Toward New Solvents for EDLCs: From Computational Screening to

Article pubs.acs.org/JPCC

Toward New Solvents for EDLCs: From Computational Screening to Electrochemical Validation Christoph Schütter,† Tamara Husch,‡ Martin Korth,*,‡ and Andrea Balducci*,†,§ †

MEET Battery Research Centre and Institute of Physical Chemistry, University of Muenster, Corrensstrasse 28/30, 48149 Münster, Germany ‡ Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany S Supporting Information *

ABSTRACT: The development of innovative electrolytes is a key aspect of improving electrochemical double layer capacitors (EDLCs). New solvents, new conducting salts as well as new ionic liquids need to be considered. To avoid time-consuming “trial and error” experiments, it is desirable to “rationalize” this search for new materials. An important step in this direction is the systematic application of computational screening approaches. Via the fast prediction of the properties of a large number of compounds, for instance all reasonable candidates within a given compound class, such approaches should allow to identify of the most promising candidates for subsequent experiments. In this work we consider the toy system of all reasonable nitrile solvents up to 12 heavy atoms. To investigate if our recently proposed computational screening strategy is a feasible tool for the purpose of rationalizing the search for new EDLC electrolyte materials, we correlatein the case of EDLCs for the first timecomputational screening results with experimental findings. For this, experiments are performed on those compounds for which experimental data is not available from the literature. We find that our screening approach is well suited to pick good candidates out of the set of all reasonable nitriles, comprising almost 5000 compounds.

1. INTRODUCTION Electrochemical double layer capacitors (EDLCs), also known as supercapacitors, are considered as one of the most important electrochemical storage devices.1−4 State-of-the-art EDLCs contain activated carbons (AC) as active materials and mixtures of quaternary ammonium salts, typically tetraethylammonium tetrafluoroborate (Et4NBF4), in organic solvents like propylene carbonate (PC) or acetonitrile (ACN) as electrolyte.5−8 In these devices the charge is stored electrostatically at the interface between the electrodes and the electrolytes. Thanks to this physical storage process, EDLCs can be charged and discharged within seconds,1−4 feature high power (10 kW·kg−1) and a very high cycle life (>500 000 cycles). The operative voltage in these EDLCs is on the order of 2.7−2.8 V and their energy is on the order of 5 Wh·kg−1.3,8,9 Nowadays EDLCs are used in an increasing number of applications, where fast delivery and uptake of energy as well as reliability are needed. Nevertheless, many reports indicated that improving the performance of these devices, especially in terms of energy, would increase their feasibility for a greater number of applications allowing for a growth of the market size.7 Therefore, considerable efforts have been made to increase the energy of these devices during the past years. The energy of EDLCs is defined by the expression E = (1/2) CV2, where C is the capacitance and V is the operative voltage of the device. Taking into account this expression, it is clear that in order to increase the EDLCs’ energy, the most convenient way is to increase the operative voltage, which is © XXXX American Chemical Society

limited by the electrochemical stability of the electrolyte of these devices.2 So far, three different categories of electrolytes are considered for the realization of EDLCs: Electrolytes based on (1) water, (2) organic solvents, and (3) ionic liquids (ILs).10 Each electrolyte has its own advantages and disadvantages. Water features low viscosity, high conductivity and a high dielectric constant and, therefore, are able to achieve high capacitance.11 The biggest drawback of water-based systems is the operative voltage, which is limited to maximum of 2 V.12−14 As mentioned above, when using organic solvents such as PC and ACN it is possible to realize EDLCs with operative voltages of 2.7−2.8 V. In the last years, also other types of organic solvents have been proposed, e.g., dimethylsulfone15,16 and adiponitrile (ADN),17,18 and it has been shown that using these alternative electrolytes the operative voltage of EDLCs could be increased up to 3.2−3.5 V.19 However, the viscosity of these electrolytes is higher and their conductivity and dielectric constant lower than those of water-based electrolytes.19 ILs are able to provide an even higher operative voltage than organic solvents (up to 3.5−3.7 V).19 However, due their high viscosity and low conductivity, the performance of IL-based systems at room temperature is limited. In particular, the equivalent series resistance (ESR) of these systems is higher compared to the Received: March 4, 2015 Revised: April 27, 2015

A

DOI: 10.1021/acs.jpcc.5b02113 J. Phys. Chem. C XXXX, XXX, XXX−XXX

Article

The Journal of Physical Chemistry C

therefore, if computational screening is a feasible tool for the determination of new electrolytes.

organic electrolyte based ones, reducing their power output at room temperature. Furthermore, ILs are typically more expensive than organic solvents.20 Considering this scenario, the state-of-the-art electrolytes still appear as the best choice for the realization of high power devices. Accordingly, many efforts are needed for introducing innovative electrolytes for the realization of high energy EDLCs. New solvents, new conducting salts as well as new ionic liquids should be considered and investigated in the future. However, it is clear that these explorative studies should be “rationalized”. As a matter of fact, to identify a new component, e.g., a new solvent, might become difficult and time-consuming, especially when dealing with a complete new group of compounds. In order to avoid a time-consuming “trial and error” approach, the introduction of an effective computational screening would be of great importance. Such a screening would allow for a fast prediction of the properties of a large number of compounds from many different classes, which in turn would enable the identification of the most promising candidates. Only these selected candidates should then be investigated, via traditional means, as electrolytes for EDLCs. There is a substantial body of work on the application of atomic-scale computational methods to electrolyte-related problems in applied electrochemistry,21−26 but computational screening of electrolyte components is still at the beginning, with exploratory studies by for instance Halls and Tasaki,27 Han et al.,28 and Bryantsev et al.29 Larger scale studies were published for inorganic30 and organic (flow cell)31,32 electrode materials. Recently, we have outlined a scheme for the systematic, large-scale screening of electrolyte components based on estimates for all relevant properties, to arrive at best possible suggestions for subsequent experimental work.33,34 Candidate compounds are automatically generated by systematic derivation (or taken from existing databases), properties are computed with quantum chemical, chemical engineering and chemoinformatics methods, results are analyzed according to systematic rules. In this work, we investigate the use of nitrile-based solvents as possible electrolytes for EDLC application. Motivated by our earlier work on ADN,18 we have recently constructed and evaluated a database with all “reasonable” (poly-)nitrile solvents up to 12 heavy atoms, where “reasonable” means no C/C double- or triple-bonds apart from those in aromatic systems and no rings other than 5 to 7 membered ones.34 On the basis of a sequence of molecular mechanics (MM), semiempirical PM6-DH+ and B86/TZVP density functional theory (DFT) calculations, followed by COSMOtherm computations to estimate properties other than the electrochemical stability, 4879 candidate compounds were listed with estimates for their electrochemical stability windows, melting/flash/boiling points, viscosities and ion solubilities/free energies of solvation for Li+ and PF6− ions. Here we analyze different strategies to pick up best candidates for the application as EDLC electrolyte materials. The overall best candidate, ACN, is already known to perform well as electrolyte solvent. From the other candidates, the most interesting three compounds were selected to prepare electrolytes for EDLCs. These electrolytes were then characterized via traditional means, regarding their physical properties and their electrochemical behavior. The aim of this study is to correlate the computational results with the experimental results, in order to check, if the latter results are validating the prediction of the computation and

2. EXPERIMENTAL SECTION 2.1. Computational Screening. Semiempirical PM6-DH +35,36 calculations were done with MOPAC201237 making use of the COSMO38 solvation model to generate the input for COSMOtherm.38 BP8639,40 and B3LYP41,42 DFT calculations have been performed with TURBOMOLE 6.443,44 using D2 dispersion corrections,45 the RI approximation for two-electron integrals,46,47 and again COSMO to generate the input for COSMOtherm. LPNO-CEPA48 were done with ORCA.49 TZVP, TZVPP and QZVP AO basis sets50 were employed for TURBOMOLE and ORCA calculations. Listed COSMOtherm (release C30−1301) property predictions are based on BP86/ TZVP calculations, not using COSMOtherm energy or vapor pressure files (see below for a discussion of this approach). Melting point predictions are available for all compounds from our previous work,34 but not used for the analysis presented here, as the viscosity turned out to be sufficient for the purpose of excluding nonliquid candidates. 2.2. Physical and Electrochemical Characterization. The investigated nitriles glutaronitrile (GTN 99%, SigmaAldrich), 2-methylglutaronitrile (2MGN 99%, Sigma-Aldrich) and adiponitrile (ADN 99%, Sigma-Aldrich) were first dried over molecular sieve (3 Å) until their water content was below 15 ppm, as measured by Karl Fischer technique. Tetraethylammonium tetrafluoroborate (Et4NBF4, Sigma-Aldrich) was used as conductive salt and added to the dry solvents until the maximum solubility was reached. Et4NBF4 (and not LiPF6 like in the computational screening) was used because of its important role in EDLC technology. The conductivity of the electrolytes was measured with a Solartron model 1260 Impedance coupled with a potentiostat/ galvanostat 273A PAR. A 0.01 mol·dm−3 aqueous solution of KCl (VWR) was used to calibrate the sealed glass conductivity cell with platinized Pt electrodes. Electrolyte preparation and assembling of the cell were done in a dry room with air humidity below 20 ppm. The temperature dependency of the conductivity for the electrolytes was determined in the range extending from −30 to +80 °C. The viscosity of the electrolytes was measured using an Anton-Paar Physica MCR 301 Rheometer, in the same temperature range as the conductivity. The shear rate for all tests was set to 50 s−1. Electrochemical stability windows of all electrolytes at 20 °C were evaluated in a three-electrode, Swagelok cell by linear sweep voltammetry (LSV) at 1 mV·s−1. The working electrode was a platinum microelectrode (embedded in PEEK; active area = 0.79 mm2). A heavy AC electrode was used as counter electrode. An Ag quasi-reference electrode was used as reference electrode. The measurements were performed using a Solartron model 1287A potentiostat controlled by Corrware software. Separate LSV tests were carried out on each sample to determine the cathodic and anodic electrochemical stability limits. The measurements were performed by scanning the cell potential from the open circuit potential (OCP) toward more negative (cathodic limit) or positive (anodic limit) potentials. Clean electrodes and fresh samples were used for each test. Carbon electrodes were prepared following the procedure reported in ref 51 using AC as active material. The ratio of active material (DLC Super 30, Norit), conductive agent (SuperC65, Imerys) and binder (CMC, Walocel CRT 2000 B


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Figure 1. Schematic representation of the screening strategy.

PPA 12 from Dow Wolff Cellulosics) was 90:5:5 with final electrode thickness of 98.1 μm and an electrode diameter of 12 mm. The mass loading of the electrodes was 5.3 mg·cm−2 average. Electrochemical investigations were carried out with Swagelok-type cells. The EDLCs were assembled in an argonfilled glovebox, with water and oxygen contents below 1 ppm. A Whatman GF/D glass microfiber filter (675 μm thickness and 13 mm diameter) was used as separator and drenched with 200 μL electrolyte (3-electrode configuration) or 100 μL (2electrode configuration), respectively. All electrochemical tests were performed at 20 °C using a VMP multichannel potentiostatic-galvanostatic system (Biologic Science Instrument, France). In order to evaluate the operative voltage of the electrolytes, cyclic voltammetry (CV) was carried out at 5 mV·s−1 in a 3electrode configuration. In this test, the counter electrode was a AC electrode with an active mass loading at least 5 times higher than that of the working carbon electrode. An Ag quasireference electrode was used as reference electrode. To evaluate the EDLCs performance with these electrolytes, impedance, CVs, and galvanostatic charge−discharge cycling were carried out in a 2-electrode configuration. An asymmetric

configuration with different electrode masses based on the expression C+·m+·ΔV+ = C−·m−·ΔV− was used for this test using values of specific capacitance (C) and voltage excursion (ΔV) obtained by the 3-electrode configuration.52 Cyclic voltammetry was carried out using scan rates ranging from 5 to 200 mV·s−1. The reported values for specific capacitance in the Results correspond to the value achieved at the half of the maximum voltage. Impedance spectra were recorded with 5 mV ac perturbation in the frequency region from 500 kHz to 10 mHz. For low frequencies the EDLC displays pure capacitive behavior and the capacitance can be calculated using the following equation: C=−

1 ω·Z″(ω)

(1) 53

On the basis of the work of Taberna et al., it is possible to obtain the time constant of the investigated materials from the impedance data by calculating the real part of the complex capacitance C′(ω) and the imaginary part C″(ω) of the complex capacitance C′(ω) = − C

Z″(ω) ω·|Z(ω)|2

(2) DOI: 10.1021/acs.jpcc.5b02113 J. Phys. Chem. C XXXX, XXX, XXX−XXX

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The Journal of Physical Chemistry C Table 1. Screening Results for Strategy 7, Compounds with Higher IP First Tb [°C] Tf [°C] μ(Li+) [kcal·mol−1] μ(PF6−) [kcal·mol−1]

IP [eV]

η [mPa·s]

ACN

10.40

0.42

114

−10

16.44

−1.59

SCN GTN EtCN

9.02 8.78 8.73

3.48 2.40 0.40

340 287 113

145 110 −12

17.33 16.59 15.90

−0.48 −1.17 0.78

1,2-dicyanocyclopentan

8.63

3.94

413

177

16.59

0.40

2-me-GTN (2MGN) 2,2-dime-SN (DSN)

8.57 8.51

2.59 2.13

295 313

115 121

16.35 17.18

−0.36 0.31

1,3-dicyanocyclopentane ADN 2,2-dime-GTN

8.49 8.47 8.39

4.22 3.23 2.41

365 343 289

152 144 110

16.00 16.14 16.23

−0.13 −0.63 0.50

name

C″(ω) =

Z′(ω) ω·|Z(ω)|2

comment flash point below 60 °C indicates possible safety problems solid at room temperature selected for experiments flash point below 60 °C indicates possible safety problems not commercially available, likely not liquid at room temperature selected for experiments not commerically available, likely not liquid at room temperature not commercially available selected for experiments very similar to 2MGN

electrolyte solvents and found errors of 5%−10% and excellent rankings with R values close to 1. For this accuracy, no additional information (for instance in the form of COSMOtherm energy or vapor pressure files) is necessary, which is convenient for a generally applicable, black-box, highthroughput strategy. Also other possible refinement strategies are not applied, to keep our approach as transferable as possible: COSMO-RS theory is not able to directly compute dynamical properties such as the viscosity. Any viscosity prediction with COSMOtherm therefore relies on QSPR techniques. Further, problem-specific adjustments of the QSPR model are possible, but not preferable in the context of a generally applicable approach. Computing solubilities of ions in solvents is nontrivial with COSMOtherm for high solubilities. As this is the case here, we used the chemical potential of the ions in the solvent as the only available, fast estimator for the solubility. As our calculations consider pure compounds, but our experiments (mostly) consider mixtures, a direct comparison of the data considered in this work is possible only for flash points, which is given in Table 3 and shows good agreement with experiment as well as a perfect ranking of compounds. Our approach to computing electrochemical stability windows is explained in in a previous publication.33 To give a short summary, redox potentials are estimated from hybridDFT/COSMO calculations on the optimized neutral and ionized compounds. Apart from comparison to experiment, the performance of different approaches for ranking stabilities is evaluated. We found hybrid-DFT/COSMO values to be very reliable for this purpose. The high accuracy of hydrid-DFT calculations for such calculations is known also from many other published studies if ideal conditions are assumed.26 There is on the other hand no approach available to compute the operative voltage of real systems without ad-hoc adjustments, and even the basic interactions of lithium atoms with organic molecules are nontrivial if very high accuracy is needed.56 This is one of the reasons why we do not focus on the agreement of the actual values with experiment, but on correctly ranking compounds with respect to their stability. Also in the following, we find that our ranking predictions are fitting if ideal conditions apply, but not in the case of real systems (see below for details). This gives further support to our idea of using (approximate) calculations only as prefilter for subsequent experiments. We have tried several approaches to arrive at good suggestions for subsequent experiments:

(3)

with Z′(ω) being the electrical impedance and Z′(ω) and Z″(ω) being the real and the imaginary part of the impedance, respectively. Galvanostatic charge−discharge cycling was carried out using current densities ranging from 0.66 to 6.6 A·g−1. The values of capacitance of the total active material (C), equivalent series resistance (ESR), and Coulombic efficiency (η) have been calculated as indicated in ref 54. All equations were created via MathType 6.7.

3. RESULTS AND DISCUSSION 3.1. Computational Screening. Electrochemical stabilities (IPs/EAs), melting/flash/boiling points, viscosities (of pure solvents at room temperature and ambient pressure), and ion solubilities/free energies of solvation/chemical potentials for Li+ and PF6− ions (in the solvents) are available for all 4879 candidate compounds in the Supporting Information and will be made publicly available on our project web page.55 For screening compounds we focus on ranking candidates relative to each other, also because such rankings are usually more accurate than the actual property estimates. Figure 1 shows a schematic representation of the screening strategy, which will be described in detail below. Our screening strategy does not aim at good agreement with experiments for individual data points, but only for the correct final ranking of compounds. We therefore put much emphasis on how reasonable the final outcome of the screening strategy is, and do not worry about (especially systematic) errors, as long as the final top-list is not influenced. We nevertheless did systematic studies in comparison to experimental data in two earlier publications on the topic: One benchmarking method for electrochemical stability windows,33 the other investigating the performance of COSMOtherm for predicting collective properties of electrolyte solvents34 (see below for details). Accordingly, properties (apart from the chemical potentials of the ions in the solvents) are not computed for mixtures but pure compounds, which circumvents problems of COSMOtherm for the description of the former. The computed values are therefore estimators for the properties of the actual electrolyte mixtures, i.e., we implicitly assume that trends for pure compounds hold for mixtures up to a certain extend. As mentioned above,34 we evaluated property prediction (including flash and boiling points) with COSMOtherm for common D


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The Journal of Physical Chemistry C • Strategy 1: Pick electrochemically most stable compounds based on (lower-level) SQM data. • Strategy 2: Filter compounds according to physically reasonable threshold values (see below), then pick electrochemically most stable candidates based on SQM data. • Strategy 3: Filter compounds with thresholds, then pick pareto-optimal candidates (i.e., compounds for which no other compound exist that is equal or better with respect to all properties) based on SQM data. • Strategy 4: Pick most stable compounds based on DFT data, i.e. same strategy as 1, but now based on higherlevel DFT, not SQM, data. • Strategy 5: Filter compounds with thresholds, then pick most stable candidates based on DFT data, i.e. same strategy as 2, but now based on higher-level DFT, not SQM, data. • Strategy 6: Filter compounds with thresholds, then pick pareto-optimal candidates based on DFT data, i.e. same strategy as 3, but now based on higher-level DFT, not SQM, data. • Strategy 7: Filter compounds with minimal thresholds, then pick pareto-optimal candidates based on DFT data (final strategy, results in Table 1). Standard thresholds were as follows: Electrochemical stability (redox potentials) equal to or above average, viscosity (of the pure solvent) less or equal to 3 mPa·s, boiling point equal to or above 100 °C, flash point equal to or above 50 °C, solubilities/ chemical potentials for Li+/PF6− equal to or above average. Minimal thresholds corresponded to no boiling/flash point cutoff and a viscosity cutoff of 4.5 mPa·s. These thresholds are motivated by the following considerations: There is no exactly defined requirement for the electrochemical stability and the ion solubilities, but it is clear that they should be above average, and omitting all compounds with the relevant properties below average is thus a reasonable prefiltering step. Pure standard electrolyte solvents usually have viscosities below 2 mPa·s, but some promising candidates like ADN show higher values. For safety, prefiltering cutoffs should be somewhat larger than the targeted values, which is why we choose 3 mPa·s as standard threshold and 4.5 mPa·s if highviscosity alternatives are also of interest. Analogous considerations were made for boiling and flash points: We aim at room temperature-liquid electrolytes with flash points of 60 °C (the standard for technological applications) and have therefore used 100 and 50 °C (with 10 °C screening safety margin) as standard threshold boiling and flash point cutoffs. The exact values of the cutoffs do not matter too much, as they are only used for prefiltering purposes and are chosen with safety margins to not exclude candidates too quickly. We investigated the influence of applying different threshold values and found only the viscosity cutoff to be essential. Accordingly, for our final strategy no cutoff values apart from the viscosity threshold of 4.5 mPa·s was applied. Picking most stable compounds without filtering (strategies 1 and 4) leads to unreasonable suggestions at both SQM and DFT level, as the most stable compounds like NC−CN and C(CN)4 have very low flash-points and unfavorable ion solubility properties. Prefilter compounds to pick most stable candidates (strategies 2 and 5) removes these cases (which was our initial motivation to extend our screening approach beyond electrochemical stabilities), but still leads to compounds with

rather unfavorable properties apart from the electrochemical stability. Only when picking pareto-optimal candidates after filtering (strategies 3 and 6), all suggestions look reasonable. When applying thresholds, we found it important to check the computed estimates of known reference compounds, as setting physically reasonable thresholds might lead to unfavorable results if properties are systematically over- or underestimated at a certain theory level. Our first set of thresholds included viscosity ≤3.0 mPa·s and flash-point ≥50 °C, thereby (correctly) excluding ADN (higher viscosity with DFT) and ACN (lower flash-point with both DFT and SQM). Our final analysis (strategy 7) therefore made use of a “minimal” threshhold set with viscosity ≤4.5 mPa·s and no boiling-/flashpoint cutoff. The screening results are overall very consistent, especially between the SQM and DFT variants, though systematic shifts are found (e.g., lower viscosity estimates with SQM than DFT). Both SQM (strategy 3) and DFT (strategy 6) suggest glutaronitrile (GTN), 2-methyl-GTN (2MGN) and 2,2dimethylsuccinonitrile (DSN) and both identify some dicyano-cyclopentanes as interesting (but probably not liquid) candidates. If no flash-point threshhold is applied, ACN is found at the very top of the list; if the higher viscosity threshold is applied, succinonitrile (SCN) and ADN enter the list. The final outcome of strategy 7 can be found in Table 1. Perusing this table one can conclude the following: As long as thermal security is not an issue, ACN cannot be beaten. ADN is high up in the list, but some SCNs and GTNs might show some benefits over ADN, with DSN and GTN as the most promising ones. SCN is not a liquid at room temperature, therefore we decided to turn to GTN in the following, as a comparably lowviscous, well-solvating material. Also 2MGN seems interesting as step in between GTN and ADN. A very interesting suggestion, but unfortunately not directly available are the dicyano-cyclopentane compounds. For our three test compounds, ADN, GTN, and 2MGN, we additionally computed electrochemical stabilities of the solvent/BF4− complex and Pka values in ACN, given in Table 2. In agreement with previous reports in the literature,57,58 we again find similarly high (electro)chemical stabilities for all three compounds. Table 2. IP Values at B3LYP-D/TZVP Level in eV for Solvents Solvent/Anion Complexes and pka Values for the Solvent Compound in ACN GTN 2MGN ADN

solvent

solvent/anion complex

pka (in ACN)

8.78 8.57 8.47

8.68 8.48 8.30

47 46 48

3.2. Physical and Electrochemical Characterization. Out of the investigated solvents in section 3.1, GTN, 2MGN and ADN were chosen for more in depth characterization. Table 3 reports the chemical structures, purity, water content as well as the flash point (both experimental and computational) of the three investigated nitriles. Electrolytes with these solvents were prepared by adding Et4NBF4 to the solvents until the maximum solubility was reached. As shown in the Table, the final concentration of Et4NBF4 was 1 mol·dm−3 for GTN, 0.5 mol·dm−3 for 2MGN and 0.7 mol·dm−3 for ADN. For the realization of high performance EDLCs, the use of electrolytes with low viscosity and high conductivity is an E


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The Journal of Physical Chemistry C Table 3. Structure, Flash Point, and Maximum Solubility of the Investigated Solvents

Key: (a) As indicated by the manufacturer. The solvents were used as received. (b) After drying over molsieve (3 Å).

Figure 2. (a) Conductivity and (b) viscosity of the investigated electrolytes in the temperature range of −30 to +80 °C with the corresponding VTF plots (c, d).

aspect of particular importance. Considering this point, the conductivity and viscosity of the electrolytes in the temperature range between −30 and +80 °C was investigated (Figure 2). For the ADN-based electrolyte, the electrolyte was solid for temperatures below 0 °C, thus no conductivity or viscosity was determined for these temperatures. With increasing temperature the conductivity increases and the viscosity decreases for all electrolytes. For all temperatures, GTN has the highest conductivity and 2MGN the lowest. The strong fade in

conductivity for ADN may be an effect of being close to the freezing point. At 20 °C, GTN has a conductivity value of 6.1 mS·cm−1, almost three-times the value of 2MGN (2.3 mS· cm−1) and almost two-times the value of ADN (3.5 mS·cm−1). However, compared to state-of-the-art electrolytes, like 1 mol· dm−3 Et4NBF4 in ACN or 1 mol·dm−3 Et4NBF4 in PC, the conductivity is considerable lower (55 or 13 mS·cm−1, respectively).18,59 The viscosity is the highest for ADN and the lowest for GTN, although an important difference is only F


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The Journal of Physical Chemistry C Table 4. Fitting Parameters of the VTF Fitting for Conductivity and Viscosity

a

sample

T0,σ [K]

σ0 [ms·cm−1]

Bσ [K]

R2 a

T0,η [K]

η0 [mPa·s]

Bη [K]

R2 a

GTN 2MGN ADN

174.0 163.0 181.0b

317.4 359.3 204.2b

488.1 661.9 457.4b

0.996 47 0.998 60 0.99977b

160.5 176.0 196.5

0.121 0.151 0.279

559.9 477.8 341.6

0.998 98 0.999 63 0.999 85

Correlation coefficient. bExcluding the data point close to the melting point.

seen for lower temperatures. For temperatures above 30 °C the difference in viscosity is almost negligible. At 20 °C GTN has a viscosity of 8.0 mPa·s, quite close to the value of 2MGN (8.9 mPa·s) and ADN (9.5 mPa·s). Again, compared to the electrolytes based on ACN (0.6 mPa·s) or PC (2.6 mPa·s), these values seem rather unfavorable.18,59 As seen in Figure 2 c,d, the temperature dependence of both conductivity and viscosity could be well described by the Vogel−Tammann− Fulcher (VTF) model. The fitting parameters are reported in Table 4. Figure 3 compares the electrochemical stability windows (ESWs) of the investigated electrolytes. The anodic stability of

Figure 3. Electrochemical stability windows of the considered electrolytes.

all electrolytes is 3.70 V vs Ag. Since it is the same for all electrolytes, it appears that the anodic stability is not affected by the solvent and only dependent on the stability of the conductive salt Et4NBF4. The cathodic stability is slightly different for the electrolytes with −1.65 V vs Ag for GTN, −1.75 vs Ag for 2MGN, and −1.85 V vs Ag for ADN, which indicates that the cathodic stability might be dependent, or is at least influenced, by the solvent. After the determination of the ESW, the operative voltages of the electrolytes were determined via conducting 3-electrode setup CV measurements. Figure 4 presents the obtained CV curves as well as the obtained efficiencies. The positive and negative potential limits were defined by the highest and lowest potential limits, at which the efficiency of the charge−discharge was higher than 99%. As shown in Figure 3, the ESW (as evaluated on Pt electrode) of the three electrolytes did not appear significantly different. Nevertheless, as illustrated in Figure 4, when these electrolytes were used in combination with AC-based electrodes, a remarkable difference in term of different potential limits was found. For example, ADN reaches a negative potential limit of −1.85 V vs Ag, 2MGN a limit of −1.90 V vs Ag, while the one of GTN is only −1.45 V vs Ag.

Figure 4. Specific capacitance and Coulombic efficiency of a carbon electrode obtained from CV at 5 mV s−1 for (a) 1 mol·dm−3 Et4NBF4 in GTN, (b) 0.5 mol·dm−3 Et4NBF4 in 2MGN, and (c) 0.7 mol·dm−3 Et4NBF4 in ADN. The horizontal line represents a 99% threshold; the vertical lines represent the determined maximum operative potential.

On the positive side 2MGN is only stable up to +1.05 V vs Ag, GTN up to +1.25 V vs Ag and ADN reached up to +1.85 V vs Ag. As a consequence the maximum operative voltage evaluated G


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Figure 5. Cyclic voltammetry of (a) 1 mol·dm−3 Et4NBF4 in GTN, (b) 0.5 mol·dm−3 Et4NBF4 in 2MGN, and (c) 0.7 mol·dm−3 Et4NBF4 in ADN. (d) Capacitance retention of EDLCs containing the investigated electrolytes.

atomic scale, so that it seems questionable whether including such techniques into computational screening strategies will improve predictions of operative voltage data. After the operative voltages of the different electrolytes were obtained, full cells with the three electrolytes were assembled. Because of the different voltage excursion on the positive and negative side of the electrolytes (especially 2MGN), the mass of the electrodes was balanced in order to be able to safely utilize the whole operative voltage.52 Figure 5 a-c show the CV obtained for these full cells. As visible, no faradaic reaction is observed in none of the investigated electrolytes. The highest capacitance is obtained by ADN (24 F·g−1 at 20 mV·s−1), followed by GTN (20 F·g−1) and 2MGN, with 2MGN showing the lowest capacitance (17 F· g−1). As shown in Figure 4, the AC electrode cycled toward positive voltage displays much higher capacitance in ADN than in the other two nitriles. This higher capacitance, which might be related to the voltage excursion of the electrode as well as to the ion−solvent interaction and salt concentration of the electrolytes, is most likely responsible for the highest capacitance delivered by the ADN-based EDLCs. The capacitance retention (Figure 5 d), however, is the highest for GTN (69.0% at 200 mV·s−1) and the lowest for ADN (56.1%) with 2MGN being very close to ADN (59.5%). The viscosity at the temperature, at which the cells were cycled (20 °C), has the same trend: 8.0 mPa·s (GTN), 8.9 mPa·s (GTN) and 9.5 mPa·s (ADN). Thus, the capacitance retention is in line with the trend of the viscosity.

for each EDLC was very different: 2.70 V for GTN, 2.95 V for 2MGN, and 3.70 V ADN. While the operative voltages of the first two electrolytes are in line with other electrolytes based on organic solvents,19 the operative voltage of ADN is, as already reported in the literature, significantly larger, and it is comparable to that of electrolytes based on ILs.19 It is important to remark that, since the salt is the same for all electrolytes, the different operative voltage observed for the three investigated nitrile-based electrolytes cannot be correlated exclusively to this component. Other aspects are obviously playing an important role on the determination of the operative voltage of these devices. Among them, the electrochemical stability of the solvents, the interaction between solvent and salt in these electrolytic solutions as well as the interaction between the investigated electrolytes and the carbonaceous electrodes appear extremely important. The study of these latter aspects is out of the scope of this investigation and, therefore, it will not be considered further. Here we would like to mention that the different operative voltage observed above represent a very good example of the complex interactions existing between solvent−salt and carbon in EDLCs. As these interactions are not easily manageable with computational methods, the determination of the operative voltage should be considered as one of the most critical aspects related of the validation of computational screening. It is important to keep in mind that even the most advanced computational approaches like ab initio molecular dynamics simulations rely on rather crude assumptions about the structure of electrode materials at the H


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it has the lowest ESR values compared to the other two electrolytes. At a current density of 1.32 A·g−1 these electrolytes deliver a specific capacitance of 19, 18, and 15 F·g−1 for ADN, GTN, and 2MGN, respectively. For a current density of 6.6 A· g−1 these values decrease to 8, 11, and 4 F·g−1. The reason for GTN being able to retain more specific capacitance at higher rates compared to the others is related to the observed lower ESR and, therefore, higher conductivity and lower viscosity. If we compare GTN to 2MGN directly, one can see that GTN has always a higher specific capacitance. Since both the conductive salt and the used active material are the same, this difference has to be related to the electrolyte properties. Most likely, the difference in conductivity, and therefore salt concentration, and viscosity is the reason for the better performance of GTN. Applying the same argumentation for comparing GTN to ADN, one would suspect that GTN should have higher specific capacitance values than ADN, since the conductivity and viscosity of ADN is more similar to 2MGN. However, for the reasons mentioned above at low rates ADN has higher values for specific capacitance than GTN. Taking into account the results reported above, all three investigated nitrile-based electrolytic solutions appear suitable for use as electrolytes in EDLCs. Clearly, these electrolytes are not outperforming conventional ones, but also computational screening predicts ACN as a better candidate. Nevertheless, the results of this investigation indicate that the proposed screening method represents a valid approach for the identification of new electrolyte components, in this case solvents, for EDLCs. Impedance spectroscopy was carried out for EDLCs containing the three electrolytes (Figure 7). In the Nyquist plot (Figure 7 a), information regarding the ESR can be drawn from the intersection of the graphs with the real part axis, since for higher frequencies EDLCs behave like a resistance. For the three electrolytes based on GTN, 2MGN and ADN a values of 11.4, 19.2, and 17.1 Ω·cm2, respectively, are reached, following a comparable trend and reaching similar values as for the galvanostatic cycling. At low frequency, the capacitance can be calculated via eq 1 resulting in 15.7 F·g−1 for GTN, 12.0 F·g−1 for 2MGN, and 15.4 F·g−1 for ADN. Figure 7 b shows the evolution of the imaginary capacitance vs the frequency. The imaginary part of the complex capacitance has a maximum at the frequency ν0, which defines a time constant τ0 = (1/ν0),53 which marks the frontier between resistive and capacitive behavior. With the obtained results for ESR and conductivity

To further investigate the performance of the electrolytes, galvanostatic cycling experiments were performed using current densities ranging from 0.66 to 6.6 A·g−1 (Figure 6). The

Figure 6. Evolution of specific capacitance, ESR and Coulombic efficiency of EDLCs containing 1 mol·dm−3 Et4NBF4 in GTN, 0.5 mol·dm−3 Et4NBF4 in 2MGN and 0.7 mol·dm−3 Et4NBF4 in ADN as electrolyte.

maximum operative voltage for this test was the same as reported above. For all electrolytes the Coulombic efficiency is stable for all cycles and all applied current densities, reaching almost 100%. The ESR is the lowest for the GTN based electrolyte with a value around 12 Ω·cm2, stable for all cycles and current densities. 2MGN and ADN have an ESR value of around 20 Ω·cm2, with ADN starting at 19.8 Ω·cm2 and increasing over cycling to 22.2 Ω·cm2. Because of the fact that GTN has the highest conductivity (6.1 mS·cm−1) and lowest viscosity (8.0 mPa·s) of all electrolytes, it is not surprising that

Figure 7. (a) Nyquist plot of two electrode EDLCs using the considered electrolytes for a frequency range from 500 kHz to 10 mHz. (b) Evolution of the imaginary part of the complex capacitance vs frequency of the same EDLCs. I



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CONCLUSION In this manuscript, we investigated the possibility to use a computational screening for the identification of new solvents for EDLCs. In the first part of this work, we presented the results of a computational screening run: Using the chemical space of all reasonable nitriles up to 12 heavy atoms as a toy system, we investigated the ability of our recently proposed screening approach to make useful suggestions for subsequent experimental work. We find that if candidate compounds are prefiltered with reasonable thresh-hold values and paretooptimal candidates are picked afterward, excellent results are achieved. This way, ACN, ADN, and GTN- and SCN-based and probably also dicyanocyclopentane compounds are identified as the most promising nitrile solvents for electrochemical applications. On the basis of the results of the screening, the most interesting, readily available solvents (apart from the also predicted, but obvious case of ACN) were investigated regarding their physical (conductivity, viscosity) and electrochemical (electrochemical stability, operative voltage, specific capacitance) properties. Two of the investigated electrolytes (1 mol·dm−3 Et4NBF4 in GTN and 0.5 mol·dm−3 Et4NBF4 in 2MGN) have similar properties to other organic electrolytes in terms of capacitance and operative voltage, however their transport properties are worse to state-of-the-art electrolytes. The third electrolyte (0.7 mol·dm−3 in ADN), while showing similar transport properties to the other electrolytes, has a much higher operative voltage of 3.7 V. At this high operative voltage, this electrolyte has reasonable values for specific capacitance and a good Coulombic efficiency, further displaying the good properties of this electrolyte. Comparing theoretical predictions with experimental findings, the following conclusions can be drawn: Computational screening was able to pick compounds with reasonable melting/flash/boiling points, trends for solubilities and viscosities are correctly predicted, as well as electrochemical stabilities of the pure solvents. The most problematic property is the operative voltage, as it depends in a nontrivial way on the interaction of all electrolyte components with themselves as well as the electrodes.

for these electrolytes, one would expect that GTN has the lowest time constant and 2MGN the highest. The actual values for the time constant are 14 s for GTN, 18 s for ADN, and 22 s for 2MGN, thus matching the expected trend. Finally, the average energy and power were calculated for the devices (Figure 8). The mass used for the calculation refers to

Figure 8. Ragone plot of EDLCs containing 1 mol·dm−3 Et4NBF4 in GTN, 0.5 mol·dm−3 Et4NBF4 in 2MGN, and 0.7 mol·dm−3 Et4NBF4 in ADN as electrolyte. Both average energy and power are referred to the total mass of active materials of both electrodes.

the sum of the active mass of both positive and negative electrodes. At 1.32 A·g−1 the device based on ADN has an average energy of 24.6 Wh·kg−1 and an average power of 2.0 kW·kg−1. The values for GTN (13.2 Wh·kg−1/1.5 kW·kg−1) and 2MGN (10.0 Wh·kg−1/1.6 kW·kg−1) are both lower and thus matching the trend of the observed specific capacitance values at this current density. However, keeping in mind that the operative voltages are also quite different (3.7 V for ADN compared to 2.7 V/2.9 V for the other two), it is not surprising that ADN pulls ahead. At the highest current density of 6.6 A· g−1 we see a decrease for both energy and power for ADN and 2MGN, where we would normally expect a decrease in energy and increase in power. The energy loss, however, is quite pronounced (−94% for ADN, −96% for GTN), so it is probably safe to assume that these systems cannot handle this current density, which may also be indicated by the strong capacitance decrease seen in the galvanostatic cycling. The inability to handle this current density may be attributed to the high ESR of both ADN and 2MGN. If we consider a scale factor of 4 for the transfer of lab scale devices to real devices,60 the average energy and power value of the ADN-based device can be estimated to 6.1 Wh·kg−1 and 0.5 kW·kg−1. Although the power density is lower for our device, the average energy density is higher than those of commercially available EDLCs based on PC or acetonitrile.61

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ASSOCIATED CONTENT

S Supporting Information *

SQM and DFT data sets. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcc.5b02113.

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AUTHOR INFORMATION

Corresponding Authors

*(A.B.) E-mail: [email protected]. *(M.K.) E-mail: [email protected]. Present Address §

Helmholtz Institute Ulm, Karlsruhe Institute of Technology, Helmholtzstraße 11, 89081 Ulm, Germany Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS A.B. and C.S. would like to thank the Bundesministerium für Bildung and Forschung (BMBF) within the Project IES (Contract Number 03EK3010) for financial support. M.K. J


Article

The Journal of Physical Chemistry C gratefully acknowledges financial support from the Barbara Mez-Starck Foundation. We gratefully appreciated the supply of materials by Norit Activated Carbon Holding (DLC Super 30) and Imerys (Super C65).

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