Integration of PZT thick films on additively manufactured substrates for vibrational energy harvesting applications
Bibliographic record
Abstract
This work aims to integrate screen-printed thick film (50–100 µm) Pb(Zr,Ti)O 3 (PZT) on additively manufactured stainless steel substrates for vibration energy harvesting. The manufacturing technologies advanced by this research opens new horizons for energy harvesting applications as it standardizes the development and manufacturing processes, thereby making vibration energy harvesters more feasible. Since the parts are built layer-by-layer, the thickness of the different layers of the substrate can be controlled. The substrates under study were manufactured using SLM (Selective Laser Melting) additive manufacturing technology from Stainless Steel 17-4 PH powder. Simple cantilever harvesters are chosen as an initial target to focus on the optimization of the screen-printing process. The sample is 15.6 mm long, 4.1 mm wide, and 0.35 mm thick cantilever beam with a 50 µm thick PZT screen-printed layer sandwiched between two gold electrodes. A dielectric layer printed on the stainless steel substrate was introduced to promote adhesion. Clamping 6.1 mm of the beam length, its resonant frequency was measured experimentally at ∼2 kHz. The maximum output power was 37 nW under a resonant base acceleration with an amplitude of 2.94 m/s 2 and a load resistance of 90 kΩ. A good fit was found between the experiment and a Finite Element Model with a difference of 12%. Thermal stress analysis was carried out to study the impact of the difference of Coefficient of Thermal Expansion on the bending of the harvester and the adhesion between the layers. The result shows the importance of matching the Coefficient of Thermal Expansion of the substrate with that of the PZT layer to avoid delamination between the layers and to improve adhesion. These initial results open routes for optimized designs of printed piezoelectric energy harvesters.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".