Enhancing stability and rate performance of Li <sub>2</sub>DHBQ cathodes in lithium-ion batteries via FEC-derived cathode–electrolyte interphase
Bibliographic record
Abstract
Organic cathode materials present a promising alternative for the inorganic counterparts in conventional lithium-ion batteries (LIBs) due to lower cost, reduced environmental impact, renewability, and enhanced energy density. However, their practical application is hindered by dissolution in electrolytes, structural degradation, and sluggish lithium-ion transport. In this study, we introduce fluoroethylene carbonate (FEC) as an electrolyte additive to engineer a protective cathode-electrolyte interphase (CEI) layer, effectively mitigating cathode pulverization and enhancing battery stability of the organic cathode material, dilithium salt of 2,5-dihydroxy-1,4-benzoquinone (Li₂DHBQ). Electrochemical, morphological, and compositional analyses, including CV, EIS, SEM, TEM, and XPS, confirm that an optimal 1% FEC concentration forms a uniform CEI layer, significantly improving structural integrity and reducing interfacial resistance. Consequently, the battery with 1% FEC retains 185 mAh g⁻¹ after 200 cycles at 500 mA g⁻¹, with a capacity decay rate of just 0.049% per cycle, compared to 81 mAh g⁻¹ and 0.302% per cycle for the FEC-free battery. Additionally, the 1% FEC battery exhibits a capacitive charge storage contribution of up to 93.7%, resulting in excellent rate performance. These findings underscore the crucial role of CEI engineering in stabilizing organic cathodes, offering a practical approach to achieving high-rate and long-cycle LIBs.
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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 | 0.001 |
| 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".