Highly Sensitive, Stretchable, and Adjustable Parallel Microgates‐Based Strain Sensors
Bibliographic record
Abstract
Abstract The demand for stretchable strain sensors with customizable sensitivities has increased across a spectrum of applications, spanning from human motion detection to plant growth monitoring. Nevertheless, a major challenge remains in the digital fabrication of scalable and cost‐efficient strain sensors with tailored sensitivity to diverse demands. Currently, there is a lack of simple digital fabrication approaches capable of adjusting strain sensitivity in a controlled way with no changes to the material and without affecting the linearity. In this study, parallel microgates‐based strain sensors whose strain sensitivity can be adjusted systematically throughout an all‐laser‐based fabrication process without any material replacement are presented. The technique employs a two‐step direct laser writing method that combines the well‐established capabilities of laser ablation and laser marking, boasting a varying gauge factor of up to 433% (GF = 168), while paving the way for the mass production of nanocomposite strain sensors. Parallel microgates‐based strain sensors exhibit a remarkable signal‐to‐noise ratio at ultralow strains (ɛ = 0.001), rendering them ideal for monitoring the gradual growth of plants. As an application demonstration, the proposed sensors are deployed on tomato plants to capture their growth under varying planting conditions including hydroponic and soil mediums, as well as diverse irrigation regimens.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".