A Joint DFT and Experimental Study of an Imidazolidinone Additive in Lithium-Ion Cells
Bibliographic record
Abstract
Electrolyte additives are a practical route to improving the lifetime and performance of lithium-ion cells. It is not well understood what makes a good additive; thus, the discovery of new additives poses a significant challenge. Computational methods have the potential to streamline the search for new additives, but it is important to compare predicted additive behavior with experimentally measured results. A new electrolyte additive, 1,3-dimethyl-2-imidazolidinone (DMI), has been evaluated in LiNi 1-x-y Mn x Co y O 2 (NMC)/graphite pouch cells as a single additive and with the co-additive vinylene carbonate (VC). This work compares the density functional theory (DFT)-predicted behavior of DMI with experimental results, including differential capacity analysis (dQ/dV), electrochemical impedance spectroscopy (EIS), high-temperature storage, gas chromatography-mass spectrometry (GC-MS) and long-term cycling tests. The DFT-calculated reduction potential of DMI is −0.63 V vs Li/Li + , consistent with the experimental observation that it reduces at a lower potential than ethylene carbonate (EC), ∼0.80 V vs Li/Li + . Although DMI turns out not to be a competitively useful additive, the good match between many aspects of the experimental results and theoretical predictions is a good indication that it is possible to understand aspects of the behavior of additives. This can guide future researchers.
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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) | 0.001 | 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 | 0.001 | 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".