Molecular Dynamics Simulations of Polymer–Ionic Liquid (1-Ethyl-3-methylimidazolium Tetracyanoborate) Ternary Electrolyte for Sodium and Potassium Ion Batteries
Bibliographic record
Abstract
We have performed molecular dynamics (MD) simulations of 1-ethyl-3-methylimidazolium tetracyanoborate ([EMIM] + [B(CN) 4 ] − ) ionic liquid to investigate the impact of addition of Na + /K + [B(CN) 4 ] − salts and poly(ethylene oxide) (PEO 6 ) on transport properties. These ternary mixtures are promising electrolyte materials for Na + -ion and K + -ion batteries as alternatives to the traditional Li + -ion ones. In addition, local structure was assessed through radial distribution functions. Our main findings are the following: (1) when compared to systems with larger amounts of Na + and K +, it was observed that lower concentrations enhance the overall ionic conductivity and decrease viscosity; (2) Na + and K + cations prefer to be coordinated to the polymer chains rather than the [B(CN) 4 ] − anions; (3) transport properties are improved when PEO 6 oligomers are included in the electrolyte medium but these improvements continue only up to an optimum amount of PEO 6 . Beyond this amount, further addition of PEO 6 did not have any additional impact on transport properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".