Invited Presentation: 4.7 V Li-ion cells: Nonsense or Possibility
Bibliographic record
Abstract
One way to improve the energy density of Li-ion cells is to use high voltage positive electrode materials like LiNi 0.5 Mn 1.5 O 4 [LNMO] or simply increase the upper cutoff potential when positive electrodes like Li[Ni x Mn x Co 1-2x ]O 2 [NMC] are used. This sounds simple, but there are numerous problems to overcome before high voltage Li-ion cells are a reality. Many literature reports of Li/LNMO and LTO/LNMO cells present very promising behavior. However, most of the reports show charge discharge cycling at high rates, where many cycles can be accumulated in a short period of time. In such experiments, authors are able to “beat the clock” on parasitic electrolyte oxidation reactions which occur in cells. We recently showed using storage experiments on LTO/LNMO cells that electrolyte oxidation, as evidenced by rapid self discharge, is a huge problem [1, 2] that is ignored in most literature reports. In this paper we discuss a number of the problems that occur in LTO/LNMO cells and high voltage graphite/NMC cells. These include: 1. Impedance increase at the positive electrode as the potential increases due to parasitic reactions. 2. Increases in parasitic heat as a function of cell voltage as measured using isothermal battery microcalorimetry 3. Increases in gas evolution as a function of cell potential, even when fluorinated solvents are used. 4. Decreases in coulombic efficiency as cell potentials increase. 5. Etc. The results above paint a bleak picture about the future of Li-ion cells that can operate at 4.7 V. Next, partial solutions to the problems above will be presented: a) Electrolyte additives that can stabilize positive electrode impedance increases during high potential cycling will be discussed. b) Electrolyte additives that can dramatically reduce parasitic heats evolved at high potentials will be discussed. c) Materials that can dramatically lower electrolyte oxidation reactions at the surface of the positive electrode and are suitable as “shells” in core-shell materials will be discussed. d) Electrolyte additives that can reduce gas evolution at high potential will be discussed. e) Etc. These encouraging results provide hope that real solutions can be found.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.032 |
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".