Piezoelectric sensors fabricated by depositing solution-grown ZnO nanorods on flexible graphene-derivative electrodes
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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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) | 0.001 | 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 | 0.001 | 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".