Evolution From Single to Hybrid Nanogenerator: A Contemporary Review on Multimode Energy Harvesting for Self-Powered Electronics
Bibliographic record
Abstract
Energy harvesting devices have strong potential to not only meet growing global energy demand but also support a wide range of self-powered electronics applications. Solar cells, electrochemical cells, piezoelectric/triboelectric/pyroelectric nanogenerators, and magnetoelectric energy harvesters are enabling technologies for converting solar, chemical, mechanical, thermal, and magnetic energy to electricity. Merging these harvesters to form hybrid energy cells can help optimize operation of self-powered systems, providing multimode energy harvesting capability that can leverage several energy sources either simultaneously or individually. Energy produced from these hybrid energy cells even can be stored in Li-ion batteries to power various personal electronics, sensors, and next generation technology for the Internet of Things. Ultimately, hybridization provides another degree of freedom in terms of more effective energy utility. This review presents the evolution of the hybrid energy cell concept and development, explores the fabrication approaches taken, and provides insights on the limitations of existing devices, steps toward performance optimization, and the enormous potential for these technologies to benefit myriad applications. Hybrid energy cells show higher output performance by providing better charging characteristics than individual energy harvester unit.
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.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".