Article pubs.acs.org/Organometallics
Striking a Compromise: Polar Functional Group Tolerance versus Insertion Barrier Height for Olefin Polymerization Catalysts Wouter Heyndrickx,†,‡ Giovanni Occhipinti,† Patrick Bultinck,‡ and Vidar R. Jensen*,† †
Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway Department of Inorganic and Physical Chemistry, Ghent University, Krijgslaan 281 (S3), 9000 Gent, Belgium
‡
S Supporting Information *
ABSTRACT: The coordination−insertion random copolymerization of polar and nonpolar olefins holds great potential for the design of new polymers with targeted properties. However, examples of catalysts capable of such polymerization still remain scarce, the majority of which are based on PdII and some also on NiII. So far, the apparent superiority of PdII has not been rationalized. In this work, the catalytic potential of a broad range of transition metals is assessed by investigating their polar functional group tolerance and insertion barrier heights for realistic and comparable complexes. Multivariate regression models suggest that the π-back-donation ability of the metal plays an important role in both the polar functional group tolerance and the insertion barrier height. Specifically, the polar functional group tolerance and insertion barrier height were found to correlate positively, indicating that a compromise must be struck. PdII seems to strike this balance optimally, thus explaining its prominent position as a transition-metal catalyst for copolymerization of polar with nonpolar olefins.
1. INTRODUCTION A key step common to a wide range of commercially important organometallic reactions such as hydrogenation, hydroformylation, Mizoroki−Heck coupling, alkene metathesis, and olefin polymerization is the coordination of an alkene to a transition metal (TM).1 If polar functional groups or compounds such as water are present in the media, the transition-metal catalyst is required to be tolerant toward these in order for the coordination and subsequent reaction of the olefin to be preferred over the corresponding coordination and reaction of the polar entity. This is in particular a notorious problem in coordination− insertion polymerization of olefins, because the most commonly used and active polymerization catalysts of this kind are based on oxophilic, early d0 transition metals such as TiIV and ZrIV, which are easily poisoned by compounds with polar functional groups.2 Late transition metals are commonly known to be more functional group tolerant, and this has made them attractive targets on which to base new polymerization catalysts. A late-transition-metal olefin polymerization catalyst is not only interesting from a practical point of view, i.e., because it loosens the requirements on the purity of the reactants and the reaction medium, but also because it opens the door to one of the long-standing challenges in polymer chemistry: the random coordination−insertion copolymerization of nonpolar olefins with polar olefins.3−5 In particular, the discovery that cationic Pd α-diimine catalysts are capable of copolymerizing ethylene and methyl acrylate (MA) inspired interest and further activity toward this end,6,7 resulting in several examples of late© XXXX American Chemical Society
transition-metal catalysts capable of copolymerizing ethylene with various polar olefins.8−15 MA counts among the most difficult polar olefins to incorporate,16−25 and the most prominent examples of coordination−insertion ethylene−MA copolymerization catalysts are based on PdII.6,7,26−31 NiII also shows copolymerization activity, although under less mild conditions,32 by use of bimetallic Ni systems33 or in combination with methylaluminoxane (MAO).34 The dominance of PdII catalysts for the copolymerization of nonpolar with polar olefins prompts the question whether this particular transition metal is the most suitable, or even the only possibility, for this task. Moreover, contemporary Pd-based catalysts are hampered by low degrees of polar olefin incorporation and reduced catalytic activities compared to those of ethylene homopolymerization.3 Catalysts based on other transition metals might provide a way to circumventing these inherent limitations35 and could also lead to cheaper catalysts if it were possible to replace Pd by a less expensive metal. The tendency of many catalysts to coordinate the polar function rather than the olefinic function constitutes one of the main challenges in the coordination−insertion copolymerization of nonpolar olefins with polar olefins, as has been shown for copolymerizations involving acrylates (where the polar olefin coordinates with its carbonyl oxygen)6,7,36−40 and nitriles.41−50 The polar function can be part of either a Received: February 2, 2012
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monomer or the growing polymer chain. In the latter case, binding of the polar function results in a thermodynamically stable chelate structure (in the case of intramolecular coordination) or oligomerization (in the case of intermolecular coordination) of the catalyst. A catalyst’s tolerance toward polar functional groups is thus of utmost importance, and this property can be expressed as the ligand exchange energy (ΔGexch, cf. Scheme 1) between
reminiscent of the corresponding cyclopentadienyl amido ligands found in constrained-geometry catalysts.53 (2) For groups 8 and 9, a model of the bis(imino)pyridine ligand54 (2), as found in the CoII and FeII precatalysts developed by the Brookhart and Gibson groups,55−57 was used. In the latter ligands, bulky phenyl and methyl substituents were replaced by hydrogen atoms for reasons of computational efficiency.58,59 (3) For groups 10−12, a model of the α-diimine ligand (3), as found in the PdII and NiII catalysts of the Brookhart group,60 was used. For reasons of computational efficiency, bulky phenyl substituents on the imine nitrogen atoms were replaced by methyl groups and substituents on the backbone of the Pd−diimine ring were omitted. It is not possible to guarantee perfect comparability between the different complexes in a broad screening of the central metal atom. In the present study, the three different model systems 1−3 have been selected to enhance comparability and to ensure that most of the catalyst complexes are realistic and viable. For example, a relatively narrow and realistic range of metal valence electron count numbers (14−18) are obtained for the present complexes, which all contain at least one imine functionality, suggesting that the complexes occupy the same chemical space and are to a large extent internally comparable. The most obvious alternative to the present strategy would be to use only a single model ligand system for all metals. While at first glance the latter strategy could appear to maximize comparability, some complexes would necessarily be unrealistic, typically with electron count numbers that are too low or too high and with uncommon metal−ligand combinations. The catalyst was, in all cases, modeled as a cation: i.e., the counterion was omitted from the calculations and assumed to have negligible influence on the insertion barrier heights and ligand exchange energies ΔGexch.61−64 It has been shown that an ethyl ligand is able to reproduce the most vital agostic interactions (β and α) that occur along the reaction pathway,65−70 and an ethyl group was chosen to mimic the growing alkyl chain during propagation. Keeping the model ligands small and avoiding counterions was also considered important in order to focus on the electronic effects of the metal on ΔGexch: i.e., to limit the steric effects of bulky, more realistic ligands or counterions. With the chosen set of ligands, it was possible to screen the complete set of transition metals with an imine functionality present in all three ligands to increase similarity and limit the influence of the ligand on ΔGexch. This work can be seen as a natural extension of earlier work establishing the influence of the transition metal and its oxidation state on ethylene uptake energies,71−73 insertion barriers,72−74 and termination reactions73,75 of the corresponding polymerization catalyst to copolymerization of nonpolar with polar monomers. In particular, these earlier works only concern ethylene as monomer and are limited to parts of the d block. In contrast, the present work covers the complete d block and includes the competition between binding of nonpolar and polar monomers: i.e., ethylene and MA. Furthermore, some ethylene insertion barriers (for smaller selections of metals), ΔG⧧ins, have been calculated in previous contributions, but to ensure that the decisive quantities ΔGexch and ΔG⧧ins are obtained on equal footing (i.e., using identical methods), ethylene insertion barriers were also calculated for all the d block elements in the present work.
Scheme 1. Calculated Ligand Exchange Energy ΔGexch
ethylene and the polar olefin as bound via its polar function, in the present work exemplified by MA coordinating via its carbonyl oxygen. The ligand exchange energy ΔGexch can be defined in terms of the ethylene and MA dissociation Gibbs free energy (ΔGdiss) (see eq 1), and hence a positive ΔGexch ΔGexch = ΔGdiss([M]−C2H4) − ΔGdiss([M]−MA)
(1)
indicates a preference for ethylene coordination: i.e., that the equilibrium shown in Scheme 1 is shifted to the left. Similar computational quantifications of the polar functional group tolerance have been undertaken earlier in the context of copolymerization to rationalize the performance of particular catalysts and to screen potential catalysts and polar comonomers. In those cases the polar functional group tolerance was calculated as the energy difference between two competitive binding modes of the monomer, i.e., via its olefinic function (often termed π-complex) and via its polar functional group (often termed σ-complex),39,41−43,45,46,51,52 an approach that could not be followed here due to simultaneous coordination of both the olefinic and the polar function for some MA complexes intended to bind solely via the olefinic function. In the present work, olefin coordination is limited to metal−ethylene complexes and none of the MA complexes display metal coordination via the alkene bond. In this work, we have probed the influence of the transition metal on this important catalyst property by calculating ΔGexch by means of density functional theory (DFT). All 30 d-block metals have been examined. The chosen model catalyst systems were as much as possible chosen according to known ethylene polymerization catalysts, while at the same time maintaining a high degree of similarity between the various metal complexes. All complexes were restricted to be monocationic. In all cases, the formal oxidation state of the metal was either II or III. In total, three different ligands were used (see Chart 1): (1) For groups 3−7, the bidentate cyclopentadienyl (Cp) imine ligand η5:η1-C5H5CH2CNCH3 (1) was used, Chart 1. Model Systems of the Ethylene Complexes Employed in the Calculation of ΔGexch
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distance between the α-ethyl (an ethyl group mimics the growing polymer chain) carbon and an ethylene carbon as the step variable. The relaxed LT calculations were started from distinct ethylene complex minima, and each such LT scan usually resulted in the location of a suitable starting point for a distinct insertion transition state, which subsequently could be fully optimized and verified to have only one imaginary frequency (corresponding to the ethylene−ethyl stretching mode). The insertion barrier was then taken as the energy difference between the most stable ethylene complex minimum and the most stable insertion transition state. In the present work, effective core potentials (ECPs) of the Stuttgart−Dresden kind were used for all non-hydrogen elements. This strategy ensures inclusion of scalar relativistic effects important for the heavier elements and is also computationally efficient. Careful validation studies have shown that only insignificant accuracy is lost in geometry optimizations of transition-metal complexes involving welldesigned effective core potentials of the Stuttgart−Dresden kind compared to optimizations using all-electron basis sets.82 An allelectron description involving valence basis sets similar in quality to those used here has previously been observed to increase the time needed for each SCF procedure by up to 35% in B3LYP calculations on first- and second-row transition-metal complexes in comparison to the present ECP-based approach.88 Additional, more substantial, reductions in the computational effort can be expected when applying relativistic Stuttgart−Dresden ECPs to the current third-row transition metals, for which a significant number of f primitives would be required to describe the 4f shell properly in all-electron hybrid density functional calculations.82 The ECPs applied in the current geometry optimizations accounted for two inner electrons of C, N, and O and were used in combination with their corresponding [2s2p] (C, N) and [2s3p] (O) contracted valence basis sets.89 Similarly, the first-row transition metals were described by a 10-electron ECP accompanied by a (8s7p6d)/[6s5p3d] contracted valence basis set,90 the secondrow transition metals by a 28-electron ECP accompanied by a (8s7p6d)/[6s5p3d] contracted valence basis set,91 and the third-row transition metals by a 60-electron ECP accompanied by a (8s7p6d)/ [6s5p3d] contracted valence basis set.91 An exception among the latter is Lu, which was accompanied by a (7s6p5d)/[6s5p4d] contracted valence basis set.92,93 Hydrogen atoms were described by a Dunning double-ζ basis set.94 2.2. Single-Point Energy Evaluations. Single-point (SP) energy evaluations at the optimized geometries were performed using the hybrid-GGA functional B3LYP76−78 as implemented in the Gaussian 03 suite of programs,79 complemented with the D2 empirical dispersion term proposed by Grimme95 and calculated using his DFT-D3 program,96 to give the corresponding B3LYP-D estimates. The SCF convergence criterion was tightened 10-fold in comparison with the Gaussian 03 default for SP calculations, to 10−5 (rms density matrix) and 10−3 (MAX density matrix). Whereas the ECPs described above for the geometry optimizations were retained in the SP energy evaluations, the valence basis sets were substantially improved, to at least polarized triple-ζ quality, with diffuse s and p functions on most elements. This approach, in addition to being cost-efficient in particular for the 5d elements (vide supra), should give reasonable flexibility to describe chemical bonding and reactions at the same time as minimizing the basis set superposition error (BSSE). The valence basis sets for C, N, and O were supplemented by single sets of diffuse s and p functions, obtained even-temperedly, and also by polarization d functions (αd = 0.72 for C; αd = 0.98 for N; αd = 1.28 for O). The resulting (5s5p1d) primitive basis sets for C and N were contracted to [4s4p1d], whereas the (5s6p1d) primitive basis set for O was contracted to [4s5p1d]. For the transition metals, two primitive f functions were added to the (8s7p6d) primitive basis sets. The resulting (8s7p6d2f) primitive basis set was contracted to [7s6p4d2f].90,91 For Lu, three primitive f functions were added to the (8s7p6d) primitive basis sets, contracted to [7s6p4d2f].92,93 Hydrogen atoms were described by a Dunning triple-ζ basis set94 augmented by a diffuse, even-tempered s function (αs = 0.043 152) and a polarization p function (αp = 1.00).
Previous molecular-level calculations have shown that, unfortunately, the polar functional group tolerance correlates positively with the height of the insertion barrier for a set of Pd α-diimine catalysts.46 The main motivation behind the current work thus is to search for candidate transition metals, other than Pd, that could deliver high functional group tolerance ΔGexch combined with low insertion barriers ΔG⧧ins. The focus is on performing a broad screening and singling out the effects of the transition metals.
2. COMPUTATIONAL DETAILS 2.1. Geometry Optimization and Calculation of Thermochemical Corrections. Geometry optimizations were performed using the hybrid generalized gradient approximation (hybrid-GGA) functional B3LYP76−78 as implemented in the Gaussian 03 suite of programs.79 The performance of the individual density functionals in geometry optimization typically depends quite strongly on the transition metal and the kind of compound investigated. This problem has been illustrated nicely in a series of investigations by Bühl and coworkers, in which they have compared DFT-optimized geometries of metal complexes of all three transition rows with highly accurate gasphase microwave and electron diffraction (GED) geometries.80−82 For the combined set of all three rows,82 they found that hybrid functionals performed better than their nonhybrid counterparts, with PBEh and B3P86 offering the lowest statistical errors. While, for this reason, it was tempting to adopt B3P86 for the present project, we finally opted for a closely related functional, B3LYP, which also was found to perform well in the overall assessment,82 due to its longstanding and solid reputation for obtaining reliable geometries and properties in a wide range of chemical reactions and applications, including those of organometallic and transition-metal chemistry.83 In the years since the investigations of Bühl and co-workers,80−82 and while the present work was in progress, a rapid development of functionals accounting for weak interactions and dispersion, not included in B3LYP or other traditional, GGA-based functionals, has taken place. However, even in cases where inclusion of dispersion drastically improves upon the intermolecular interaction energies, the corresponding effect on optimized geometries may turn out to be small.84 In fact, for the majority of TM complexes, the influence of dispersion on the geometries seems to be limited, as demonstrated in a reinvestigation85 of the same set of accurate gas-phase structures as used by Bühl and co-workers.80−82 In the more recent investigation85 it was found that the Minnesota functionals,86,87 which are designed to include mediumrange correlation and dispersion via extensive parametrization, did not offer more accurate geometries than did standard GGA and meta-GGA functionals such as PBE and TPSS. Numerical integrations were performed using the default fine grid of Gaussian 03 (75 radial shells and 302 angular points per shell). Gaussian 03 default values were also adopted for geometry optimization (maximum force 0.000 45 au, rms force 0.0003 au, maximum displacement 0.0018 Å, rms displacement 0.0012 au) and SCF convergence (10−8 (rms density matrix) and 10−6 (MAX density matrix)) criteria. All geometry optimizations were performed within C1 symmetry. All optimized geometries were characterized by the eigenvalues of the analytically obtained second-derivatives matrix (Hessian). Thermal corrections to the thermodynamic functions and their kinetic counterparts (in the case of transition states) were computed within standard ideal-gas, rigid-rotor, and harmonic oscillator approximations at default temperature (298.15 K) and pressure (1 atm). Open-shell systems were treated in the unrestricted Kohn−Sham formalism. For both the ethylene and MA complexes, conformational issues were tackled by explicit manual consideration, using DFT, of several (3−8) of the most reasonable candidate minima. Automatic conformational searching based on force-field methods was not practical due to the lack of suitable force constants for a number of the geometry parameters. Starting points for the ethylene insertion transition states were obtained in series of constrained geometry optimizations (relaxed linear transit (LT) calculations) using the C
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Electrostatic and nonelectrostatic solvent effects were estimated using the polarizable continuum model (PCM)97−99 as implemented in Gaussian 03. Toluene, frequently used in polymerization, was chosen as solvent in the solvent calculations. Solute molecular cavities were generated on the basis of Bondi atomic radii100 involving explicit hydrogen atoms. The default tesserae area (0.2 Å2) was reduced to 0.05 Å2, resulting in smoother cavity surfaces. 2.3. Regression Analysis. Multiple linear regression (MLR) was performed with the program ARTE-QSAR101 in order to obtain insight into the factors influencing ΔGexch and ΔG⧧ins. ARTE-QSAR101 offers model validation techniques pertaining to the model’s validity, stability, and predictivity. To measure the goodness of fit of the model, the squared multiple correlation coefficient R2 is used. Autoscaling, i.e. the combination of scaling of the variables by (standard deviation)−1 after subtraction by the mean, i.e., mean centering, was performed prior to analysis. Cross-validation indicating predictivity via the crossvalidated R2 (q2) was obtained via the leave-many-out technique, where the set of 30 transition metals were divided into a training group of 25 transition metals and a test group of 5 transition metals (leavefive-out). The Topliss ratio (ratio of samples over descriptors in the model) was kept higher than 5 for all models. Descriptor selection was done via sequential selection, allowing the automated selection of the best possible model from all possible models for a fixed number of descriptors. Artificial overfitting is always a risk when using MLR techniques, and hence special precautions are taken in the form of the unsupervised forward selection (UFS) scheme.102,103 In UFS it is checked whether a newly added variable is redundant, informationwise, with respect to an already included variable or with respect to a linear combination of already included variables. In other words, considering the set of descriptors for QSAR for the different molecules as a matrix, UFS minimizes the correlation between the columns in the matrix.
Figure 1. ΔGexch in kcal/mol for all transition metals as a function of their group in the periodic table.
0 kcal/mol and is significantly higher (by 7.5 kcal/mol) than that for Ni. This energy difference of 7.5 kcal/mol between Ni and Pd is consistent with the results obtained by Ziegler and co-workers for MA π- and σ-complexes of Ni and Pd α-diimine model catalysts.37,39 The difference between Ni and Pd can be generalized: a clear trend is that the first-row transition metals (except for groups 3, 4, 11, and 12) show considerably lower ΔGexch values than their second-row counterparts, which, in turn, show lower ΔGexch values than the third-row metals. The difference between the third and the second row is smaller than that between the second and the first. A maximum in ΔGexch is observed for the mid transition metals (groups 5−8), implying that these metals are the most selective for the alkene double bond versus the polar ester group. A very negative (
