A Comprehensive Review on Piezoelectric Polymeric and Ceramic Nanogenerators
Bibliographic record
Abstract
Piezoelectric nanogenerators (PNGs) have recently received significant attention because of their great potential for harvesting electricity from wasted mechanical energy resources. In spite of many studies on piezoelectric energy harvesters, a comprehensive review that summarizes alternative types of piezoelectric materials is yet to be reported. This article categorizes piezoelectric materials into two types: piezoelectric perovskite and wurtzite micro‐/nanostructures ceramics and ferroelectric polymers and compares their energy harvesting capabilities and piezoelectric properties. Piezoelectric inorganic materials with a perovskite structure, such as lead magnesium niobate−lead titanate (PMN−PT, d33 = 2500 pCN−1) and lead zirconate titanate, d33 = 500–600 pCN−1) PNGs, generate the highest output voltage and current density among all piezoelectric materials. However, the piezoelectric coefficient d31 (−28 to ≈−69 pC N−1) of PMN−PT is lower than PZT (−175 pC N−1) and its toxicity and expensive fabrication process have limited its utilization. Cellular polypropylene (PP) as a ferroelectret polymer offers a high piezoelectric coefficient d33 (250−1400 pC N−1), although their d31 is lower than piezoelectric poly(vinylidene fluoride) (PVDF) polymer. Piezoelectric natural polymers such as cellulose (d33 ≈ 8−28 pC/N, silk (d33 ≈ 0.3−0.8 pC/N, and collagen (d33 ≈ 22 pC/N are also introduced for bio‐PNG applications to tackle environmental problems. There is still a research gap on rationally designed self‐powered, wearable, stretchable, and biocompatible PNGs with high and controllable energy conversion efficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".