization of the MAP-structured dielectric, examining its response to tensile loading and unloading, cyclic loading, dynamic behavior, and positive-negative pressure responses. Furthermore, we meticulously optimized the geometrical parameters of the MAP structures, including variations in base width, interstructural distance, and base height. This optimization aimed to increase the device sensitivity across various pressure regimes. Finally, we demonstrated its applicability in both positive and negative pressure sensing. The research findings presented in this paper offer valuable contributions to the field of pressure sensing technology, opening possibilities for enhanced performance and broader applications in pressure measurement systems.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".