Piezoelectric nanogenerators for self‐powered wearable and implantable bioelectronic devices
Bibliographic record
Abstract
One of the recent innovations in the field of personalized healthcare is the piezoelectric nanogenerators (PENGs) for various clinical applications, including self-powered sensors, drug delivery, tissue regeneration etc. Such innovations are perceived to potentially address some of the unmet clinical needs, e.g., limited life-span of implantable biomedical devices (e.g., pacemaker) and replacement related complications. To this end, the generation of green energy from biomechanical sources for wearable and implantable bioelectronic devices gained considerable attention in the scientific community. In this perspective, this article provides a comprehensive state-of-the-art review on the recent developments in the processing, applications and associated concerns of piezoelectric materials (synthetic/biological) for personalized healthcare applications. In particular, this review briefly discusses the concepts of piezoelectric energy harvesting, piezoelectric materials (ceramics, polymers, nature-inspired), and the various applications of piezoelectric nanogenerators, such as, self-powered sensors, self-powered pacemakers, deep brain stimulators etc. Important distinction has been made in terms of the potential clinical applications of PENGs, either as wearable or implantable bioelectronic devices. While discussing the potential applications as implantable devices, the biocompatibility of the several hybrid devices using large animal models is summarized. This review closes with the futuristic vision of integrating data science approaches in developmental pipeline of PENGs as well as clinical translation of the next generation PENGs. STATEMENT OF SIGNIFICANCE: Piezoelectric nanogenerators (PENGs) hold great promise for transforming personalized healthcare through self-powered sensors, drug delivery systems, and tissue regeneration. The limited battery life of implantable devices like pacemakers presents a significant challenge, leading to complications from repititive surgeries. To address such a critical issue, researchers are focusing on generating green energy from biomechanical sources to power wearable and implantable bioelectronic devices. This comprehensive review critically examines the latest advancements in synthetic and nature-inspired piezoelectric materials for PENGs in personalized healthcare. Moreover, it discusses the potential of piezoelectric materials and data science approaches to enhance the efficiency and reliability of personalized healthcare devices for clinical applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".