Charge-Sharing Based Stabilization of Nano-Porous Organic Electrode for Rechargeable Li-Ion Battery
Bibliographic record
Abstract
Organic-based lithium-ion batteries have garnered increasing attention due to their potential for cost-effectiveness, sustainability, and high energy density. Despite the promising theoretical prediction, the actual battery performance of organic-based electrodes has frequently fallen below the expected values. This discrepancy is primarily attributed to a low density of active sites, limited ion diffusivity, and high solubility to the electrolyte. This study introduces 5,10-dihydro-5,10-dimethylphenazine (DMPZ) as an organic active material, demonstrating superior electrochemical performance characterized by high capacity and prolonged cycle performance. To achieve a high capacity, a cryogenic milling is employed to create a porous nanostructure, enhancing the surface-to-volume ratio without altering the molecular structure. Consequently, the initial specific capacity of the porous DMPZ electrode reaches 184 mAh g -1 at 0.6 C, representing a remarkable 180% increase compared to non-milled DMPZ. To improve cycle stability, a charge-sharing reaction among organic active materials is facilitated by forming an organic nanocomposite comprising DMPZ and various n-type organic materials. The organic nanocomposite effectively mitigates the elution of organic active materials, ensuring unprecedented cycling stability with a capacity retention exceeding 90% over 500 cycles with initial specific capacity of 248 mAh g -1 . C-rate tests at 1.2, 2, 4, and 8 C demonstrate outstanding rate capability, with an average capacity of 105 mAh g -1 at 8 C. The performance enhancement mechanism of the organic nanocomposite cathode is elucidated through experimental analyses, including ex-situ XPS, PiFM, and FTIR. These analyses collectively contribute to a comprehensive understanding of the mechanisms underlying the observed improvements in battery performance.
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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 | 0.001 | 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 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".