Anchoring Ultrafine ZnFe<sub>2</sub>O<sub>4</sub>/C Nanoparticles on 3D ZnFe<sub>2</sub>O<sub>4</sub> Nanoflakes for Boosting Cycle Stability and Energy Density of Flexible Asymmetric Supercapacitor
Bibliographic record
Abstract
Heterostructure-based metal oxide thin films are recognized as the leading material for new generation, high-performance, stable, and flexible supercapacitors. However, morphologies, like nanoflakes, nanotubes, nanorods, and so forth, have been found to suffer from issues related to poor cycle stability and energy density. Thus, to circumvent these problems, herein, we have developed a low-cost, high surface area, and environmentally benign self-assembled ZnFe 2 O 4 nanoflake@ZnFe 2 O 4 /C nanoparticle heterostructure electrode via anchoring ZnFe 2 O 4 and carbon nanoparticles using an in situ biomediated green rotational chemical bath deposition approach for the first time. The synthesized ZnFe 2 O 4 nanoflake@ZnFe 2 O 4 /C nanoparticle heterostructure thin films demonstrate an excellent specific capacitance of 1884 F g –1 at a current density of 5 mA cm –2 . Additionally, all solid-state flexible asymmetric supercapacitor devices were designed on the basis of ZnFe 2 O 4 nanoflake@ZnFe 2 O 4 /C nanoparticle heterostructures as the negative electrode and reduced graphene oxide and energy density of 81 Wh kg –1 at a power density of 3.9 kW kg –1 . Similarly, the asymmetric device exhibits ultralong cycle stability of 35 000 cycles by losing only 2% capacitance. The excellent performance of the device is attributed to the self-assembled organization of the heterostructures. Moreover, the in situ biomediated green strategy is also applicable for the synthesis of other metal oxide and carbon-based heterostructure electrodes.
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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) | 0.000 | 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 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".