Probing Solid-Electrolyte Interphase (SEI) Growth and Ion

to be critical to determining the modality of metal deposits on the electrode (e.g., ... (9) However, safety issues associated with Li-dendrite gr...
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Probing Solid-Electrolyte Interphase (SEI) Growth and Ion Permeability at Undriven Electrolyte−Metal Interfaces Using 7Li NMR Andrew J. Ilott and Alexej Jerschow* Department of Chemistry, New York University, New York, New York 10003, United States

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S Supporting Information *

ABSTRACT: We examine here the exchange of Li ions between electrolyte and metallic lithium with 7Li NMR spectroscopy. The measurements quantify the liquid−solid exchange processes, as well as the growth of a solid-electrolyte interphase (SEI) layer. A numerical model including diffusion in the solid phase through atom hopping, radiofrequency penetration considerations through the skin effect, as well as surface exchange explains the experimental trends. Incorporation of the growth of an SEI layer explains the “missing” Li quantities, and, as the SEI layer grows, a decreased ion permeability in dependence on the layer thickness is modeled to explain the longterm trends. These measurements provide indirect probes for SEI growth and permeabilities and also provide a means for quantifying Li diffusion in the metal.

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INTRODUCTION When lithium metal is brought into contact with an electrolyte solution, spontaneous reactions lead to the formation of a layer made from inorganic and organic reaction products.1 The primary function of the layer, termed solid-electrolyte interphase (SEI), is passivation and protection from metal dissolution.2,3 The SEI is the subject of intense research, because it is often central to the successful operation of an electrochemical cell, particularly Li-ion cells.4,5 In addition to protecting the anode, the layer has to allow for efficient ion propagation.6 Furthermore, with Li-metal anodes, in particular, the nature of the SEI layer is thought to be critical to determining the modality of metal deposits on the electrode (e.g., smooth or irregular deposits and dendrite formation).7 At the same time, there is a lot of uncertainty about the initiation, composition, and growth of this layer, because (i) it forms rapidly upon electrode contact with the electrolyte, (ii) is generally very thin (tens of nanometers), and (iii) is affected significantly by the presence of impurities, electrolyte composition, and the electrode surface morphology.2,5 Its structure is often found to be composed of both organic and inorganic layers with various levels of porosity and ion conductivity.6,8 Li metal is a promising anode material, especially for secondary batteries, and is central to emerging cell technology, such as Li−air cells.9 However, safety issues associated with Lidendrite growth currently prevent the use of lithium metal as an anode in secondary batteries. Efforts are being taken to engineer SEI layers such that dendrite growth can be minimized.10 The layer is inherently difficult to study due to its small size, but also because, in the process of sample preparation (for ex situ studies, for example), the layer may be altered significantly. © 2018 American Chemical Society

Recently, NMR spectroscopy and MRI techniques have been developed to study electrochemical processes in situ and derive ion mobilities, monitor electrode reactions, and observe microstructure formation.11−20 The SEI has also been studied with solid-state NMR in destructive analyses by scraping off the layer from an electrode or grinding up the electrode and performing multinuclear magic-angle spinning experiments.21−24 To the best of our knowledge, this technique, however, has not been used for metallic lithium electrodes. Static spectra of the SEI are relatively featureless, due to the inability to resolve broad spectral features.25 Further, challenges in this approach arise from the low sensitivity of the signals obtained from the small amounts of SEI materials. Hyperpolarization techniques could be used to address this problem.24 Here, we investigate the Li-ion/metal exchange and SEI growth when Li metal is brought into contact with an electrolyte solution without applying any additional potential. The experiments are performed by means of isotopic tracking from an initial point where a Li-metal electrode is enriched with 6 Li. 7Li NMR experiments performed on a 6Li-enriched lithium-metal strip (6Li/7Li = 0.95:0.05) immersed (Figure 1) in a natural abundance LiPF6/ethylene carbonate (EC)/ dimethyl carbonate (DMC) battery electrolyte (6Li/7Li = 0.074:0.926) show an increase in the 7Li-metal and 6Lielectrolyte signals over time, whereas the 6Li-metal and 7Lielectrolyte signals decrease. These results, discussed in more detail below, suggest that there is significant lithium exchange between the electrolyte and the lithium-metal electrode even in Received: February 26, 2018 Revised: May 18, 2018 Published: May 21, 2018 12598

DOI: 10.1021/acs.jpcc.8b01958 J. Phys. Chem. C 2018, 122, 12598−12604

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The Journal of Physical Chemistry C

Figure 1. Schematics of the experimental setup: (a) 6Li-enriched lithium metal immersed in 7Li (natural abundance) electrolyte inside an NMR tube. (b) Illustration of the exchanging components of the system between the metal (gray, right) and the electrolyte (green, left), including Li selfdiffusion inside the metal, SEI growth, and interphase exchange.

experiments were performed on the 6Li- and 7Li-metal signals on this “dry” sample. The sample was then put back into the glove box and a measured weight of standard battery electrolyte added (1 M LiPF6 in 1:1 ethylene carbonate and dimethylene carbonate solvent, with natural lithium isotopic abundance, 6 Li/7Li = 0.074:0.926). To allow for quantification, the amount of electrolyte added was set so that it occupied a region entirely within the excitation profile of the coil (ca. 15 mm). The tube was again sealed and returned quickly for the experiments, with the coil changed between 6Li and 7Li measurements. The experiments were performed on a 14 × 5 × 0.45 mm 6Li-metal strip weighing 14.8 mg (the thickness is calculated from the other dimensions and measured mass) that was placed in 0.6989 g of LiPF6 electrolyte (ne7Li(0)/nm 7Li(0) = 4.07, where 7 ne7Li(0) and nm 7Li(0) are the total number of moles of Li in the electrolyte and metal at t = 0, respectively). The spectra resulting from the NMR measurements were phased and integrated, so that the integrated areas could be compared to the simulated intensities as expressed above. The full set of NMR spectra is shown in Figure S1. In the metal series, the spectra were automatically phased to minimize the integral of the imaginary component, after which the integral of the real part of the spectrum was compared directly against the calculated value of S0 from 9 to 13. A lineshape fit of the imaginary component of the spectrum could be used to extract additional information from the phase of the signal (see 9−11), but this approach is complicated by the presence of multiple, overlapping lineshapes in the metal spectra due to the slightly different susceptibility shifts in different parts of the metal strip and was hence not used. Numerical Simulation. The model illustrated in Figure 1b is used for characterizing the measured data. The concentration of 7Li in the metal electrode is governed by the diffusion equation

this undriven situation (no potential applied) over long periods of time. A model of the process is established by including diffusion in the solid phase (atom hopping), a liquid−solid exchange rate constant, a Li-metal sink into the SEI layer, and a growing SEI layer leading to decreased permeability. The Li signals obtained from the metal are further analyzed considering the properties of radiofrequency (rf) penetration into the conductor. Although both 6Li and 7Li were measured, the bulk of this work focuses on 7Li due to its higher sensitivity and consequently more reliable results.

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EXPERIMENTAL SECTION NMR Experiments and Sample Preparation. NMR experiments were performed on a Bruker Ultrashield 9.4 T Avance I spectrometer operating at 155.51 MHz for 7Li and 58.90 MHz for 6Li. A Bruker Micro2.5 imaging probe was used, with Bruker WB40 25 mm inside diameter 1H/7Li and 1H/6Li coils. The samples were aligned in the coil such that the major surface of the lithium metal was parallel to the direction of the rf field.26 For each nucleus, two experiments were performed: the one with the transmitter on resonance with the electrolyte signal at 0 ppm and the other at the metal signal, at approximately 261 ppm, which is the Knight shift of Li metal.27 The probe was tuned to the metal signal for consistency. Single-pulse experiments were performed with a π/2 excitation pulse of τ90 = 35 μs duration for 6Li and 39 μs for 7Li, which were calibrated on the on-resonance electrolyte signal for each experiment series. The other acquisition parameters were set to yield quantitative signals for each species: 6Li metal (recycle delay, rd = 3 s, spectral width, sw = 16 kHz, and number of scans, ns = 1200), 6Li electrolyte (rd = 30 s, sw = 5 kHz, and ns = 40), 7Li metal (rd = 0.8 s, sw = 50 kHz, and ns = 1500), and 7Li electrolyte (rd = 15 s, sw = 4 kHz, and ns = 1500). The sample preparation was performed inside a glove box under Ar atmosphere. To make the 6Li-metal sample, a small strip was cut from a 6Li-metal chunk (0.95:0.05 = 6Li/7Li, Sigma-Aldrich) using a razor blade, then pressed inside a polyester bag using a hammer. A rectangular strip was cut from the pressed metal using a razor blade, measured and weighed. The experiments were set up according to the schematic in Figure 1, with the metal strip placed at the bottom of a 10 mm NMR tube. The NMR tubes of 3 mm were placed around the metal to ensure stable positioning throughout the experiments. The NMR tube was sealed with a cap and parafilm. Initial NMR

∂ 2U (x , t ) ∂U (x , t ) =D ∂t ∂x 2

(1)

where U(x, t) = f m 7Li(x, t) is a discretized one-dimensional representation of the fraction of 7Li at each depth in the metal, where x ∈ [0, L] represents the distance from the surface of the metal, such that x = 0 corresponds to the metal surface and x = L is some maximum depth from the surface of the metal. For convenience, L is set to half the overall thickness of the lithium metal. In this way, the model geometry can be readily scaled up to the full volume of the electrode through multiplication by the surface area of the main face of the metal, A ≈ 2ab, ignoring 12599

DOI: 10.1021/acs.jpcc.8b01958 J. Phys. Chem. C 2018, 122, 12598−12604

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The Journal of Physical Chemistry C

As [Li+] is depleted, the exchange rate between the lithium in the electrolyte and the metal decreases (4), and therefore these effects are included in the numerical model. To calculate the boundary conditions at each time step, the fraction of 7Li in the electrolyte, f e7Li(t), is updated by tracking the number of moles of 7Li in the metal and the SEI layer and the concentration of the electrolyte at each time step. Calculation of the NMR Signal. The 6Li and 7Li NMR signals of the electrolyte are proportional to the number of moles of each isotope that are present, and these quantities are already calculated during the process of solving the diffusion equation (acknowledging that ne6Li(t) = ne(0) − ne7Li(t) − SEI 6 nSEI 6Li (t), where n6Li (t) is the total number of moles of Li used in forming the SEI). For the metal, the skin effect28 and the principle of reciprocity29 must be taken into account to calculate the NMR signal from the lithium profiles calculated at each time point. The skin effect changes both the amplitude and the phase of the rf field as it enters the metal, according to

the minor electrode faces that make up a small fraction of the overall surface area and are also poorly sampled in NMR due to rf field effects.26 Appropriate boundary conditions at x = 0 allow for exchange between the surface sites in the metal and the electrolyte. The time derivative in 1 is integrated numerically using the Python/Scipy interface to the lsoda ODE integrator from the FORTRAN library, odepack. The spatial derivative for the diffusion equation in the metal is estimated using central differences. Neumann boundary conditions are used at each end of the box (x = 0, L). At x = L, the boundary condition, Ux(L, t) = 0, is used to prevent “leakage” of 7Li from the cell. At x = 0, the metal region is in contact with the electrolyte, and exchange between the metal and the electrolyte occurs Li+(electrolyte) + e− ⇌ Li(metal)

(2)

To preserve electroneutrality, both the forward and reverse reactions occur at the same rate, which can be written in terms of the concentration of Li+ in the electrolyte and the surface area of the lithium-metal strip, SA, as

rex(t ) = kex[Li+](t )SA

H1 = H10 e−a e ia

where H10 is the rf field amplitude at the surface and a = x/δ, where x is the depth of the metal, following the same notation as used to solve the PDE, and δ is the frequency-dependent skin depth constant. At the frequencies used in the experiments 7 6Li Li (ω7Li = 0 /2π = 155.51 MHz and ω0 /2π = 58.90 MHz) δ

(3)

where kex is the rate coefficient in units of m−2 s−1. The lithium concentration is assumed to be uniform throughout the volume of the electrolyte, although it does vary in time as the reaction proceeds. Any imbalance in the exchange is modeled by the Li sink into the SEI as described below. We note that a temperature dependence of the rate coefficients is expected but not explicitly stated. All experiments were performed at room temperature (293 K). Although the overall Li exchange is considered to be in a steady state, the relative effects for the 6Li and 7Li isotopes will be different, and we can separately define the exchange of 7Li between the electrolyte and the metal surface via the exchange rate rin, 7Li(t ) =

6

10.39 μm and δ Li = 16.89 μm, respectively. The intensity of the NMR signal originating from a conductor was derived by Mehring et al. for a uniform metal as30,31 S = S0 e iϕ

A=

∫0

B=

∫0

fm 7Li(x

L

L

da e−a sin(β0 e−a)sin 2a

(10)

da e−a sin(β0 e−a)cos 2a

(11)

where L is the half of the width of the metal sample and β0 = γH10τ is the flip angle for the nucleus with a gyromagnetic ratio γ at the surface when an rf pulse of duration τ is applied. In our system, the metal is not uniform, because we observe 7 Li and 6Li NMR signals in separate experiments and their distribution is depth dependent and proportional to f m 7Li(x, t) m and fm 6Li(x, t) = 1 − f 7Li(x, t). 10 and 11 can be updated to include these quantities, according to Am =

∫0

Bm =

∫0

(5)

which is also expected to depend on the surface area of the metal. For simplicity, we assume that the surface area does not change and hence the first-order rate equation can be integrated to give the total Li+ concentration in the electrolyte at each time step [Li+](t ) = [Li+]0 exp(−k SEISAt )

(9)

with

where is the fraction of Li in the electrolyte and = 0, t) is the fraction of 7Li at the surface of the metal, i.e., U(x = 0, t). From 3, the exchange rate, rex(t), is time dependent when either [Li+] or SA varies in time. We neglect here kinetic differences in the isotope masses between 6Li and 7Li, which are very difficult to predict, especially for the interphase behavior, but can be assumed to be sufficiently small (electrochemical separation effects have been reported to be on the order of 2− 6%). We now model the formation of the SEI layer under the influence of the large potential difference between the metal and the electrolyte. This process is modeled by a gradual loss of Li+ from the electrolyte d rSEI(t ) = − [Li+] = k SEI[Li+](t )SA dt

(A2 + B2 ) , ϕ = −tan−1(B /A)

S0 =

∂U (x = 0, t ) = rex(t )[f 7eLi (t ) − f 7mLi (x = 0, t )] ∂x 7

(8)

where the signal intensity and the phase are given by

(4)

f e7Li(t)

(7)

L

L

da f m (a) e−a sin(β0 e−a)sin 2a

(12)

da f m (a) e−a sin(β0 e−a)sin 2a

(13)

where f m(a) refers to the fraction of the relevant Li isotope at position a. With these expressions, one can quantify the relative amounts of each lithium isotope in the surface layers of the metal. The skin effect and the e−a term in these expressions

(6) 12600

DOI: 10.1021/acs.jpcc.8b01958 J. Phys. Chem. C 2018, 122, 12598−12604

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Figure 2. NMR results (points) and simulation results (lines) with fixed molar ratio and diffusion coefficient, kSEI = 0, and kex taking the values shown (in units of m−2 h−1) for the signal intensities vs immersion time. NMR intensity is plotted with respect to the intensity at t = 0.

for to understand the NMR results. Some variation of the precise value of the diffusion coefficient of up to 20% may also arise from the 6Li/7Li composition.33 This variation is not expected to change the qualitative behavior of the models, but may translate into an uncertainty in the rate constants. To assess the sensitivity of the models to the precise diffusion rate, simulations were also performed at two and four times the standard diffusion rate. The results, shown in Figures S2 and S3 parts (g−i), confirm that the model is relatively insensitive to the precise diffusion coefficient used. The number of moles of each Li species in the metal and electrolyte and the thickness of the electrode are all known from measured quantities, leaving only kex (the rate coefficient for the exchange between lithium in the electrolyte and the metal) and kSEI (the rate coefficient for the formation of SEI from Li+ in the electrolyte) as parameters in the model. Simulations Including Exchange and Diffusion Only (kSEI = 0). These simulations included only the exchange between the phases and the diffusion within the metal (the diffusion within the liquid is assumed to be sufficiently fast and not limiting). The simulation results for kex = 2.5, 5.0, 7.5, and 10.0 m−2 h−1 are shown together with the experimental 7Li NMR data in Figure 2. Similar data for 6Li are shown in Figure S4. The simulations show that the 6Li-electrolyte and 7Li-metal signals are most sensitive to the exchange process, with ranges of signal increase of approximately 1.3−2.2 and 1.4−2.6, respectively. This sensitivity is due to these isotopes being the minor fractions in the respective phases. Although the 6Lielectrolyte signal is sensitive to the occurring changes, its utility in resolving different models is still limited due to the low signal-to-noise ratio as mentioned above. Likewise, the 6Limetal signal is noisy, and the intensity decreases only by a factor of 0.93−0.98. Although the range of changes in the 7Lielectrolyte signal is also small (0.90−0.97), the better signal-tonoise ratio for this signal makes it a more viable indicator for validating the model. Considering only the 7Li NMR results, there is not a single model that can describe both sets of results: kex = 5.0 m−2 h−1 gives a good fit of the 7Li-electrolyte data but overestimates the increase in the 7Li-metal signal and kex = 2.0 m−2 h−1 fits the metal data reasonably well but underestimates the changes in

result in the NMR signal intensity being extremely sensitive not only to the levels of each isotope but also to their spatial distribution and therefore their rate of diffusion in the bulk metal phase.

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RESULTS AND DISCUSSION Figure 2 shows the result of the spectral integration of the experiments, tracking the 7Li intensities in the electrolyte and the metal. The initial point is given by the metal strip composition of 0.95:0.05 (6Li/7Li) and the natural abundance LiPF6/EC/DMC battery electrolyte with a composition of 0.074:0.926 (6Li/7Li), which can be used to normalize the signals in the respective phases. There are clear trends in the experimental data: the 7Li-metal signals increase in time, whereas the 7Li-electrolyte signals decrease. The 6Li results show roughly complementary results, but due to the difficulties in measuring this nucleus (low sensitivity and long T1 times) only limited data were acquired with 6Li (see Supporting Information). The NMR observations validate the overall understanding that there is a vigorous two-way exchange occurring between the two phases, despite a strong thermodynamic favoring of dissolution of the metal. Another aspect of metals that must be considered is the selfdiffusion of atoms in the metallic phase. Measurements of diffusion coefficients in metals have been taken via radioactive tracers and by NMR relaxation measurements. A universal estimate of the diffusion coefficient in lithium metal has been described, which accounts for both NMR and radioactive tracer results,32 and pulsed-field gradient (PFG) NMR measurements have been performed at 350 K and above.33 At room temperature, the self-diffusion coefficient of the Li atoms in the metal34 was determined to be 7.66 × 10−11 cm2 s−1 from relaxation measurements and a hopping model. Given potential uncertainties in PFG NMR measurements with the very short T2 relaxation times of 7Li encountered in the metal33 and the very slow diffusion coefficients, we chose the latter value as a basis for our analysis. Converting this quantity into time and length scales relevant to the NMR experiments gives a selfdiffusion coefficient of 27.6 μm2 h−1. This value highlights that the self-diffusion of the atoms in the metal must be accounted 12601

DOI: 10.1021/acs.jpcc.8b01958 J. Phys. Chem. C 2018, 122, 12598−12604

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The Journal of Physical Chemistry C

Figure 3. NMR results (points) and simulation results (lines) with fixed molar ratio and exchange coefficient, kex = 2.0, with kSEI taking the values shown (in units of m−2 h−1) for the signal intensities vs immersion time. NMR intensity is plotted with respect to the intensity at t = 0. Labels for the lines in (b) are omitted for clarity, but the overlapping lines correspond to kSEI = 0, 1.5, and 3.0 as in (a).

and this is not reflected by any of the models. Second, the growth of the SEI layer can be expected to impact the electrolyte−metal exchange rate. For instance, from kSEI and [Li+]0, the number of moles of Li in the SEI can be calculated from

the electrolyte data (as seen in Figure 2). These discrepancies show that the exchange−diffusion model is not able to fully explain the experimental results. We note that an alternative explanation for the reducing 7Lielectrolyte signal could be an increase in the T1 relaxation time of the cation, which would lead to some signal saturation at short delay times. To check for this effect, T1 saturation recovery measurements were performed on the 7Li-electrolyte signal, with values obtained during the experiment series. These measurements showed T1 to undergo only a minor change from 2.1 to 1.9 s at the start and end of the experiment, respectively. The minor change in T1 may indicate the presence of radicals in solution or simply could be a consequence of the changing ratio of lithium isotopes. A delay of 15 s was used between consecutive experiments to avoid any variations due to T1 changes and hence this factor could be ruled out. From these results, it is clear that the amount of 7Li is decreasing faster from the electrolyte than it is appearing in the metal region. A more refined model is hence sought to explain these results. Simulations with SEI Formation (kSEI > 0). Allowing for the reaction of Li+ in the electrolyte to form an SEI layer provides an additional route to deplete the electrolyte (and therefore reduce the electrolyte signal), but also reduces the effective exchange rate between lithium in the electrolyte and metal by lowering [Li+] (3). Figure 3 shows how the simulation results compare with the experiment when SEI formation is enabled with a fixed value of kex = 2.0 m−2 h−1. Under these conditions, it is possible to reach simultaneous agreement between simulation and the electrolyte and metal NMR results (for kSEI = 1.5 m−2 h−1). A simple way to understand this result is that the 7Li-metal signal does not increase sufficiently to account for the full amount of 7Li+ that is being removed from the electrolyte and therefore an extra “sink” for 7Li+ is required. The formation of an SEI layer satisfies this need, both in terms of numerical agreement with the model and chemical understanding of the processes occurring in the system. There are still several areas where the model and the approach seem unsatisfactory. First, at long times, the experimental 7Li-metal NMR signal seems to reach a plateau

nSEI(t ) = Ve[Li+]0 (1 − [Li+](t )) = Ve[Li+]0 (1 − exp(−k SEISAt ))

(14)

and the thickness of the SEI layer can be estimated by dSEI(t ) =

MnSEI(t ) ρSA

(15)

where Ve is the volume of the electrolyte (assumed to be constant throughout the experiment), M is the average molar mass of the constituents of the SEI layer per Li ion, ρ is the density, and SA is the surface area of the lithium metal as before. For kSEI = 1.5 m−2 h−1 as found from the model fit and assuming an SEI layer composed mainly of Li2O (M = 29.88 g/ mol and ρ = 2.012 g/mol), the thickness of the SEI layer would grow at a rate of approximately 6 nm h−1 (for two other common SEI ingredients, LiOH and Li2CO3, the values would be 13 and 14 nm h−1, respectively). These numbers appear to be well in line with literature values. We note that the NMR spectra (Figure S1) do not show the development of any additional peaks associated with SEI formation, nor would they be expected to do so, given the broad NMR signal associated with this amorphous/crystalline phase, and the small quantity of SEI material overall. The presence and growth of the SEI layer can be expected to slow down kex due to reduced ion permeability. Therefore, this effect is also modeled. Simulations with SEI Layer Impacting kex. The SEI layer is an ionic conductor (hence why batteries will still cycle even after the SEI layer has formed), but its presence restricts the availability of Li+ at the surface of the metal to some extent. The growth of the SEI may also “freeze in” the isotopic ratio of the exchange pool of lithium to the SEI layers that form in the initial stages of the experiment. In practice, it will be difficult to 12602

DOI: 10.1021/acs.jpcc.8b01958 J. Phys. Chem. C 2018, 122, 12598−12604

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The Journal of Physical Chemistry C

Figure 4. NMR results (points) and simulation results (lines) with fixed molar ratio, exchange coefficient, kex = 3.0, kSEI = 2.0, and kd taking the values shown (in units of nm−1) for the signal intensities vs immersion time. NMR intensity is plotted with respect to the intensity at t = 0.

the SEI permeability to ion motion slows with time, but significant growth is observed over long periods of time. The model presented and tested here therefore provides a good means of studying SEI formation dynamics and could be employed further to determine the influence of electrolyte additives of surface modification.

distinguish between models associated with these phenomena. Instead, we implement a simplified model, whereby the exchange rate depends on the SEI thickness, according to rex = kex[Li+]SA exp(−kddSEI(t ))

(16)

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where kd is an additional rate constant with dimensions of m−1. Although several models would be consistent with reasonable assumptions, an exponentially decaying permeation rate would be consistent with a gradual decrease of ion conductivity of the SEI layer (e.g., also decrease of porosity). Other models, for example, with d−1 and d−2 behavior have been considered, which could be rationalized by electrophoretic and diffusion transport, respectively. These models, however, were found to be incompatible with the results, probably because the flow cannot be characterized by these regimes alone. The simulation results for the model including the SEIlimited exchange rate are shown in Figure 4, where the values of kex and kSEI were fixed at 3.0 and 2.0 m−2 h−1. The nature of the SEI composition affects the fitted rate constant, kd, as the molar volume of the SEI material impacts the simulated SEI thickness, dSEI(t), grown at any point in time. The numbers reported here are based on Li2O as the major component. Other Li-based components could be used as discussed above and would lead to slightly different layer growth rates. Variation in kd leads to minor differences in the electrolyte profile, but major changes in both the magnitude and overall shape of the metal profile. In every case, there is an initial increase in the 7Li-metal intensities as the exchange proceeds between the 7Li in the electrolyte and the 6Li at the surface of the metal. However, when kd is high (5 nm−1), the exchange rapidly slows and only small amounts of 7 Li are added into the metal, whereas the existing 7Li diffuses away from the surface leading to an overall decrease in the NMR signal due to the skin effect. For smaller values of kd, there is a more balanced effect and the 7Li-metal signal reaches a plateau as the exchange slows. The reproduction of the plateau feature in the metal signal intensities in the range of kd = 1.0−1.5 nm−1 marks a significant improvement over the previous models, which did not show this effect. As shown here, it is only observed when the exchange rate between the electrolyte and the metal slows with time. Overall, it is seen that

CONCLUSIONS We have provided here results of Li exchange between bulk Li metal and an electrolyte solution by tracking isotope fractions over time with 7Li and 6Li NMR. 7Li data were most useful due to the better signal-to-noise ratios, especially in the signals from the metal region. Theoretical models, based on diffusion in the metal, exchange between the two phases, SEI growth, and associated changes in surface permeability, were found to fit the experimental data well. These findings allow for an indirect characterization of the origination and growth of an SEI layer. The use of isotopic labeling of the metal, in this case, allows determining slow exchange processes and changes over many hours. These findings and techniques could be important for screening the behavior of SEI growth and Li-ion permeability with different electrolytes, additives, and surface modifications.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcc.8b01958. Full time series of 7Li and 6Li NMR spectra; additional simulations of isotope exchange across the interface; additional analysis and discussion of 6Li NMR data (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Alexej Jerschow: 0000-0003-1521-9219 Notes

The authors declare no competing financial interest. 12603

DOI: 10.1021/acs.jpcc.8b01958 J. Phys. Chem. C 2018, 122, 12598−12604

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The Journal of Physical Chemistry C

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ACKNOWLEDGMENTS Funding is acknowledged from the US National Science Foundation under Award CHE-1412064.

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DOI: 10.1021/acs.jpcc.8b01958 J. Phys. Chem. C 2018, 122, 12598−12604