Novel Linear Piezo‐resistive Auxetic Meta‐Sensors with Low Young's Modulus by a Core–Shell Conceptual Design and Electromechanical Modelling
Bibliographic record
Abstract
Abstract Production of piezo‐resistive auxetic sensors is usually carried out through mixing and coating methods. Although these methods are beneficial, Young's modulus of mixed sensors becomes high because of using a high percentage of sensing elements while the durability of coated sensors gets low due to the separation of sensing elements from the sensor surface. This article presents a new core–shell metamaterial model to address the mentioned problems. The shell and the core are produced of polydimethylsiloxane (PDMS) rubber and a mixture of PDMS/graphite powders (73.45 wt% graphite powders), respectively. A finite element model is developed via COMSOL software to predict the electromechanical behaviors of the created sensor and verified by an experimental study. Scanning electron microscope imaging is conducted to detect the separations of the graphite particles. The main important feature of this meta‐sensor is to possess a linear sensitivity due to having zero Poisson's ratio. The advantage of this method is that Young's modulus of the sensor does not decrease (unlike the mixing method), and the sensor‐coated particles do not separate from the sensor after a while (unlike the coating method). The introduced model has advantages that promote potential applications such as using sensory gloves to detect, for instance, human hand movements.
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 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) | 0.001 | 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".