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Record W2265934442 · doi:10.1149/ma2014-04/1/145

Invited Presentation: 4.7 V Li-ion cells: Nonsense or Possibility

2014· article· en· W2265934442 on OpenAlex
J. R. Dahn, Laura E. Downie, D. Y. Wang, C. P. Aiken, Lin Ma, Rémi Petibon, John C. Burns, Jian Xia, John Camardese, Sirong Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteFaraday efficiencyIonElectrodeBattery (electricity)Materials scienceChemical engineeringChemistryThermodynamicsPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.282
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it