MétaCan
Menu
Back to cohort
Record W2308043542 · doi:10.1149/ma2014-04/4/725

The Impact of Intentionally Added Water to the Electrolyte in Li-Ion Cells

2014· article· en· W2308043542 on OpenAlexaffabout
John C. Burns, Deijun Xiong, Nupur Nikkan Sinha, Gaurav Jain, Hui Ye, Collette M. VanElzen, Erik R. Scott, Ang Xiao, Bill Lamanna, J. R. Dahn

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteFaraday efficiencyLithium (medication)Materials scienceCyclingChemical engineeringGraphiteChemistryComposite materialElectrodeEngineering

Abstract

fetched live from OpenAlex

Electrolyte formulation is crucial to extending the lifetime and increasing the performance of lithium ion cells. Keeping moisture levels low (< 15 ppm [1]) has always been a standard in commercial lithium ion cell production which also increases the cost of electrolyte and cell manufacturing. The purpose of this study is to show the impact of small amounts of intentionally added water (100 – 2000 ppm) to the electrolyte impacts cell performance. Recently it has been shown that short term measurements of the coulombic efficiency and charge endpoint slippage rate coupled with storage and impedance measurements can give good indications of long term cycling performance [2-4]. In this study, wound LiCoO2/graphite, Li[Ni0.42Mn0.42Co0.16]O2/graphite and LiCoO2/Li4Ti5O12cells were made (similar to those in references 2-4) and filled with electrolytes containing additives such as vinylene carbonate [3, 5] and LiTFSI [4, 6] with and without intentionally added water to the electrolyte. Duplicate cells were made for both cycling on the High Precision Charger [7] and storage on an automated cycling/storage system [8] both built at Dalhousie University. Along with these short term measurements, impedance spectra were collected and long term cycling performance was evaluated. This presentation will discuss the impact of adding water with and without the presence of these additives in different cell chemistries along with other studies done on pouch cells containing intentionally added water in the electrolyte. One outcome of this work is that there appears to be no negative impact, and perhaps a positive impact in adding 1000 ppm water to LCO-graphite cells in the presence of the additives, VC and LiTFSI. This suggests that in the presence of these additives, water content specifications could perhaps be relaxed somewhat leading to an avenue for cost reduction. Long term cycling results on many of the cells are available to compare to the short-term, precision, measurements. Figure 1. A summary of data collected for LiCoO2/graphite cells, with and without both additives and water in the electrolyte, including charge transfer resistance (Rct), voltage drop during storage (V Drop), coulombic efficiency (shown as 1-CE) and charge endpoint slippage rate (Ch. Slippage). Figure 2. The charge endpoint capacity (top), discharge capacity (middle) and coulombic efficiency (bottom) versus cycle number for LiCoO2/Li4Ti5O12cells at both 30 (left) and 60°C (right) containing various amounts of added water in the electrolyte from 200 – 2000 ppm. References: [1] http://www.targray.com/documents/DMMP-Electrolyte-Solution.pdf, last accessed April 9, 2013. [2] J.C. Burns, G. Jain, A.J. Smith, K.W. Eberman, E. Scott, J.P. Gardner, and J.R. Dahn, J. Electrochem. Soc., 158, A255 (2011). [3] J.C. Burns, N.N. Sinha, D.J. Coyle, G. Jain, C.M. VanElzen, W.M. Lamanna, A. Xiao, E. Scott, J.P. Gardner, and J.R. Dahn, J. Electrochem. Soc., 159, A85 (2012). [4] J.C. Burns, N.N. Sinha, G. Jain, H. Ye, C.M. VanElzen, W.M. Lamanna, A. Xiao, E. Scott, J. Choi, and J.R. Dahn, J. Electrochem. Soc., 159, A1095 (2012). [5] B. Simon and J.-P. Boeuve, U.S. Patent No. 5626981 (6 May 1997). [6] M. Armand, M. Gauthier, and D. Muller, U.S. Pat. 5,021,308 (1991) [7] A.J. Smith, J.C. Burns, S. Trussler, and J.R. Dahn, J. Electrochem. Soc., 157, A196 (2010). [8] N.N. Sinha, A.J. Smith, J.C. Burns, G. Jain, K.W. Eberman, E. Scott, J.P. Gardner, and J.R. Dahn, J. Electrochem. Soc., 158 A1194 (2011).

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAdvanced Battery Technologies ResearchFrench-language works237,207