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Record W2519750949 · doi:10.1149/ma2016-02/3/236

Consumption of Intentionally Added Gas in Lithium-Ion Cells

2016· article· en· W2519750949 on OpenAlexaff
L. D. Ellis, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteLithium (medication)ElectrodeIonGraphiteChemistryChemical engineeringInorganic chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Introduction Lithium-ion cells produce a considerable amount of gas during their first cycle, as electrolyte solvents react with the surface of the charging electrodes to form passivating films. In cells without electrolyte additives, this gas is largely a mixture of CO2 and C2H4. [1] If lithium-ion cells are packaged in flexible bags (commonly termed “pouch” cells), these gases must be removed by the manufacturer in a degassing step, to prevent deformation of the cell and ensure stack pressue on the electrodes. If the degassing step is omitted, as in the case of cells packaged in metal cans, some amount of these gases are consumed by the cell over time. The reaction that consumes gas, and the effects of gas consumption on cell performance are not fully understood. It has been suggested that consumption of CO2 gas in lithium-ion cells causes reversible self-discharge through a “chemical dialogue” between negative and positive electrodes: CO2 is reduced at the lithiated negative electrode to form lithium oxalate, which diffuses to the positive electrode and is oxidized to reform CO2. [2 ] To reveal the presence of a chemical dialogue, and determine the fate CO2 and C2H4 inside cells, the reaction of these gases with individual charged electrodes will be discussed. Experimental Machine-made lithium-ion pouch cells, containing Li[Ni0.4Mn0.4Co0.2]O2 (NMC442) positive electrodes and graphite negative electrodes, were obtained sealed, without electrolyte from LiFun Technologies (Zuzhou City, China). The cells were filled with a slight excess of electrolyte, composed of 1 M LiPF6, dissolved in a 3:7 wt blend of ethylene carbonate and ethyl methyl carbonate. The cells were charged to 4.5 V, at a rate of C/20 at 40°C. Several of of the cells were transferred to an Ar-filled glove box, where the charged electrodes were removed from the cell and sealed individually in aluminized polymer bags, made from the same material as the casing of the parent pouch cell. Each of these bags was equipped with a rubber septum, through which gases were injected. After injection, the bags were vacuum sealed below the septum and the septum was cut off. The inflated bags were stored at 40°C, in a similar manner to charged full cells which were not disassembled, and which were left at open-circuit voltage. The changes in volume of the charged cells and the inflated bags were measured at regular intervals, using Archimedes’ principle. Results and Discussion Figure 1 shows the voltage and volume change of a cell during its first charge cycle (formation cycle), followed by 100 hours of open-circuit voltage storage at 40°C, during which time over 0.5 mL of gas was consumed. Figure 2 shows the volume change versus time for pouch bags inflated with CO2, containing lithiated graphite negative electrodes or charged NMC442 positive electrodes, stored at 40°C. The volume of the bags containing lithiated graphite negative electrodes decreased steadily with time, indicating that CO2 was consumed by the lithiated graphite negative electrode. The volume of the bags containing charged NMC442 positive electrodes remained constant after equilibration at 40°C, indicating that CO2 was not consumed by the positive electrode. The amount of gas consumed by the full cell over 100 hours, shown by Figure 1, is approximately equal to the amount of CO2consumed by the lithated graphite electrode, shown by Figure 2. The consumption of gas in lithium-ion cells and the effect of gas consumption on the surface chemistry of electrodes will be discussed further. References: 1. J. Self, C. P. Aiken, R. Petibon, and J. R. Dahn, J. Electrochem. Soc., 162, A796–A802 (2015). 2. S. E. Sloop, J. B. Kerr, and K. Kinoshita, J. Power Sources, 119–121, 330–337 (2003). Figure 1

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations2
Published2016
Admission routes1
Has abstractyes

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