Adenine and D‐Ribose Coderived Activated Carbon with N‐Methyl‐2‐Pyrrolidone‐Modified Aqueous Electrolyte for Long‐Life Zinc‐Ion Capacitors
Bibliographic record
Abstract
Aqueous zinc (Zn)‐ion capacitors (AZICs) have addressed considerable attention due to their high energy density, low toxicity, and rich abundance of Zn metal. However, the development of ultra‐long cycle life and high energy density AZICs is often hindered by the lack of adequately optimized active carbon (AC) electrodes and compatible electrolytes. Herein, high‐performance, free‐standing AC electrodes for AZICs are derived from sustainable precursors—adenine and D‐ribose—using magnesium chloride hexahydrate as an activation agent via a eutectic template strategy. Furthermore, an aqueous hybrid electrolyte tailored to the designed AC electrodes is developed, significantly enhancing the stability and cycle life of AZICs. The resulting AZIC achieves a high specific capacity of 164.39 F g −1 at 0.1 A g −1 and a magnificently long cell life of over 50 000 cycles with nearly 94.5% capacitance retention at 10 000 th cycles, and 76.3% at 50 000 th cycle. The pouch cell assembly also demonstrates comparable specific capacitance and energy density to the coin cell, underscoring the potential of large‐scale applications of AZICs.
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".