Nitroaromatics as High‐Energy Organic Cathode Materials for Rechargeable Alkali‐Ion (Li<sup>+</sup>, Na<sup>+</sup>, and K<sup>+</sup>) Batteries
Bibliographic record
Abstract
Abstract Organic cathode materials with existing redox functionalities are attracting increasing attention for rechargeable alkali‐ion batteries due to their high theoretical gravimetric capacity, molecular diversity, and sustainability. However, they are still restricted in specific capacity and energy density. The discovery of new multielectron redox‐active functionalities that can impart significantly enhanced capacity and energy density is highly desired. Herein, a group of organic nitroaromatic compounds (p‐, o‐, and m‐dinitrobenzene (DNB)) is reported as novel high‐energy cathode materials for alkali‐ion batteries. For the first time, nitro groups in DNBs are found to undergo an electrochemically reversible two‐step two‐phase reaction at a voltage above 2 V, rendering a high theoretical capacity of 638 mAh g−1. A systematic study is undertaken to reveal the reaction mechanism and verify the redox reversibility. By confining the optimum p‐DNB within a microporous carbon nanosphere host, record‐high reversible capacities of 620, 573, and 536 mAh g−1 at 50 mA g−1 are achieved for lithium‐, sodium‐, and potassium‐ion batteries, respectively. Demonstrating nitro as a unique redox‐reversible functionality, this work opens a new direction in the development of novel high‐performance organic nitroaromatic cathode materials for next‐generation alkali‐ion batteries.
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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) | 0.001 | 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".