Effects of Functional Groups in Redox-Active Organic Molecules: A

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The Effects of Functional Groups in Redox-Active Organic Molecules: A High-Throughput Screening Approach Kenley M. Pelzer, Lei Cheng, and Larry A Curtiss J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.6b11473 • Publication Date (Web): 08 Dec 2016 Downloaded from http://pubs.acs.org on December 9, 2016

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The Journal of Physical Chemistry

The Effects of Functional Groups in Redox-Active Organic Molecules: A High-Throughput Screening Approach Kenley M. Pelzer1*, Lei Cheng1,2, Larry A. Curtiss1,2 1. Materials Science Division, Argonne National Laboratory, 9700 Cass Ave., Lemont, IL 60439 2. Joint Center for Energy Storage Research, Argonne National Laboratory, 9700 Cass Ave, Lemont, IL 60439 *E-mail: [email protected]. Phone: 630-252-7020. Fax: 630-252-9555

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Abstract Non-aqueous redox flow batteries have attracted recent attention with their potential for high electrochemical storage capacity, with organic electrolytes serving as solvents with a wide electrochemical stability window. Organic molecules can also serve as electroactive species, where molecules with low reduction potentials or high oxidation potentials can provide substantial chemical energy. To identify promising electrolytes in a vast chemical space, high-throughput screening (HTS) of candidate molecules plays an important role, where HTS is used to calculate properties of thousands of molecules and identify a few organic molecules worthy of further attention in battery research. Here, we present reduction and oxidation potentials obtained from HTS of 4178 molecules. The molecules are composed of base groups of five or six-membered rings with one or two functional groups attached, with the set of possible functional groups including both electron-withdrawing and electron-donating groups. In addition to observing the trends in potentials that result from differences in organic base groups and functional groups, we analyze the effects of molecular characteristics such as multiple bonds, Hammett parameters, and functional group position. This work provides useful guidance in determining how the identities of the base groups and functional groups are correlated with desirable reduction and oxidation potentials.


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Introduction Non-aqueous redox flow batteries (NRFBs) have recently attracted attention as a promising alternative to aqueous flow batteries, which suffer from the unwanted electrolysis of water placing limitations on the reduction and oxidation potentials of electroactive species.1 With NRFBs, a wider reduction/oxidation potential window of the solvent brings with it a wider chemical space of molecules that may serve as electroactive species. Organic molecules also show great promise as electroactive species in flow batteries, with a low reduction potential (high oxidation potential) suggesting that molecules may serve as anolytes (catholytes) with high chemical energies. However, identification of the most promising organic electrolytes presents a daunting problem, with the chemical space of candidate molecules far too vast to be explored with a trial-and-error approach. High-throughput screening (HTS) offers a method of testing thousands of molecules for desirable properties without the need for experimental trial and error. HTS methods begin with an exploration of chemical space, using variations such as the substitution of functional groups to create sets of thousands of candidate molecules. The molecules then pass through a series of theoretical calculations. After each set of calculations, unpromising molecules are discarded and the set of candidates grows smaller, until finally a small set of molecules is selected. With the guidance of HTS, experimental work can focus on the synthesis and testing of only these few most promising candidates. HTS has recently been applied as a strategy for designing molecules for applications ranging from flow batteries to photovoltaics to lightemitting diodes.2-17 The Electrolyte Genome (EG) project4,11 was recently developed by the Joint Center for Energy Storage Research (JCESR)18-19 as an HTS approach to identifying promising organic electrolytes for use in NRFBs. Using density functional theory (DFT) electronic structure calculations, the Electrolyte Genome algorithm computes various properties of candidate molecules to assess their usefulness as battery electrolytes. The reduction and oxidation potentials, solvation energies, and structural changes (upon ionization) of candidate molecules are computed. Here we focus on reduction and oxidation potentials (ROPs), analyzing ROP data for a set of 4178 organic molecules studied with the EG algorithm. The sample of 4178 molecules is comprised of candidates with a five- or six-membered ring combined with one or two functional groups. Both saturated and unsaturated base groups are considered. These combinations were randomly generated from a set of 23 possible base groups (Figure 1a) and 16 possible functional groups (Figure 1b), where the base groups may include N, O, or S atoms and the functional groups may have electron-donating or electron-withdrawing properties. In this work, we aim to use this data to explore correlations between various properties of molecules and the ROPs that determine the usefulness of an electrolyte in energy storage systems. In addition to the question of how base/functional group identities influence ROPs, we test variations in ROPs as a function of various characteristics of the functional groups: Hammett parameters, electronegativities, the presence of multiple bonds, and relative position of functional groups. The calculations are applied to monomers for the purpose of minimizing computational cost (and complexity, where long polymers would require us to consider the conformational freedom of the polymers and the vast array of possible geometries). However, in studying the properties of monomers we can explore trends in electrical properties that are likely to be relevant to polymers containing the same base unit. This high-throughput approach to studying such trends provides insight into which functional groups are most worthy of attention in the electrochemistry laboratory.

Methods

The EG algorithm is described in detail elsewhere4,11; here we provide a brief summary of the features that are relevant to the data presented here. The process begins with the creation of several thousand candidate molecules, which have chemical structures defined by an automated molecule generator. In the present work, the generator worked with a set of 23 possible base groups (shown in Figure 1a) and 16 possible functional groups (shown in Figure 1b). Each chemical structure proposed by the generator contains either one or two functional groups placed at random points around the ring (without creating duplicate structures).


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We note that in assessing practical applications of electrolytes, another useful step in high-throughput screening is an automated check for major geometric changes upon oxidation or reduction. Although such geometric changes do not necessarily imply that a calculation was inaccurate, they do indicate that that molecule would be a poor choice of electrolyte for most practical purposes due to the risk of decomposition. This automated check for geometric changes was not performed for this dataset, and thus we expect that the data will contain molecules that significantly rearrange and even decompose upon changes in electronic state. In particular we have noted that molecules containing the CCl3 functional group are prone to instability upon oxidation and reduction. Although a few molecules were cut from the dataset when we performed a case-by-case inspection of results for cases with implausibly high oxidation potentials, this inspection of a few suspicious cases is not at all equivalent to a rigorous assessment of geometric stability for all molecules in the dataset. Thus, although the present dataset is appropriate for the purposes of examining trends in ROPs, an additional step of screening for geometric changes would provide additional information. Another useful step for further work would be the inclusion of frequency calculations to ensure that optimized geometries represent true energetic minima. In this work, for the sake of minimizing the computational cost of the high-throughput screening, such frequency calculations were not performed. Reduction and oxidation potentials are frequently obtained from computational methods, with previous work applying techniques such as MP2, Hartree-Fock, URCCSD(T), ROMP2, and DFT.20-26 In this work, the structures were optimized in the gas phase using DFT calculations with the B3LYP functional27-30 and 6-31+G(d) basis set. Following optimization, single-point calculations were performed using the IEFPCM implicit solvent model31 with water as the solvent to compute the solvation free energy. (Although non-aqueous flow batteries are of particular interest in this work, we note that previous EG work found the order of the ROPs of different molecules to be invariant with changes in the solvent dielectric constant.11) All DFT calculations were performed with the Gaussian 09 software.32 Optimizations were performed for the neutral, cationic, and anionic states of each molecule. The adiabatic reduction and oxidation potentials were then computed from the potentials of the final optimized and solvated structures, as described in reference11: 𝐸!,!"# = − 𝐸!,!" = −

!"# ∆!!"# (!)

!" !"# ! ∆!!"

!"

− 1.24 𝑉

(1)

− 1.24 𝑉

(2)

!"# !"# where ∆𝐺!"# (𝑀) and ∆𝐺!" 𝑀 (the free energies of reduction and oxidation) are computed as: !"# ∆𝐺!"# 𝑀 = ℰ!"#$%&' − ℰ!"#$"

(3)

!"# ∆𝐺!" 𝑀 = ℰ!"#$%& − ℰ!"#$%&'

(4)

where we approximate G with the energy ℰ!"#$%&'/!"#$"/!"#$%& obtained from the single-point DFT calculation. n is the number of electrons involved in the redox reaction and F is the Faraday constant. The offset of -1.24 V represents the difference between the standard hydrogen electrode (SHE, −4.28 V) and Li/Li+ redox couple (−3.04 V). Here we present this data for a set of 4178 molecules. In analyzing our results we will most frequently refer to ∆𝐸!,!"# and ∆𝐸!,!" . These refer to the difference between 𝐸!,!"#/!" of the candidate molecule and 𝐸!,!"#/!" of the bare base group, where a positive ∆𝐸!,!"! /𝑜𝑥 shows an increase in the potential upon adding functional groups. This can be conceptualized as the effect of a particular functional group(s) on ROPs, which is the key information that we seek in this work. We will also often work with sub-groups of the sample of 4178 molecules according to characteristics of the functional groups (such as molecules with two electron-withdrawing


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groups versus two electron-donating groups). Such separation helps us to disentangle the many factors influencing the ROPs.

Results and Discussion Base group effects We first assess the trends in ROPs for the different base groups, which are shown in Figure 2. In this figure a data point for the reduction/oxidation potential of a single candidate molecule is denoted with a + sign. Each column of + signs shows the sample of candidate molecules containing that base group, with the average potential (in V) noted by the number at the bottom of each column and marked in each column with a horizontal blue line. For comparison, the potential of the bare base group is noted with a horizontal red line. The plot is divided by the vertical red line into unsaturated base groups (left) and saturated base groups (right). The names of the base groups (which are imaged in Figure 1a) are noted on the x-axis. For the reduction potentials shown in Figure 2a, the different base groups show fairly similar ranges, although pyrazine and 1,3,5-trithiane show somewhat smaller ranges and higher average potentials. It is not obvious a priori why these two base groups would show higher potentials and less fluctuation with varying functional groups. Taking the mean value of the reduction potential for the unsaturated base groups (the leftmost 12 columns) and the saturated base groups (the rightmost 11 columns) we find a somewhat greater average reduction potential for unsaturated base groups. While the unsaturated groups show 𝐸!,!"# = 0.90 V, for the saturated groups we see 𝐸!,!"# = 0.53 V, showing that reduction is more energetically favorable for unsaturated base groups. For the oxidation potentials in Figure 2b, we see more variation according to base group. Again taking the mean value of the potential for the subsets of unsaturated versus saturated base groups, 𝐸!,!" = 4.58 V for the unsaturated groups and 𝐸!,!" = 5.37 V for the saturated groups, showing that oxidation is more energetically favorable for the unsaturated base groups. The fact that both reduction and oxidation are more favorable with unsaturated base groups is likely due to the fact that it is easier to remove or add an electron when rings contain π-bonding.. Figure 3 shows ∆𝐸!,!"# and ∆𝐸!,!" , the differences between the potential of the functionalized molecule and the potential of the bare base group shown in Figure 1a. At the bottom of each column we note the mean value of ∆𝐸!,!"#/!" for all molecules with that base group, and show this mean value with the horizontal blue line in each column. In 3a, we see mostly positive numbers, showing that the addition of functional groups leads to a higher reduction potential. We again see variation between unsaturated and saturated base groups. For unsaturated cases, ∆𝐸!,!"# = 1.08 V, while ∆𝐸!,!"# = 1.46 V for saturated cases. Turning to 3b, we see more of a mix of positive and negative values of ∆𝐸!,!" . We again see variation between the unsaturated and saturated cases, with ∆𝐸!,!" = -0.09 V for unsaturated cases and ∆𝐸!,!" = 0.27 V for saturated cases. We note that we see the opposite trend in Figure 2b, in which the unsaturated groups tend to have lower oxidation potentials than their saturated counterparts. This highlights the fact that absolute potentials 𝐸!,!"#/!" provide different information compared to the ∆𝐸!,!"#/!" data that we will show in the following plots. With ∆𝐸!,!"# and ∆𝐸!,!" , we seek to understand the sensitivity of different base groups to functional group addition as well as the power of different functional groups to affect potentials. The latter point is especially important for applications, where functional group substitution is a common tool used in the pursuit of improved electrolytes. In addition to variations in ∆𝐸!,!"# and ∆𝐸!,!" between saturated and unsaturated base groups, visual inspection also reveals some differences in the range of ∆𝐸!,!" values between different base groups. Although the range of values seems to be about the same between saturated and unsaturated base groups for ∆𝐸!,!"# , for ∆𝐸!,!" we see especially small ranges for the saturated base groups tetrahydrothiophene, tetrahydro-2H-thiopyran, and 1,3,5-trithiane. Such small ranges suggest a lesser sensitivity to the identity of the functional group.


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Although we see differences corresponding to the unsaturated or saturated nature of the rings, we do not see any clear trends with respect to the presence of heteroatoms. This suggests that any effect of the simple presence or absence of heteroatoms is by far dominated by factors such as the saturated or unsaturated nature of the ring and the position of the heteroatoms. Interestingly, the presence of a single nitrogen or oxygen heteroatom (indicating greater polarity of the base group) does not appear to have a significant effect on ROPs according to our data in Figures 2 and 3. It seems unlikely that polarity is an insignificant molecular property in determining ROPs. Rather, the insignificance of base group polarity that we see here suggests that the functional groups have a more dominant effect than ring heteroatoms in determining molecular polarity. The polarity induced by functional groups is discussed below in our analysis of the meta/ortho/para positions of functional groups.

Functional group effects a. Identity of functional group The impact of functional groups on ∆𝐸!,!"# and ∆𝐸!,!" is demonstrated in Figure 4. The format of these plots is equivalent to that shown in Figures 2 and 3, except that here we separate the data by functional group rather than base group. Functional groups to the left of the red vertical line are electronwithdrawing; functional groups to the right are electron-donating. The range of data points in each column illustrates the fact that different base groups are influenced in different ways by the addition of the same functional group. This is not surprising given the variation in polarity, saturation, and inclusion of heteroatoms among our set of possible base groups. Unlike Figures 2 and 3, which show the full sample of 4178 molecules, Figure 4 shows only molecules with a single functional group. This is a far smaller sample with only 199 molecules, limiting the significance of our conclusions. However, molecules containing two functional groups present a far more complicated problem from which it is difficult to extract any trustworthy information on the effects of a particular functional group per se. In both panels of Figure 4 we see the expected effects of electron-withdrawing/donating functional groups. Although we see substantial variation in potentials for many of the functional groups, it is obvious that molecules with electron-withdrawing groups generally have a more positive change in reduction and oxidation potential than molecules with electron-donating groups. This trend, which reflects a more stable anion and less stable cation in the presence of electron-withdrawing groups (and the inverse trend with electron-donating groups) is well known. In addition, some insight can be gained into the magnitude of these effects. The range of ∆𝐸!,!"# and ∆𝐸!,!" values in Figure 4 demonstrates a fundamental feature of the effects of functional groups on ROPs: Adding functional groups almost always helps reduction, but adding functional groups may help or hurt oxidation. In Figure 4a we see a positive ∆𝐸!,!"# for 13 out of 16 functional groups with a range from -0.3 to 3.2 V, where a positive ∆𝐸!,!"# reflects a stabilization of the anion upon adding functional groups. However, in 4b we see an even split between positive and negative ∆𝐸!,!" and a range from -1.1 to 0.6 V, with negative values of ∆𝐸!,!" reflecting a stabilization of the cation. The benefit of adding functional groups in reduction may simply be due to the fact that the functional groups provide more atoms over which the excess charge can delocalize. Of course, we see these effects more strongly when the functional groups are electron-withdrawing (the six leftmost columns). This distinction between electron-withdrawing and electron-donating groups is most apparent in Figure 4b. When functional groups are electron-donating, the excess electron density pushed into the ring by the low-electronegativity functional group provides an area of greater density in the ring from which an electron can easily be removed. This leads to more energetically favorable oxidation and lower oxidation potentials, as we see in the rightmost columns of Figure 4b. However, with electronwithdrawing groups, the molecule forms an electron-poor ring from which electrons cannot be easily removed (while the functional groups, by definition of being more electronegative, do not like to lose electrons).


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b. Functional group electronegativity Given the fundamental differences in the effects of electron-withdrawing and electron-donating functional groups, it would be useful to go beyond the rough binary classification of “electron-withdrawing” or “electron-donating” and quantify the electronegativity of each functional group. To estimate the electronegativity of a functional group (rather than a single atom) is a non-trivial task that would require its own set of quantum mechanical calculations. However, we argue that with the present data, electronegativity can be roughly estimated by summing the Pauling-scale electronegativities of each atom in the functional group. Although this approach is obviously very simplistic, we find a strong relationship with ROPs. We test this relationship by computing correlation coefficients. Much of the data presented in this paper is discrete and can be described by looking at averaged ROPs for a particular group of molecules. However, for continuous variables such as electronegativity, we can best capture relationships using the correlation coefficient analyses that are intended for two continuous variables. To perform meaningful comparisons we worked only with the set of molecules containing two electron-withdrawing groups that are located on the same side of the ring. The case of electron-withdrawing functional groups on the “same side” is defined as molecules with the functional groups attached to the same ring atom, attached to adjacent ring atoms, or both adjacent to an O or N atom (with the reasoning that the electronegativity of the O or N atom would result in similar polarization of electron density). 354 molecules meet these criteria. For these molecules, we calculated the sum of atomic electronegativities for each atom in the functional group, 𝑋! = ! 𝜒! , where i is an index labeling each atom in the functional group and k=1,2 is an index labeling the two functional groups. We calculate a total sum of electronegativities over the two functional groups as 𝐸𝑛 = 𝑋! + 𝑋! . Table 1 shows En for the electron-withdrawing functional groups. With this method of approximating functional group electronegativity, we see that although the fluoro and hydroxyl groups have very high-electronegativity atoms (O and F), the value of En is rather small because of the lower number of total atoms in that group. We then compute the correlation coefficients R for the variables En and ∆𝐸!,!"#/!" . We find that R(En, ∆𝐸!,!"# )=0.70 with a p-value approaching zero (pvalue

Effects of Functional Groups in Redox-Active Organic Molecules: A

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