Mechanistic insights into lithium ion battery electrolyte degradation – a quantitative NMR study

Wiemers‐Meyer, Simon; Winter, Martin; Nowak, Sascha

doi:10.1039/c6cp05276b

Cited by 128 publications

(146 citation statements)

References 34 publications

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“…The spectrum coincided with the BMImBF4 19 F-NMR spectrum (Figure 9b). Moreover, the 19 F-NMR spectrum corresponding to sucrose crystals precipitated from BMImBF4 3 mL, illustrated in Figure 9a was affirmed to be similar with 19 F-NMR spectrum of BF4 − anion reported by Wiemers-Meyer et al [39]. Based on the result depicted in Figure 9, we could presume that not BMIm + , but only BF4 − , were attached on the surfaces of sucrose crystals.…”

Section: Intramolecular Intermolecular Periodic Bonds Chains (Pbcs)supporting

confidence: 60%

Effect of Ionic Liquids on the Separation of Sucrose Crystals from a Natural Product Using Crystallization Techniques

Kiyonga

Yoon

et al. 2017

Crystals

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Abstract:The present work aims to investigate the applicability of ionic liquids (ILs) for natural ingredient crystallization. First, the medicinal plant, namely Angelica gigas Nakai, was extracted using methanol (MeOH) as a solvent. Afterwards, ILs 1-butyl-3-methylimidazolium tetrafluoroborate (BMImBF 4 ), 1-butyl-3-methylimidazolium hexafluorophosphate (BMImPF 6 ), 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (BMImTFSI), 1-allyl-3-ethylim idazolium tetrafluoroborate (AEImBF 4 ), and 1,3-diallyl imidazolium tetrafluoroborate (AAImBF 4 ), in three ratios of 1:1, 1:2, and 1:3 (extraction solution/ILs (v/v)) were used as an anti-solvent to induce crystallization. Crystals were obtained within 8 h and were then identified to be pure crystals of sucrose through nuclear magnetic resonance ( 1 H-NMR) analysis. Moreover, the single-crystal X-ray diffraction (SXD) analysis revealed all recovered crystals have an identical crystal structure and the morphology was monitored using a video microscope. With the application of BMImBF 4 and BMImPF 6 , transformation of sucrose crystal morphology from an elongated hexagon shape to an elongated rectangular shape was observed with respect to the respective concentration increase. Here, all crystals precipitated from BMImBF 4 and BMImPF 6 were found to possess identical PXRD patterns. However, when BMImTFSI was employed, small rectangular crystals attached to the larger rectangular-shaped crystals due to secondary nucleation and shapeless amorphous forms were observed according to the alteration in the solution to ILs ratio. Accordingly, the ability of ILs as a relevant anti-solvent for the selective crystallization of a single compound from a natural product was assessed through the study. Furthermore, the applicability of ILs as crystal engineering solvents are expected to modify both the solid state and the crystal morphology of natural compounds, which can influence drug manufacturability, dissolution rate, and bioavailability.

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Section: Intramolecular Intermolecular Periodic Bonds Chains (Pbcs)supporting

confidence: 60%

Effect of Ionic Liquids on the Separation of Sucrose Crystals from a Natural Product Using Crystallization Techniques

Kiyonga

Yoon

et al. 2017

Crystals

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“…5 Aged lithium-ion cells with many cycles or after testing in extreme conditions show dramatic electrolyte changes. [6][7][8] Recorded electrolyte changes include oxidation at the positive electrode, 7,9 reduction at the negative electrode, 7,9 dimerization of ethylene carbonate (EC) and linear carbonates, 8 transesterification of the linear carbonates, 10 LiPF 6 break down and subsequent reactions, 7,[11][12][13] and consumption of active Li and electrolyte in solid electrolyte interphase (SEI) formation. 7,14 These reactions may be paired with electrode transformations later in cell life including changes to the positive electrode surface structure from layered to spinel 1,15 and transition metal dissolution from the positive electrode followed by deposition on the negative electrode.…”

mentioning

confidence: 99%

Quantifying Changes to the Electrolyte and Negative Electrode in Aged NMC532/Graphite Lithium-Ion Cells

Thompson

Stone

Eldesoky

et al. 2018

J. Electrochem. Soc.

103

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A set of LiNi 0.5 Mn 0.3 Co 0.2 O 2 /graphite lithium-ion cells underwent 750 charge-discharge cycles during about 8 months at 55 • C to upper cutoff potentials of 4.0, 4.1, 4.2, 4.3, and 4.4 V. The electrolyte in these cells was extracted using a centrifuge method and studied using gas chromatography/mass spectrometry to determine the changes to the solvents and by inductively coupled plasma-mass spectrometry to determine the changes to the salt content in the electrolyte. The negative electrodes from the cells were harvested and studied by micro-X-ray fluorescence to quantify the amount of transition metals which migrated from the positive electrode to the negative electrode during the testing. Emphasis is given to a detailed description of the quantitative methods used in the hope that others will adopt them in similar studies of different types of aged lithium-ion cells. The cells studied here initially had 1.1 molal LiPF 6 ethylene carbonate (EC): ethyl methyl carbonate (EMC) (3:7 by weight) electrolyte. The aged cells showed increasing amounts of dimethyl carbonate and diethyl carbonate created by transesterification as the upper cutoff potential increased. Only extremely small amounts of Mn, less than 0.1% of the total Mn in the positive electrode, were found on the negative electrode after this aggressive testing. Lithium-ion batteries are currently used in a wide range of applications: cell phones, power tools, vehicles and even grid energy storage.

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“…[7][8][9][10][11][12][13][14][15][16][17][18][19][20] These instruments are costly, and these methods require the preparation and measurement of many calibration solutions. Often the columns or detectors used in chromatography experiments cannot be exposed to the high temperature decomposition products of LiPF 6 , so these experiments often focus on the organic portions of the electrolyte, after the water-soluble portions of the electrolyte have been removed.…”

mentioning

confidence: 99%

A New Method for Determining the Concentration of Electrolyte Components in Lithium-Ion Cells, Using Fourier Transform Infrared Spectroscopy and Machine Learning

Ellis

Buteau

Hames

et al. 2018

J. Electrochem. Soc.

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A new method is introduced for determining unknown concentrations of major components in typical lithium-ion battery electrolytes. The method is quick, cheap, and accurate. Machine learning techniques are used to match features of the Fourier transform infrared (FTIR) spectrum of an unknown electrolyte to the same features of a database of FTIR spectra with known compositions. With this method, LiPF 6 concentrations can be determined with similar accuracy and precision as an inductively coupled plasma optical emission spectrometry (ICP-OES) method. The ratios of organic carbonate solvent species can be determined with more rapidity than gas chromatography (GC). This FTIR method is faster and less expensive than GC and ICP-OES, and has the added benefit of being able to determine LiPF 6 concentration and solvent fractions simultaneously. Application of this tool can facilitate electrolyte analysis of aged lithium-ion cells, and will help elucidate mechanisms for cell degradation. A dominant cause of lithium-ion cell failure, especially in high voltage cells, is degradation of the electrolyte, particularly at the surface of the charged electrodes.1 Most studies on the topic of cell degradation and electrolyte decomposition have focused on the formation of films of electrolyte decomposition products which build up on the surfaces of the electrodes.2-6 These films contain chemical moieties derived from both the electrolyte solvents and the electrolyte salt, LiPF 6 . Although mechanisms for the consumption of solvents and LiPF 6 in lithium-ion cells have been determined, there have been few systematic studies which quantify the changes in the bulk electrolyte as a function of cell aging.Quantitative analyses of electrolyte solutions typically employ nuclear magnetic resonance (NMR) spectroscopy, gas chromatography (GC), high performance liquid chromatography (HPLC), and inductively coupled plasma optical emission spectrometry (ICP-OES). [7][8][9][10][11][12][13][14][15][16][17][18][19][20] These instruments are costly, and these methods require the preparation and measurement of many calibration solutions. Often the columns or detectors used in chromatography experiments cannot be exposed to the high temperature decomposition products of LiPF 6 , so these experiments often focus on the organic portions of the electrolyte, after the water-soluble portions of the electrolyte have been removed. 20A method of determining the concentration of LiPF 6 and weight fractions of solvents in unknown electrolyte solutions, using attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy is presented here. Until now, FTIR has only been used for qualitative electrolyte analysis, and for the determination of solvation structures. Quantitative analysis was achieved using a machine learning (ML) algorithm to match features in the FTIR spectra of unknown electrolyte solutions to those interpolated from a spectral database of known electrolyte solutions. This method is very fast. Once the spectral database and mac...

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Mechanistic insights into lithium ion battery electrolyte degradation – a quantitative NMR study

Abstract: Water as the main driving force of LiPF6 degradation increases the reaction rate and determines the composition of degradation products.

Cited by 128 publications

References 34 publications

Effect of Ionic Liquids on the Separation of Sucrose Crystals from a Natural Product Using Crystallization Techniques

Effect of Ionic Liquids on the Separation of Sucrose Crystals from a Natural Product Using Crystallization Techniques

Quantifying Changes to the Electrolyte and Negative Electrode in Aged NMC532/Graphite Lithium-Ion Cells

A New Method for Determining the Concentration of Electrolyte Components in Lithium-Ion Cells, Using Fourier Transform Infrared Spectroscopy and Machine Learning

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