Piezoelectric sensors fabricated by depositing solution-grown ZnO nanorods on flexible graphene-derivative electrodes
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Bibliographic record
Abstract
Abstract Zinc oxide nanorods (ZnO-NRs) with high-aspect ratios can significantly enhance the voltage output of mechanically flexible piezoelectric materials. A versatile chemical synthesis process for growing long narrow ZnO-NR from nanoparticle (NP) seeds by regulating the polarity of reaction solvents is introduced in this paper. The efficient nanorod (NR) growth method produces large quantities of high-aspect ratio ZnO-NRs in the reaction solvent. For ultra-small NP seeds (AVG 10.54 nm, SD 3.69), the synthesis process creates NRs with a minimal lateral growth (AVG 13.92 nm, SD 4.77) and significant longitudinal growth (AVG 150.85 nm, SD 64.93). The average aspect ratio of ZnO-NRs in the solution is ∼10.8 (SD 2.48). Once synthesized, the ZnO-NRs are mixed with polydimethylsiloxane (PDMS) to create a thin flexible piezoelectric layer/film. The composite polymer material is spin coated on an inkjet printed graphene/carboxymethyl cellulose (G-CMC) interdigitated electrode (IDE) to form the piezoelectric layer. A dielectrophoretic alignment technique is then used to reposition the NR orientations in the composite prior to final polymer curing. In this study, three different piezoelectric composites are investigated and compared: polyhedral NPs (ZnO-NP/PDMS), non-aligned nanorods (ZnO-NR NA /PDMS), and aligned nanorods (ZnO-NR A /PDMS). Each composite is deposited on a similar IDE and tested for impact loading and low frequency mechanical bending. Under bending, the NP ZnO-NP/PDMS sensor generated 3–4 mV while the non-aligned NR ZnO-NR NA /PDMS sensor produced 70–80 mV. In contrast, the horizontally aligned NR ZnO-NR A /PDMS sensor generated 150–170 mV under the same bending conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it