Fabrication and calibration of a piezoelectric nanocomposite paint
Bibliographic record
Abstract
Abstract A new liquid form piezoelectric nanocomposite paint material is fabricated with possible applications as dynamic strain sensors and/or piezoelectric transducers. The applied coating is in the form of low-cost paint, which is flexible and bonds strongly on a metallic surface after drying out via the solvent-casting method. The nanocomposite is produced by an ultrasonic mixture of varying percentages of zinc oxide (ZnO) nanoparticle water dispersion, poly vinyl acetate glue (PVA) and carbon nanotubes (CNTs). ZnO nanoparticles are used as the piezoelectric sensing elements in a PVA matrix of the paint, while CNTs are introduced as robust bridge of ZnO particles enhancing the piezoelectricity and material properties. Transmission electron microscopy (TEM) images confirmed the linkages of ZnO nanoparticles in the composite by CNTs. Through piezoelectricity calibration, the optimum mixing ratio with the highest piezoelectricity is 78.1 wt% ZnO, 19.5 wt% PVA glue and 2.4 wt% multi-wall carbon nanotubes (MWCNTs). Through nanoindentation tests for the characterization of the mechanical properties of the nano-composite paint, it is found that Young’s modulus and hardness reached a threshold point in the increment in the addition of CNTs to the paint before showing signs of decline. Detailed analysis and explanation of the calibration results and physical phenomenon are provided. The stable paint material is ready to be applied on rough area of engineering structures as sensor and transducer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".