Electrochemical Study of Functional Additives for Li-Ion Batteries
Bibliographic record
Abstract
In the battery industry, the performance of lithium-ion batteries operating at a high voltage is enhanced by utilizing functional additives in electrolytes to achieve higher energy densities and longer lifetimes. These additives chemically stabilize the electrolyte and aid in the formation of a stable cathode electrolyte interphase (CEI). In this paper, the investigation of oxidative potentials of more than 100 additives, using density functional theory calculations to determine the best candidates for CEI formation, is reported. The method was validated by comparing the calculated oxidation potentials and the experimental data obtained using linear sweep voltammetry based on the evaluation of 18 candidates. Further electrochemical studies (AC impedance and cycling stability) on six selected additives were conducted. Among the tested additives, the addition of quinacridone at 0.03% weight concentration resulted in the formation of a less resistive surface film on the cathode in Li/Ni0.5Mn0.3Co0.2O2 coin cells. Moreover, the capacity retention in Gr/Ni0.5Mn0.3Co0.2O coin cells increased from 62% to 77% after 200 cycles at 1C and approximately 4.4 V. The derived results suggest that the combination of the oxidation potential prediction with impedance study could be used as a powerful tool to properly and efficiently select CEI-forming additive candidates for improved battery performance.
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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 | 0.001 |
| 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".