Tuning Electrochemical Properties of Nitroaromatic Cathodes by Function‐Oriented Design for Rechargeable Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Rechargeable lithium‐ion batteries (LIBs) based on organic cathodes are an attractive alternative energy storage technology owing to the low cost and sustainability. Recently, nitro functionality in dinitrobenzenes is successfully demonstrated as an electrochemically reversible high‐capacity redox group. In this study, a function‐oriented design is employed to further disclose the effects of substituting functional groups and molecular conjugate structures on electrochemical properties of a range of nitroaromatic derivatives as organic cathodes for rechargeable LIBs. In specific, it is revealed that the redox potential of nitroaromatic cathodes can be effectively adjusted by introducing distinct electronically inducible functional groups, while the cyclic life can be significantly prolonged with the introduction of the hydrophilic groups. When constructed with extended π‐conjugated structures, the electronic conductivity and electrochemical kinetics of nitroaromatics are increased significantly owing to their various long‐range π–π stacking. Moreover, density‐functional theory calculations further provide theoretical insights into the distinct electrochemical behaviors of the various nitroaromatics in the molecular level. This is the first study that reveals the influences of substituting groups and conjugated structures on the electrochemical performance of nitroaromatic cathode materials, which enables a function‐oriented molecular design of such organic materials and sheds light on their future development.
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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".