Scalable High-Power Redox Capacitors with Aligned Nanoforests of Crystalline MnO<sub>2</sub>Nanorods by High Voltage Electrophoretic Deposition
Bibliographic record
Abstract
It is commonly perceived that reduction-oxidation (redox) capacitors have to sacrifice power density to achieve higher energy density than carbon-based electric double layer capacitors. In this work, we report the synergetic advantages of combining the high crystallinity of hydrothermally synthesized α-MnO2 nanorods with alignment for high performance redox capacitors. Such an approach is enabled by high voltage electrophoretic deposition (HVEPD) technology which can obtain vertically aligned nanoforests with great process versatility. The scalable nanomanufacturing process is demonstrated by roll-printing an aligned forest of α-MnO2 nanorods on a large flexible substrate (1 inch by 1 foot). The electrodes show very high power density (340 kW/kg at an energy density of 4.7 Wh/kg) and excellent cyclability (over 92% capacitance retention over 2000 cycles). Pretreatment of the substrate and use of a conductive holding layer have also been shown to significantly reduce the contact resistance between the aligned nanoforests and the substrates. High areal specific capacitances of around 8500 μF/cm(2) have been obtained for each electrode with a two-electrode device configuration. Over 93% capacitance retention was observed when the cycling current densities were increased from 0.25 to 10 mA/cm(2), indicating high rate capabilities of the fabricated electrodes and resulting in the very high attainable power density. The high performance of the electrodes is attributed to the crystallographic structure, 1D morphology, aligned orientation, and low contact resistance.
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 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 | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".