A Comprehensive Review on Piezoelectric Polymeric and Ceramic Nanogenerators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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, d 33 = 2500 pCN −1 ) and lead zirconate titanate, d 33 = 500–600 pCN −1 ) PNGs, generate the highest output voltage and current density among all piezoelectric materials. However, the piezoelectric coefficient d 31 (−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 d 33 (250−1400 pC N −1 ), although their d 31 is lower than piezoelectric poly(vinylidene fluoride) (PVDF) polymer. Piezoelectric natural polymers such as cellulose ( d 33 ≈ 8−28 pC/N, silk ( d 33 ≈ 0.3−0.8 pC/N, and collagen ( d 33 ≈ 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.000 |
| 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 it