Revealing the electrochemical evolution of nitroso-to-azo cathodes for lithium-ion storage
Bibliographic record
Abstract
In this paper, aromatic nitroso compounds are investigated for their electrochemical performance as organic cathodes for lithium-ion batteries, with an elucidation of their redox mechanism. For the first time, ex situ spectroscopic analyses of small-molecule mono-nitroso compounds (including nitrosobenzene, 2-nitrosotoluene, 4-nitrosobenzoic acid, and lithium 4-nitrosobenzoate) reveal a universal, irreversible transformation of their nitroso dimers to azo species during the first electrochemical cycle, establishing azo groups as the true redox-active centers. Unlike small-molecule analogues that convert rapidly, polymeric 1,4-dinitrosobenzene (DNSB) evolves gradually through azoxy intermediates, accompanied by increased conjugation and enhanced structural stability. To enhance utilization efficiency and cycling stability, DNSB is encapsulated in porous carbons (BP2000, Ketjenblack) via solution infusion, yielding composites with a high initial capacity of 383 mAh g −1 and a stable reversible capacity of 102 mAh g −1 after 800 cycles at 150 mA g −1 . Electrochemical impedance spectroscopy confirms reduced charge-transfer resistance and improved interfacial stability. This work uncovers the fundamental nitroso-to-azo conversion mechanism and demonstrates a scalable carbon-confinement strategy to construct durable polymeric azo-type organic electrodes from nitroso compounds for sustainable energy storage.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".