Review of progress in 4D printing of piezoelectric energy harvesters
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
The fabrication of piezoelectric energy harvesters (PEHs) has evolved significantly over the past three decades, transitioning from mechanization to automation and computerization. Additive manufacturing (AM), a forefront technology in advanced manufacturing, has been extensively used to design and produce complex components from piezoelectric materials. By integrating the fourth dimension, we can improve the fabrication of PEHs, allowing them to alter their shape while converting mechanical stress into electrical energy, thus adding dynamic functionality and broadening their application spectrum. Despite the extensive literature on 3D printing of piezoelectric materials and 4D printing technology, a notable research gap exists in merging these two fields. This review aims to bridge this gap by providing a comparative analysis of 3D-printed piezoelectric materials and shape memory materials, discussing the relevant AM technologies, stimuli, and smart materials, and highlighting innovative integration methods. Furthermore, we explore a novel approach termed '4D printing of piezoelectric energy harvesters.' This innovative method merges the principles of 4D printing with the advanced capabilities of 3D printing of piezoelectric materials, resulting in multifunctional devices that can adapt and respond to external stimuli over time. The article also addresses the challenges and opportunities in optimizing AM processes to enhance the performance and functionality of these advanced materials and devices.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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