Self‐Powered Piezo‐Supercapacitors Based on <scp>ZnO</scp>@<scp>Mo‐Fe‐MnO<sub>2</sub></scp> Nanoarrays
Bibliographic record
Abstract
The development of self‐charging supercapacitor power cells (SCSPCs) has profound implications for smart electronic devices used in different fields. Here, we epitaxially electrodeposited Mo‐ and Fe‐codoped MnO2 films on piezoelectric ZnO nanoarrays (NAs) grown on the flexible carbon cloth (denoted ZnO@Mo‐Fe‐MnO2 NAs). A self‐charging supercapacitor power cell device was assembled with the Mo‐ and Fe‐codoped MnO2 nanoarray electrode and poly(vinylidenefluoride‐co‐trifluoroethylene) (PVDF‐Trfe) piezoelectric film doped with BaTiO3 (BTO) and carbon nanotubes (CNTs) (denoted PVDF‐Trfe/CNTs/BTO). The self‐charging supercapacitor power cell device exhibited an energy density of 30 μWh cm−2 with a high power density of 40 mW cm−2 and delivered an excellent self‐charging performance of 363 mV (10 N) driven by both the piezoelectric ZnO nanoarrays and the poly(vinylidenefluoride‐co‐trifluoroethylene) piezoelectric film doped with BaTiO3 and carbon nanotubes. More intriguingly, the device could also be self‐charged by 184 mV due to residual stress alone and showed excellent energy conversion efficiency and low self‐discharge rate. This work illustrates for the first time the self‐charging mechanism involving electrolyte ion migration driven by both electrodes and films. A comprehensive analysis strongly confirmed the important contribution of the piezoelectric ZnO nanoarrays in the self‐charging process of the self‐charging supercapacitor power cell device. This work provides novel directions and insights for the development of self‐charging supercapacitor power cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".