Simultaneous strain, strain rate and temperature sensing based on a single active layer of Te nanowires
Bibliographic record
Abstract
Stress/strain-temperature sensors are capable of sensing both stress/strain and temperature stimuli, and are widely used in biological health monitoring and human-machine interaction. Conventional stress/strain-temperature sensors are prepared by stacking two single sensors, which have complex structures and often require external power to drive, making long-term stable monitoring challenging. Herein, we demonstrate a flexible single-channel multimodal sensor based on the combined thermoelectric and piezoelectric effects of tellurium nanowires. Based on the tilt-grown reticulated nanowire structure, the sensor can simultaneously sense strain/strain rate and temperature in a single channel of a single active layer of nanowire. The sensor exhibits a record-high strain/strain rate sensing performance with a strain sensing sensitivity of 0.454 V and a strain rate sensing sensitivity of 0.0154 V s, surpassing previous study benchmarks. Additionally, it showcases significant temperature-sensing performance with a sensitivity of 225.1 μV K−1. The origin of the piezoelectric effect of the sensor is attributed, by experimental and computational evidence, to the change in atomic charge when the Te nanowires are bent, and it can be modulated by external electric fields, such as a thermoelectric potential. Our results provide insights for designing and fabricating high-performance flexible single-channel multimodal sensors. This study presents a multifunctional sensor based on Te nanowires that utilizes the thermo-piezoelectric effect to achieve simultaneous sensing of strain, strain rate, and temperature.
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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) | 0.000 | 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 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".