Solid Electrolyte Interphase in Li-Ion Batteries: Evolving Structures

May 10, 2012 - Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation. Jasper A. Vrugt. Env...
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Solid Electrolyte Interphase in Li-Ion Batteries: Evolving Structures Measured In situ by Neutron Reflectometry Jeanette E. Owejan,*,† Jon P. Owejan,† Steven C. DeCaluwe,‡,§ and Joseph A. Dura*,‡ †

Electrochemical Energy Research Laboratory, General Motors, Honeoye Falls, New York 14472, United States NIST Center for Neutron Research, Gaithersburg, Maryland 20899-6102, United States § Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United States ‡

S Supporting Information *

ABSTRACT: Li-ion batteries are made possible by the solid electrolyte interphase, SEI, a self-forming passivation layer, generated because of electrolyte instability with respect to the anode chemical potential. Ideally it offers sufficient electronic resistance to limit electrolyte decomposition to the amount needed for its formation. However, slow continued SEI growth leads to capacity fade and increased cell resistance. Despite the SEI’s critical significance, currently structural characterization is incomplete because of the reactive and delicate nature of the SEI and the electrolyte system in which it is formed. Here we present, for the first time, in situ neutron reflectometry measurements of the SEI layer as function of potential in a working lithium half-cell. The SEI layer after 10 and 20 CV cycles is 4.0 and 4.5 nm, respectively, growing to 8.9 nm after a series of potentiostatic holds that approximates a charge/discharge cycle. Specified data sets show uniform mixing of SEI components. KEYWORDS: Li ion battery, solid electrolyte interphase, in situ neutron reflectometry

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INTRODUCTION Ongoing research and development of lithium ion battery materials for automotive applications is largely focused on the durability of electrodes and their electrolyte interactions. Because of the instability of the electrolyte with respect to the anode chemical potential,1−4 the electrolyte components decompose to form lithium-containing inorganic and organic compounds on the electrode surface, thus reducing the amount of active lithium available to the cell. Beneficially, this layer is passivating in nature, such that ionic conductivity is maintained while electronic conductivity drastically decreases, preventing further electrolyte decomposition.5 Of the identified causes for capacity fade, continued growth of the solid electrolyte interphase (SEI) layer, primarily due to fresh electrode exposure induced during lithiation, is therefore considered a necessary evil. Numerous previous investigations have sought to understand the SEI layer, primarily by performing electrochemical tests and subsequently studying the SEI by ex situ spectroscopy or microscopy techniques. It is of great interest to expand these measurements to study the growth, composition, thickness, and porosity of the SEI in situ as a function of operating conditions. The efficacy of SEI forming additives may be directly evaluated if such a method could be developed, and various hypotheses, such as the preferential decomposition of various electrolyte components to form SEI and decomposition of metastable lithium alkyl carbonates into more stable compounds could be directly verified.6−18 Additionally, the SEI is understood to © 2012 American Chemical Society

evolve with temperature, potential, and time, although these dependencies have not yet been thoroughly explored.6,7,9−11,19,20 A thorough review of SEI characterization to date is summarized by Xu,21 and more recently by Verma et al.22 It is clear that although ex situ spectroscopic surface characterization of the SEI gives evidence that the SEI is a layered structure containing inorganic components closer to the anode surface and organic moieties in proximity to the electrolyte interface,7,10,23,24 the composition of the organic species in situ is under debate.25−28 Discrepancies arise due to not only the possibility of trace impurities including moisture or oxygen but also the ex situ nature of these experiments. Synthesis of bulk mono- and dialkyl carbonates utilized for exhaustive characterization efforts has been undertaken by Xu et al.29−31 Neutron reflectometry, NR, determines the depth profile of the scattering length density, SLD (which is related to composition), by fitting the intensity of reflected neutrons as a function of grazing angle from the surface, θ, providing subÅngström precision of features greater than ∼1.5 nm in thickness.32,33 The technique is mature, having been widely used for studying magnetic materials,34 thin film interfacial structures in polymers,35 biological membranes,36,37 and recently for Li intercalation into Si electrodes.38 NR is ideal Received: March 2, 2012 Revised: May 4, 2012 Published: May 10, 2012 2133

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a subsequent anodic sweep to 3.0 V at a rate of 10 mV s−1. A set of 10 CV scans was then initiated in the cathodic direction. Directly after, a potentiostatic hold at 250 mV was started by ramping the voltage down at 10 mV s−1. The hold lasted until NR data collection was complete, typically around 8 h, initiated after a current of 1 × 10−9 A was reached. NR data was taken as a series of fast scans, which were compared to confirm that the sample structure was invariant to within statistics. Scans that did not vary from one another were subsequently combined to reduce measurement uncertainty, as described in the Supporting Information. Therefore, the duration of the reflectivity data collection is not believed to affect the interface structure being studied. This procedure was repeated with a hold at 150 mV. Six additional potentiostatic holds were carried out after 10 mV s−1 ramps to the new potential, but without being preceded by CV cycles. Integrated charge for all eight holds was calculated from recorded data. Neutron Reflectivity Fitting. Specular neutron reflectometry was performed by standard procedures (see the Supporting Information). Because NR measures the reflected intensity rather than the amplitude, the phase of the neutron wave is not measured; therefore, single NR spectra cannot be directly inverted to determine a SLD profile. Refl1D48 was used to model the reflectivity data. The SLD depth profile is represented as a series of material layers of specified scattering length density (real and imaginary) and thickness, with Gaussian interfaces between them. Reflectivity is computed using the Abeles optical matrix method, with interfacial effects computed by the method of Nevot and Croce or by approximating the interfaces by a series of thin layers. In some cases, refinement of multiple reflectivity data sets with constraints between the models has been employed. The three instrumental fitting parameters that were allowed to vary were the beam intensity, the residual background, and the incident angle offset. Refl1D uses a Bayesian approach to determine the uncertainty in the model parameters. By representing the problem as the likelihood,

for in situ lithium ion battery diagnostics as it is sensitive to light elements in SEI compounds. Additionally, isotopic contrast variation for H and Li allow accurate depth profiles of these elements or isotopic labeling of sources for these elements. Furthermore, fluid exchange with working fluids of different scattering length densities allows direct measurement of the porosity depth profile. Unlike electron spectroscopy techniques, neutrons are highly penetrating and nonperturbing, thus making possible in situ measurements of unaltered SEI layers. This study presents the first ever use of in situ NR to measure the structure of an SEI in a lithium battery. The cells are configured as a lithium half-cell, where the lithium acts as both counter and reference electrode. Because specular neutron reflectivity averages over in-plane features, a rough surface such as that which graphite exhibits would not be applicable to NR measurements. Copper, deposited onto a Ti adhesion layer on a single-crystal Si substrate, was chosen as working electrode as it does not intercalate lithium, and thus all electrochemical charge is attributed to decomposition of the electrolyte to form the SEI layer. Copper is frequently used as an “ideal” electrode for SEI studies,31,39−45 and it has been established that films grown on nonintercalating substrates are similar to those grown on carbon materials at low potentials in Li salts.46,47 Aurbach et al. have also shown that the thermodynamics of reduction processes of the electrolyte solution species are governed by the cation used.43 The electrolyte consisted of 1 mol/L LiPF6 in a 1:2 (v/v) ratio of deuterated ethylene carbonate, EC, and diethyl carbonate, DEC. The deuteration was chosen for two reasons. It increases the SLD of the electrolyte to provide greater contrast to the SEI, which is expected to have a relatively low SLD due to Li incorporation. Additionally, isotopic labeling of the EC allows one to attempt to identify the possible preferential decomposition of cyclic over acyclic carbonates.

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L = e−χ /2, of observing the measured reflectivity curve given a particular parameter set, Refl1D can use Markov Chain Monte Carlo (MCMC) methods49 to draw a random collection of parameter sets from the joint parameter probability distribution. In this study a DREAM algorithm49 was employed since it samples the entire range of parameter space more effectively than other approaches. The distribution of values for a particular parameter from the collection is used as an estimate of the probability density for that parameter. The parameter values, SLD profile, and model reflectivity curve that are reported are for the best fit, i.e., the parameter set with the lowest value of χ2. The uncertainty of a given parameter is expressed as the 68% confidence interval, the shortest range of parameter values that includes 68% of the total probability density. The 68% confidence interval would correspond to the ±1σ uncertainty level if the parameter values were normally distributed. To visualize the uncertainty in the best fit reflectivity curve, a selection of 50 parameter sets is randomly chosen. An envelope that includes the 68% centrally distributed about the median of the calculated reflectivity values for each Q value is plotted as a shaded region. To first approximation, there is a 68% probability that a point on the true reflectivity curve would fall within this shaded region. Similarly, the same 50 parameter sets are used to visualize uncertainty in the SLD depth profile. While SLD profiles have an arbitrary zero point of depth, because the Ti layer and its associated interfaces are held fixed for all test points, the SLD profiles are coaligned at this layer. An envelope that includes the central 68% of the model SLD values for each depth z of the profile is plotted as a shaded region. To first approximation there is a 68% probability that a point on the true SLD profile would fall within this shaded region. The 95% confidence intervals, displayed with lighter shading, are calculated in the same way from the same selection of parameter sets.

EXPERIMENTAL METHODS

Half Cell Fabrication. The native oxide was first removed by HF cleaning from a single-crystal silicon wafer (Institute of Electronic Materials Technology) that is 7.6 cm in diameter by 0.95 cm thick with N/P/100 type/dopant/orientation, respectively. (Certain commercial equipment, instruments, or materials are identified in this paper to foster understanding. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.) Immediately after cleaning, a 7.5 nm titanium adhesion layer and 40 nm copper layer were deposited via electron beam evaporation, at the Semiconductor and Microsystems Fabrication Laboratory at Rochester Institute of Technology. Layer composition, uniformity, and roughness of a sacrificial wafer were verified using Xray reflectometry. A lithium foil (Alfa Aesar), 5.72 cm diameter, was polished and pressed onto a copper/titanium wafer to form the counter/reference electrode. A 500 μm thick Kalrez (Dupont) gasket was adhered to the working electrode, determining a 20.26 cm2 active area. Two 25 μm gold foils (99.98%, Goodfellow) served as current collectors on each side of the half cell. Partially deuterated electrolyte was made with LiPF6 (Alfa Aesar), deuterated EC (CDN Isotopes), and DEC (Alfa Aesar) for a final composition of 1 mol/L LiPF6 in 1:2 d-EC:DEC (v/v). Electrochemical Testing. Electrochemical data was collected on a Gamry ref 600 potentiostat. All voltages are reported vs Li. Cyclic voltammetry, CV, was carried out to determine peak locations for potentiostatic hold test points; the voltage window was limited to avoid lithium plating and copper oxidation. CV scans were initiated at the open circuit potential, OCV, with a cathodic sweep to 0.05 V with

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RESULTS AND DISCUSSION NR data was taken using AND/R at the NIST Center for Neutron Research50 of the virgin Cu cathode in the electrolytefilled cell at OCV (test point a-OCV). Subsequently, an SEI 2134

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Figure 1. Neutron reflectivity vs Q is shown for the sample at OCV and after 10 CV cycles during a hold at 250 mV. The solid lines are the best fit, from a simultaneous fit to the two data sets. Inset − The SLD profiles for the two best fits. The SLD values of Si, Cu and Ti (calculated from known densities) are indicated, and Electrolyte, SEI, and TiSix layers are identified. For both parts, the darker and lighter shaded regions are the 68% and 95% confidence intervals, respectively, as discussed in the methods section.

This fit (not shown) resulted in not only an increase in χ2 but also an increase in the Bayesian information criterion, thus indicating that the model with an SEI is statistically more likely than the model excluding it. The next test point (c-150 mV) repeated the cyclic voltammograms, followed by a potentiostatic hold at 150 mV. Charge data for all test points may be found in the Supporting Information. The SEI layer at test point c is remarkably similar to the previous test point, b, having similar SLD and interface widths, but slightly thicker at 4.5 nm [4.0, 4.7] nm due to the lower reduction potential and/or additional 10 CV cycles. These results also agree with a 3.6 nm [3.2, 4.2] nm thick SEI deposited by a similar procedure in a second cell with a fully protonated electrolyte (see the Supporting Information). Subsequently, six data sets (d−i) were taken at various potentials by slowly ramping the potential at a rate of 10 mV s−1 to the next value and holding during NR data collection, as summarized in Figure 2, which shows representative CV curves taken throughout the experiments. Note that complex chemistries occur as demonstrated by the various peaks in the CV curves, the origins of which have been discussed in the literature.31,43,44,51 They serve for us as points at which to execute the potential holds and NR measurements. The decrease in the magnitude of the current density with increased cycling in Figure 2 demonstrates the passivating nature of the growing SEI layer, also verified by smaller reductive currents collected during NR measurements. Thus, in our study, the most active test points with respect to SEI growth were those between b and d, and point f, where the passivation layer was forming, while subsequent points were much less active. The associated NR data sets for test points c through i are shown in Figure 3. Each had excellent individual fits to a model

was produced by running 10 CVs, followed by a potentiostatic hold at reducing potential (250 mV vs Li). NR data were again taken after the current decayed to near zero (test point b-250 mV). These two data sets, shown in Figure 1, show a distinct difference in amplitude and oscillation peak position as a function of momentum transfer, Q, due to the effects of the SEI layer on the scattering. These two data sets were simultaneously fit, with common parameters for the Ti layer, its surrounding interfaces, and the Cu and electrolyte SLDs, to provide an accurate structural determination of the layered sample with and without SEI. All other parameters in the model were varied independently for each data set, except the Si substrate SLD which was held fixed at the value calculated from the known scattering length and the density of crystalline Si. The resultant parameters and 68% confidence intervals are shown in Table S2 of the Supporting Information. The reflectivity, R, is plotted vs momentum transfer, Q = (4π/λ)sin θ, where λ is the wavelength of the neutrons, 0.5001 ± 0.0004 nm. Fitting this data reveals that the Cu SLD is 99% of the bulk value, and the Ti SLD indicates silicide formation. Furthermore, a copper carbonate/hydroxide liganding layer was present on the initial surface, formed due to the pristine nature (no surface oxides) of the copper. After the CV scans, the liganding layer is removed and an SEI is deposited that consists of a single 4.0 nm [3.6, 4.2] nm thick layer with SLD well below the level of the electrolyte, as seen in the inset to Figure 1. (Numbers in brackets are the 68% confidence range determined by the fitting software. NR error bars are plus or minus one standard deviation based upon propagating counting statistics from the specular, background, and slit scans used to obtain the specular reflectivity.) To confirm that this model, which includes an SEI layer, is a better fit to the data than one without an SEI, an additional simultaneous fit excluding the SEI was performed. 2135

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Figure 2. Cyclic voltammogram results for selected scans. Test points b-i denote the location of potentiostatic holds for NR testing.

that held all parameters the same as determined from the simultaneous fit (Figure1) except the Cu and SEI thickness, the SEI SLD, both interface widths surrounding the SEI, and three instrumental parameters which varied insignificantly. The resultant SLD profiles from these fits (along with test point b) are shown in Figure 4, and the fitted parameter values and 68% confidence intervals are shown in Table S3 of the Supporting Information. Key fit parameters are summarized in Figure 5. It was found that the apparent thickness of the Cu layer increased from a starting thickness of 41.51 nm [41.39, 41.57] nm for test point b to a thickness of 41.81 nm [41.76, 41.88] nm and 42.17 nm [42.10, 42.23] nm for test points e and f, respectively, because of the addition of a few monolayers of material, presumably Cu2O (which has a high SLD sufficiently close to that of Cu). This layer is too thin to be accurately modeled as a separate layer,32,33 and is subsequently removed during cathodic potential holds in test points g-i, where the apparent Cu layer thickness decreases to a final value of 41.43 nm [41.40, 41.49] nm, which is the same as the initial value to within uncertainty. Sufficient published electrochemical data of copper in nonaqueous systems identifies Cu1+ oxidation occurring near 2.0 V vs Li and Cu2+ oxidation occurring at 3.5 V vs Li.51 These increases in apparent copper thickness originate from the demand of current-generating chemical reactions to sustain the potential holds. Thus, even at test point g (800 mV) where one would expect all oxide to be reduced, we observe a larger than initial copper thickness since we remained at a slightly oxidative potential (test point f) for an extended amount of time and have not fully removed the Cu1+ oxide during the hold at g. In general, it is important to note that the species formed during these extended potential holds will not correspond directly with those formed at similar potentials during the CV scans in Figure 2. These fits and associated uncertainties clearly show that the SEI present on the copper surface changes in both composition and thickness as a function of potentiostatic holds. SEI measured thickness grows steadily from b to d, from 3.96 to 4.75 nm. At these test points, the SLD of the layer also increases from 1.90 to 2.24 (× 10−4 nm−2), demonstrating that a layer with molecules originally rich in lithium incorporates molecules that contain relatively less lithium (i.e., LiOH, SLD = 0.0602 × 10−4 nm−2 vs (pronated) lithium alkyl carbonates, SLD = 2.22 × 10−4 nm−2). While lithium underpotential

Figure 3. Neutron reflectivity (expressed as RxQ4) vs Q is shown for the sample measured during holds at the potentials indicated. The solid lines are the best individual fit, with several parameters kept constant at values determined from the simultaneous fit of the OCV and 250 mV data (see text for details). The darker and lighter shaded regions are the 68% and 95% confidence intervals, as discussed in the Methods section.

deposition and stripping may occur during these test points, it would add at most one monolayer, which can also be consistent with the thickness and SLD values measured. The layer shrinks slightly at e (2.3 V), presumably due to the oxidation current and/or due to solubility of several molecules. Moving from e (2.3 V), a stable region for carbonate-based electrolytes, to f (1.5 V), we observe a doubling in thickness accompanied by an increase in SLD, with little change when the potential is decreased to 800 mV at test point g. The next two test points, h and i, demonstrate increasing reduction of the SLD as the potential is lowered, indicating a 2136

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Figure 4. The SLD as a function of depth for the SEI deposited on Cu, showing the evolution of the thickness, SLD, and interface roughness with hold potential, as indicated. The lines for b-i, are best individual fits, with several parameters kept constant at values determined from the simultaneous fit of the OCV and 250 mV data (see text for details). The inset shows the full SLD profile, with the fits coaligned on the Ti layer.

Future measurements employing Li isotopic substitution will be able to distinguish between these two possibilities. While for some test points (b−e, h, and i) large composition gradients are evident in the SLD profiles, for select test points, f and g, the uniformly flat SLD profiles show complete mixture of components within the observed SEI. Such observations contradict the segregation between inorganic and organic species suggested by previous studies.2,52−54 Such a segregation would be very evident in our SLD profiles, as the inorganic molecule Li2O has a very low SLD (0.8126 × 10−4 nm−2), and segregation of this molecule to near the copper surface would result in a dip in the SLD profile well below the SEI SLD of roughly 2.5 × 10−4 nm−2 seen in Figure 4. Similarly, we also do not observe a segregated lithium-containing organic phase closest to the electrolyte. These materials would cause a peak in the SLD profile at the electrolyte/SEI interface because the SLD values of deuterated lithium alkyl carbonates (SLD = 8.65 × 10−4 nm−2) and dicarbonates (SLD = 6.45 × 10−4 nm−2) are greater than that of our electrolyte. To further investigate the possible composition of the SEI layer as a function of potential holds, a model that estimates the amount of various SEI compounds was created. The partial thicknesses of each compound and SLD of the total were calculated and matched to the NR fitting results. X-ray Photoelectron Spectroscopy, XPS, data (contained in Supporting Information) indicates the presence of LiF, LiOH, lithium alkyl carbonates, and non-lithium containing polyethylene oxide (SLD = 3.86 × 10−4 nm−2), PEO. The fractional amount of lithium was assigned to each molecule according to the peak areas in the deconvoluted XPS data. The relative amount of PEO was determined from the C1s and O1s peaks in the XPS data. Porosity of the SEI is handled by adding electrolyte to the composition, which affects both SLD and overall thickness. The thickness is determined from the number of molecules in the model and the molecular volume. The amounts of each component were adjusted within the variability range observed in the post mortem XPS measurements, and guided by known chemical reactions producing SEI components,21 until the

Figure 5. Selected fitting parameters as a function of test point. For reference, the hold potential vs Li is also shown. The red and blue dashed lines are the total thickness and SLD from the composition modeling, as described in the text, and match well with values measured by NR. Cu thickness is plotted relative to the thickness measured in test point a − OCV.

net gain of lithium-containing or low density molecules. The large SLD gradients seen at the SEI/electrolyte interface in these last two test points can be interpreted in two ways. The one-layer model shows the layer thickness roughly constant or slightly decreasing as the SLD decreases (in going from test point g through i), which can be interpreted as the SEI layer as a whole is substituting high SLD material (Li poor or dense) with lower SLD material (Li rich or less dense). Alternatively, examination of the SLD profile indicates a layer that increases in thickness from g to h and less so to i, wherein the low SLD material is added near the Cu interface. This composition gradient interpretation would indicate that Li-rich material is added near the Cu interface while the already deposited material remains adjacent to the electrolyte and is roughened. This is supported by the net charge accumulation during these potential holds, indicating net molecules were added to the SEI. 2137

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Figure 6. SEI thickness and composition modeled by use of XPS, electrochemical, and NR measured parameters compared to the thickness measured by NR. Each segment in the bar represents the partial thickness (not gradient) that each molecule contributes to the SEI thickness. The hold potential and charge ratio (measured during the holds/modeled) are also shown.

model thickness and composition (red and blue dashed lines, respectively) matched the thickness and SLD measured by NR (red squares and blue circles, respectively) as seen in Figure 5. The results of this model are shown in Figure 6, where the modeled partial thicknesses are summed and compared to the NR measurement. The changes of the SEI composition when moving to successively more reducing potential include the increase in concentration of LiOH and LiF molecules, and the decrease of lithium alkyl carbonates. The porosities determined in these models (9−10%) are similar to the 11.0% [10.6%, 15.9%] porosity determined by another cell that utilized contrast variation via fluid exchange (see the Supporting Information). The quantity of LiOH and LiF grows during test duration, while the lithium alkyl carbonate concentration fluctuates during testing. The molecules identified above are consistent with previous ex situ studies, however the lack of segregation of certain molecules shows that our in situ evaluation of the SEI structure and composition sheds significant light on how the SEI initiates and grows. Furthermore, others have modeled that aging of the SEI over time will reduce hydroxides and lithium alkyl and dicarbonates to the more stable Li2O, LiF, and Li2CO3 components.55 However, this combined electrochemical/reflectivity test was run over a period of approximately 3 days, and was not examined ex situ by XPS until approximately 60 days later, when the cell was released from radiation screening. After this duration, we did not see evidence of the oxide or carbonate in our ex situ data. It is important to note here that the total accumulated charge measured during the NR experiments is similar to additional experiments performed on button cells. Much less charge was required for the composition models than the measured charge, indicating that a significant portion of the electrolyte decomposition products were not retained within the SEI layer. The ratio of the measured charge divided by the modeled charge is shown in Figure 6 and increases from 1.85 at test point b to 5.39 at test point i. This is most likely due to the solubility of several SEI components.

Several factors add slightly to the possible systematic error in the SLD values used to determine these compositions, including sample warp and lateral inhomogeneities observed in XPS; see the Supporting Information section for additional detail. Additionally, these models do not have a unique solution; less probable combinations of molecules may also simultaneously fit the data. Finally there is a possibility that a second layer with SLD similar to the electrolyte (and thus invisible to NR) exists in this sample. This, however, is unlikely in that if this second layer grows in proportion to the excess measured charge its increased thickness would make it easier to detect, and it is likely that its SLD would evolve with changing potential, like that of the observed SEI layer. The SLD of this layer would then differ from the electrolyte SLD by a significant amount at some potentials and would be measurable via NR for those cases.

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CONCLUSIONS It is demonstrated that neutron reflectivity is a highly effective tool for investigating in situ growth and evolution of the SEI. A well-functioning electrochemical cell and electrode materials compatible with NR measurements have been developed which are highly suited for this application, and the SEI has been measured for a range of potentiostatic holds. Results in this paper represent the first direct measurements of critical SEI properties − such as thickness, porosity, layered structures and gradients, and chemical composition − on an operating Li cell, without confounding experimental artifacts associated with ex situ techniques. These results include an SEI thickness of 4.0 to 4.5 nm for 10 to 20 CV cycles growing to 8.9 nm after a series of potentiostatic holds that approximates a charge/discharge cycle. The SLD profile of several test points indicates a lack of significant gradients, contrary to proposed structures in the literature. The SEI thickness grew most rapidly directly after test point e, an oxidative hold, where electrolyte components are stable, to point f (1.5 V), which lies in the region of electrolyte instability. Due to its passivating nature, growth of the SEI thickness slowed subsequent to this, even at the lowest potentials examined. However, lower SLD, Li-rich molecules 2138

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were substituted into the SEI at lower potentials, as demonstrated in the progressive change in SLD when moving from test points g through i. Furthermore, roughly 0.6 nm of a high SLD material, for example, Cu2O was deposited under the SEI adjacent to the Cu anode between 2300 mV and 1500 mV vs Li, and then removed completely upon electrochemical reducing potentials. Future implications of this study include the capability to directly and quantitatively study SEI properties as a function of electrolyte composition (including additives), temperature, voltage, current, and cycling/time, etc. In addition, the direct measurement of critical SEI parameters, both presented herein and in the future under a variety of conditions, will lead to marked improvements in the accuracy of numerical simulations of processes in Li batteries involving the formation of and ion transport within the SEI.17,56−60 Taken together, such NR investigations will enable future systematic improvements for commercial device performance, affordability, and sustainability.

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ASSOCIATED CONTENT

S Supporting Information *

Additional figures in the form of charge accumulation, XPS overlay plots, reflectivity plot for the fluid exchange cell, and scattering length density profile as a function of test point for the fluid exchange cell. Tabular data for atomic % composition by XPS for the non fluid exchange cell, description of ex situ sample preparation, cell build, and test for the fluid exchange cell. Neutron Reflectivity data collection and analysis details, results of data from fluid exchange cell. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 585-259-2130 (J.E.O.); 301-975-6251 (J.A.D.). Fax: 585-624-6680 (J.E.O.); 301-921-9847 (J.A.D.). E-mail: jeanette. [email protected] (J.E.O.); [email protected] (J.A.D.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors gratefully acknowledge funding for this work through NIST ARRA Award Number: 60NANB10D027, part of the American Recovery and Reinvestment Act of 2009, as well as the National Research Council for funding through the NRC Research Associateship Program. Nicholas P. Irish of GM Global R&D is acknowledged for XPS data collection and peak deconvolution and Paul A. Kienzle, NCNR, for useful discussions of NR fitting.

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