Performance of a Novel In-Situ Converted Additive for High Voltage Li-ion Pouch Cells
Bibliographic record
Abstract
In search for new classes of additives for high voltage NMC/graphite lithium-ion cells, the precursor additive bis(trimethylsilyl) malonate (bTMSM) is shown to be activated via a spontaneous reaction with LiPF6 and LiBF4 salts in carbonate-based electrolyte to form lithium tetrafluoro(malonato)phosphate (LiTFMP), and lithium difluoro(malonato)borate (LiDFMB), respectively. The reaction schemes and rates were studied via NMR spectroscopy and GCMS. The effects of LiTFMP and LiDFMB on high voltage electrochemical performance were then examined up to 4.5 V in Li[Ni0.4Mn0.4Co0.16]O2 (NMC442)/graphite and Li[Ni0.6Mn0.4Co0.0]O2 (NMC/640)/graphite pouch cells using aggressive voltage-hold cycling, long-term charge/discharge cycling, storage experiments, electrochemical impedance spectroscopy, and gas evolution measurements. While in situ converted additives suffer from gassing issues due to the presence of trimethylfluorosilane (TMSF) gas, a side product of the in situ reaction of bTMSM with LiPF6, the cycling and storage capability for the activated additives under study shows competitive performance and controlled impedance when compared to other well-known high voltage additives. Micro X-ray fluorescence spectroscopy (μXRF) confirmed that LiTFMP successfully minimizes the rate of transition metal deposition on the surface of graphite apparently by forming a protective agent at the cathode surface, hence allowing for improved cycling performance at high voltages.
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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 | 0.000 |
| Open science | 0.001 | 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".