Fabrication of Multi‐Layered Paper‐Based Supercapacitor Anode by Growing Cu(OH)<sub>2</sub> Nanorods on Oxygen Functional Groups‐Rich Sponge‐Like Carbon Fibers
Bibliographic record
Abstract
Abstract This work addresses the challenges in developing carbon fiber paper‐based supercapacitors (SCs) with high energy density by focusing on the limited capacity of carbon fiber. To overcome this limitation, a sponge‐like porous carbon fiber paper enriched with oxygen functional groups (OFGs) is prepared, and Cu(OH)2 nanorods are grown on its surface to construct the SC anode. This design results in a multi‐layered carbon fiber paper‐based electrode with a specific structure and enhanced capacitance. The Cu(OH)2@PCFP anode exhibits an areal capacitance of 547.83 mF cm−2 at a current density of 1 mA cm−2 and demonstrates excellent capacitance retention of 99.8% after 10 000 cycles. Theoretical calculations further confirm that the Cu(OH)2/OFGs‐graphite heterostructure exhibits higher conductivity, facilitating faster charge transfer. A solid‐state SC is successfully assembled using Ketjen Black@PCFP as the cathode and KOH/PVA as the gel electrolyte. The resulting device exhibits an energy density of 0.21 Wh cm−2 at 1.50 mW cm−2, surpassing the performance of reported Cu(OH)2 SCs. This approach, combining materials design with an understanding of underlying mechanisms, not only expands the range of electrode materials but also provides valuable insights for the development of high‐capacity energy storage devices.
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 | 0.000 |
| 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".