Exploring Binder–Ionic Liquid Electrolyte Systems in Silicon Oxycarbide Negative Electrodes for Lithium-Ion Batteries
Bibliographic record
Abstract
Enhancing the safety of lithium-ion batteries (LIBs) by replacing flammable electrolytes is a key challenge. Ionic liquid (IL)-based electrolytes are considered an interesting alternative due to their thermal and chemical stability, high voltage stability window, and tunable properties. This study investigates the electrochemical behavior of two newly synthesized ILs, comparing them to conventional alkyl carbonate-based electrolytes. Nitrogen-doped carbon silicon oxycarbide (NC-SiOC), used as the active material in negative electrodes, was combined with two polymeric binders: poly(acrylic acid) (PAA) and poly(acrylonitrile) (PAN). NC-SiOC/PAN electrodes exhibited a significantly higher initial charge capacity—approximately 25–30% greater than their PAA-based counterparts in the first cycle at 0.1 A g−1 (850–990 mAh g−1 vs. 600–700 mAh g−1), and demonstrated an improved initial Coulombic efficiency (67% vs. 62%). Long-term cycling stability over 1000 cycles at 1.6 A g−1 retained 75–80% of the initial 0.1 A g−1 capacity. This outstanding performance is attributed to the synergistic effects of nitrogen-rich carbonaceous phases within the NC-SiOC material and the cyclized-PAN binder, which facilitate structural stability by accommodating volumetric changes and enhancing solid electrolyte interphase (SEI) stability. Notably, despite the lower ionic transport properties of the IL electrolytes, their incorporation did not compromise performance, supporting their feasibility as safer electrolyte alternatives. These findings offer one of the most promising electrochemical performances reported for SiOC materials to date.
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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".