Angela L.
Holmberg
a,
Michael G.
Karavolias
a and
Thomas H.
Epps
III
*ab
aDepartment of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, USA. E-mail: thepps@udel.edu
bDepartment of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, USA
First published on 22nd April 2015
This work features a new suite of correlations for estimating kinetic parameters from multicomponent reversible addition–fragmentation chain-transfer (RAFT) polymerizations and an improved methodology for determining reactivity ratios in the pursuit of cost-effective and renewable plastics prepared from moderately processed bio-oils. Select monomers representing possible derivatives of compounds found in renewable bio-oils, such as pyrolyzed Kraft lignin and vegetable oils, were polymerized to investigate the consequences of structural diversity on the kinetics of RAFT polymerization. To facilitate predictions of heteropolymer dispersities and molecular weights, apparent chain-transfer coefficients (Capptr's) and propagation rate constants (kappp's) from homopolymerizations were correlated to kinetic parameters associated with the polymerization of bio-oil mixtures. Capptr depended on the reactivity ratios of the bio-oil components and the composition of the bio-oil feed, whereas kappp was related to only the composition of the bio-oil feed. A modified approach for analyzing Mayo–Lewis plots resulted in more accurate reactivity ratios and with greater precision in comparison to conventional nonlinear fitting procedures and traditional linearization fitting methods, respectively. The measured compositional data readily mapped onto the predicted monomer distribution profiles in multicomponent polymers, confirming the validity of the improved method described herein to determine reactivity ratios. Altogether, this manuscript offers a strategy for improving the viability of biobased polymers, addressing two key factors: minimizing separations costs by polymerizing bio-oil mixtures and preventing batch-to-batch inconsistencies in polymer properties by applying a priori knowledge about the bio-oil constituents’ individual kinetic parameters.
Yet another advantage offered by heteropolymers often is overlooked: polymerized mixtures can provide cost savings by reducing separations needs. Such an approach can ameliorate some of the economic challenges associated with biobased plastics. These renewable materials tend to be expensive largely due to the costs of separating complex mixtures (i.e., bio-oils) into purer individual components.10,11 Renewable bio-oil mixtures could be incorporated into plastics directly (or after minimal processing) as heteropolymers, thereby eliminating many separation steps, reducing costs, and subsequently enabling novel bio-based polymers to compete economically with well-established petroleum-based polymers.
Coupling the advantages of heteropolymers with controlled reversible-deactivation radical polymerization techniques, such as reversible addition–fragmentation chain-transfer (RAFT) polymerization, further improves the polymer's utility. Materials synthesized via controlled methods exhibit reproducibly narrow molecular weight distributions and predictable molecular weights. The resulting polymers subsequently can be chain-extended into self-assembling block polymers for numerous applications that benefit from nanostructure formation.12–14 For biobased polymers, these controlled methods are employed predominantly to synthesize materials for thermoplastic elastomers and pressure-sensitive adhesives,10,15 and RAFT potentially is ideal due to its sustainability benefits.16
Despite the above advantages, employing mixtures of monomers in the synthesis of polymers comes with numerous challenges attributable to the multivariate nature of the polymerization feedstocks,1,17 in which the properties of an n-component heteropolymer depend minimally on n − 1 mole-fractions of monomers and n!/(n − 2)! reactivity ratios. Bio-oils come with additional difficulties. Not only are bio-oils naturally complex, but their compositions also can vary significantly depending on the feedstock source, type, season, processing method, etc.18–21 This variability can hinder the practicality of bio-oil-based heteropolymers due to the possibility of generating materials with different monomer distributions (or polymer compositions) and consequently inconsistent properties between batches.
The concern of variability in bio-oil composition can be mitigated if controls are enacted to manage the polymerization kinetics and keep the monomer segment distributions and overall polymer compositions in an acceptable range for property consistency. Of utmost importance for reproducible properties is the monomer distribution profile; different glass transition temperatures and mechanical properties, among other traits, are displayed by gradient, statistical, blocky, and random segment distribution types.22–29 Control over these characteristics results from knowing the composition of the bio-oil, the kinetic parameters for the homopolymerization of each constituent, and how those kinetic parameters impact the heteropolymerization of corresponding mixtures.
In this work, we investigate the RAFT homopolymerization behavior of a library of potentially biobased methacrylates, as well as the polymerization behavior and resulting monomer segment distributions when producing heteropolymers from model bio-oil mixtures. To the authors’ knowledge, this report uniquely investigates equations that correlate select kinetic parameters from RAFT polymerizations containing more than two monomers to kinetic parameters from the homopolymerizations of the individual components. Additionally, this study features an elegant approach for fitting Mayo–Lewis reactivity ratio data with an appropriate level of precision, and it contributes to the growing library of biobased monomers10 available for block polymer syntheses. The correlations presented in this work for the apparent chain-transfer coefficient (Capptr) and the apparent propagation rate constant (kappp) in heteropolymerizations, as well as the structural differences between the monomers, help define the relative values of the kinetic parameters. The reported nonlinear Mayo–Lewis fitting procedure for determining reactivity ratios is an improvement over traditional methods as it accounts for variable level of confidence in the individual data points to enhance measurement credibility. The resulting reactivity ratios are employed to predict monomer distributions, providing insight into the separations that may be required in bio-oil processing for polymers applications. Finally, the structurally diverse collection of monomers in this work, combined with the insight into their kinetic behavior, provides exciting pathways to designer polymers with precise molecular weights and compositions.
The monomers investigated in this work (Scheme 1) are derivatives of compounds that can found in some processed biomasses, such as pyrolyzed Kraft lignin,18,19,30 fermented biomass,31,32 and certain plant oils.10,33 The representative bio-oil constituents include guaiacol, creosol, 4-ethylguaiacol, vanillin, and phenol, which can comprise depolymerized softwood lignin;18,19,30n-butanol, which is a sought-after fermentation product;32 and lauric acid, which is a major constituent of coconut oil.33 Each of these chemicals can be converted to guaiacol methacrylate (GM, 2-methoxyphenyl methacrylate), creosol methacrylate (CM, 4-methyl-2-methoxyphenyl methacrylate), 4-ethylguaiacol methacrylate (EM, 4-ethyl-2-methoxyphenyl methacrylate), vanillin methacrylate (VM, 3-methoxy-4-methacryloyloxybenzaldehyde), phenyl methacrylate (PM), n-butyl methacrylate (BM), and lauryl methacrylate (LM, dodecyl methacrylate).34–36 Ideally, these renewable bio-oil methacrylate (BOM) monomers would be obtained from minimally processed mixtures and subsequently polymerized, with non-reactive species in the bio-oil serving as the polymerization solvent. The composition of the bio-oil or BOM could be assessed by partial fractionation of the components and subsequent characterization of the resulting fractions by advanced methods,37 such as two-dimensional gas chromatography coupled with time-of-flight mass spectrometry38 or various NMR techniques.39 The chemical makeup of the mixture could be refined as necessary by distillation, solvent extraction, blending, or other techniques. However, in this model study, petroleum-based versions of the individual components are mixed prior to polymerization, and the composition of the idealized BOM is confirmed by NMR spectroscopy. Anisole is the representative polymerization solvent, as its chemical structure is similar to other inert lignin pyrolysis products19,30 and it is considered reasonably ‘green’.40
The first model bio-oil methacrylate polymer (poly[bio-oil methacrylate]-1 or PBOM-1) investigated in this work is composed solely of compounds that can be generated from the pyrolysis products (bio-oil) of softwood Kraft lignin.18,30 These 2-methoxyphenol (guaiacol) derivatives are structurally identical except for the p-position moiety, which is a hydrogen atom (GM), methyl group (CM), ethyl group (EM), or formyl group (VM) depending on the monomer, and provide insight into the assumptions that can be made regarding the RAFT polymerization behavior of reasonably homogeneous bio-oils. The second model poly(bio-oil methacrylate) (PBOM-2) comprises possible derivatives of compounds found in multiple different processed biomasses: vanillin, a common aromatic target of lignin pyrolysis; phenol, an aromatic component of more deoxygenated lignin-based bio-oils;30n-butanol, a short-chained fatty alcohol from some fermentations; and lauric acid or lauryl alcohol, a long-chained fatty acid or alcohol found in many plant oils.10 The polymerization of BOM-2 serves to model situations in which bio-oils are mixed or are less structurally homogeneous. Furthermore, mixing dissimilar bio-oils is an attractive approach for tuning polymer properties and compositions.
The kinetic parameters we chose to investigate, viz., reactivity ratios, kappp, and Capptr, provide a means for controlling polymerization behavior and resulting polymer characteristics. Reactivity ratios (ri,j's) define the relative rate constants for a radical of monomer i self-propagating with monomer i vs. cross-propagating with monomer j, and allow one to calculate the expected monomer distribution profile in a polymer, whether it is gradient, statistical, blocky, or random.17 In RAFT polymerizations, kappp influences the polymerization rate and therefore the ‘livingness’ of a polymerization (i.e., the propensity for reversible deactivation during a polymerization); slow rates can lead to low fractions of ‘living’ chains (i.e., polymers with retained RAFT-enabling end groups) at high molecular weights or conversions.12,41 Additionally, Capptr correlates the rate of monomer consumption to the rate of chain-transfer agent consumption and helps define the dispersity (Đ) of the resulting polymer.12,42 Increasing values of Capptr correspond to narrowing polymer Đ's, faster consumption rates of the chain-transfer agent, and consequently more accurate molecular weight predictions at low monomer conversions. The value of Capptr depends on numerous factors including reaction solvents and temperatures, reagent choice, and certain reagent concentrations.12,42
All synthesized monomers were purified to >98 mol% as described below. GM, CM, and EM were subjected to flash chromatography on silica gel (Sorbent Technologies, Standard Grade, 230 × 400 mesh, 60 Å) with a ‘green’ tripartite eluent43 (0.9/0.075/0.025 v/v/v heptane/isopropyl acetate/methanol), dried under reduced pressure, and stored at −2 °C until use. VM was purified as previously reported44via serial recrystallizations from hexanes (or n-heptane as a ‘green’ alternative) and stored at −2 °C until use. BM was purified to >99 mol% by vacuum distillation from calcium hydride at 0 °C, and PM was purified to >99 mol% by fractional vacuum distillation from calcium hydride at 40 °C. Unreacted methacrylic anhydride and methacrylic acid byproducts were removed in the first fraction of distilled PM, and both monomers were stored at −2 °C until use. Caution: calcium hydride reacts violently with water and should be deactivated carefully over ice prior to disposal. Purified BM was stored at −2 °C for ∼two weeks before auto-polymerization was noted.
The molar monomer-to-polymer conversion (x) for each sample was calculated using 1H NMR data by tracking the change in area of the allyl peaks (6.45–5.45 ppm, see ESI† for individual peak assignments) relative to the peaks in the reference aliquot that were normalized to an internal standard. Anisole peaks (methoxy: 3.79 ppm singlet and aromatics: 6.97–6.88 ppm multiplets) served as the internal standard for the homopolymerizations of PM (methoxy), BM (aromatics), and LM (aromatics), whereas DMF peaks (8.01 ppm singlet and 2.91 ppm doublet) served as the internal standard for the polymerizations of all other monomers (GM, CM, EM) and reactivity ratio samples. For VM-containing polymerizations, the cumulative area of the aldehyde peak in VM and PVM (10.10–9.60 ppm) was the internal standard (i.e., VM, BOM-1, and BOM-2). The composition of each BOM aliquot and reactivity ratio sample also was determined using 1H NMR in CDCl3, and the corresponding characteristic NMR spectra and analysis methods are presented in the ESI.†
![]() | (1) |
![]() | (2) |
The mean standard error in Ficalc for the resulting fit equation was estimated using the following equation:
![]() | (3) |
![]() | (4) |
C apptr for each polymerization was estimated from SEC data (Xn and Xw), the fractional conversion of monomer (x), and the following form of the Mayo equation:42,46
![]() | (5) |
In addition to measuring Capptr for each polymerization, an average apparent chain-transfer coefficient (apptr) was estimated for each heteropolymerization using a nonlinear combination of the apparent chain-transfer coefficient Capptr,i from each of the homopolymerizations of monomer i, the mole-fraction of each monomer in the feed, and the reactivity ratios between each of the monomers. The generic equation that we used to calculate
apptr for the polymerization of a mixture of j chemically distinct monomers was as follows:
![]() | (6) |
In this work, we determined that eqn (6) could be applied accurately to estimate apptr from each heteropolymerization, thus providing information about the expected Đ of a heteropolymer prior to synthesis. Using eqn (6) with j = 4 and the other relevant parameters (the reactivity ratios and monomer feed compositions are in the following sections), the expected
apptr for BOM-1 and BOM-2 was 2.4 ± 0.4 and 7.0 ± 0.6, respectively. These estimates are statistically indistinguishable from the measured values of 2.1 ± 0.3 and 7.4 ± 0.5 reported in Table 1, indicating that eqn (6) is appropriate for estimating
apptr in multicomponent RAFT polymerizations. Note that one variation of the two-component equation weights each fj in the denominator of eqn (6) by the product of ri,j instead of the quotient;52 however, that weighting does not accurately capture
apptr in BOM-2, significantly overestimating the value as 11.9 ± 1.3. A simple mole-weighted average also overestimates
apptr for BOM-2 as 9.5 ± 0.2. More complicated models might improve the accuracy of the estimate for
apptr if extended beyond the binary case, but they require additional parameters and measurements.51 Thus, eqn (6) provides a good estimate for
apptr without requiring excessive experimentation.
M | [M]/[T]b | [I]/[T]c | t init (h) |
k
appp![]() |
C apptr |
---|---|---|---|---|---|
a Reactions were performed at 72 °C in anisole (4.9 wt% DMF) with a monomer (M)![]() ![]() ![]() ![]() |
|||||
GM | 134 ± 16 | 0.12 ± 0.04 | 0.2 | 0.29 ± 0.07e | 1.4 ± 0.5e |
CM | 144 ± 9 | 0.13 ± 0.02 | 0 | 0.22 ± 0.03e | 2.7 ± 1.1e |
EM | 129 ± 16 | 0.09 ± 0.02 | 0 | 0.23 ± 0.04e | 2.5 ± 0.6e |
VM | 230.3 | 0.100 | 2.1 | 0.21 | 2.8 ± 0.1 |
PM | 229.9 | 0.100 | 1.0 | 0.11 | 4.6 ± 0.3 |
BM | 229.7 | 0.100 | 0.4 | 0.17 | 14 ± 2 |
LM | 224.4 | 0.100 | 2.6 | 0.09 | 19 ± 3 |
BOM-1f | 232.9 | 0.100 | 0.4 | 0.24 | 2.1 ± 0.3 |
BOM-2f | 230.0 | 0.100 | 0.1 | 0.16 | 7.4 ± 0.5 |
As indicated by eqn (6), an interesting implication of polymerizing mixtures is that apptr can be lower for a heteropolymerization than any of the corresponding homopolymerizations (as demonstrated previously for binary cases51). Such low
apptr's can result in materials that have broad Đ's (e.g., Xw/Xn from eqn (5)) and therefore ‘poor gradient quality’.58 Unfavorable
apptr's are most likely in systems that favor alternating monomer distributions (i.e., as any product of ri,j and rj,i approaches zero), which is not the case in the sets of monomers herein. An increase in
apptr and decrease in Đ relative to corresponding homopolymerizations and homopolymers would arise only if a significantly blocky monomer distribution is expected (i.e., if at least one product of ri,j and rj,i greatly exceeds unity and the rest approach unity), which is uncommon.59
Additional kinetic parameters from the homopolymerizations and heteropolymerizations of interest to this work are tinit and kappp. Together, these parameters describe the time over which a reaction should proceed to reach a desired monomer conversion with an appropriate fraction of ‘living’ chains (L). Estimates for tinit and kappp were extracted from data in Fig. 2 and are reported in Table 1.
![]() | ||
Fig. 2 Kinetic data with pseudo-first-order linear fits (dashed lines) from the polymerizations of (a) the color-coded lignin-based monomers and bio-oil and (b) the remaining color-coded monomers and bio-oil. The data in (a) were normalized to an initiator/chain-transfer agent ([I]ref/[T]) ratio of 0.10, noting the proportionality of kappp (the slope) to the square-root of the initiator concentration.56 The data in (b) were all collected at [I]/[T] = 0.10, so normalization was unnecessary. Error bars represent 95% confidence limits on the basis of interpretation of the NMR data and the accuracy of the [I]/[T] ratio. |
No universal relationship can be gleaned from the tinit data, which is not unusual as this parameter is strongly affected by impurities in the reaction mixture, such as air or water, especially when benzodithioates are selected as the chain-transfer agents.12 The challenge of an unpredictable tinit is that the necessary reaction time for a polymerization to reach a desired conversion, and thus L (a function of reaction time), also is unpredictable. This problem can be mitigated by seeding the polymerizations with macromolecular or oligomeric chain-transfer agents, which typically have negligible pre-equilibrium times.12
The expected relationship between kappp in the polymerizations of the individual monomers and the BOM mixtures is complicated, possibly dependent on conversion and penultimate reactive chain ends, and unreported for systems with four or more chemically distinct monomers.60–62 Thus, we employ the simplest assumption, taking kappp as a molar composition-weighted average of the normalized kappp's from the homopolymerizations of each constituent (Table 1). This average allows one to estimate kappp's for BOM-1 (0.24 ± 0.02 h−1) and BOM-2 (0.15 ± 0.01 h−1), which agree closely with the measured values of 0.24 ± 0.01 h−1 and 0.16 ± 0.01 h−1, respectively, from Table 1. More experiments would be necessary to confirm whether this simplification of the kinetic behavior in heteropolymerizations is widely applicable, but these experiments suggest that an average gives an appropriate first-approximation for kappp in homogeneous mixtures of similarly structured monomers (e.g., miscible methacrylates).
In comparing each kappp from the individual homopolymerizations, polymerization behavior generally can be explained by steric effects. The constituents of the homogeneous bio-oil, BOM-1, polymerize at similar rates, whereas the short-chained and long-chained n-alkyl methacrylates, BM and LM, polymerize at different rates. However, one key exception is PM, which propagates more slowly than the guaiacol methacrylate derivatives (VM, GM, CM, and EM) despite the fact that PM is not hindered by an o-methoxy group. This reduced propagation rate may be explained by the electron-donating potential of methoxy moieties63 that promotes reactivity in substituted aromatic monomers relative to unsubstituted aromatic monomers.64 Overall, this comparison between PM and the components of BOM-1 may make lignin-based bio-oils with higher methoxy contents more favorable for polymerization than those that have been substantially deoxygenated.
i ↓ j→ | VM | PM | BM | LM |
---|---|---|---|---|
VM | — | 1.18 ± 0.08 | 1.21 ± 0.10 | 1.88 ± 0.10 |
PM | 0.58 ± 0.04 | — | 1.10 ± 0.09 | 1.00 ± 0.11 |
BM | 0.57 ± 0.05 | 0.45 ± 0.04 | — | 0.97 ± 0.04 |
LM | 1.06 ± 0.05 | 0.44 ± 0.05 | 0.81 ± 0.03 | — |
One feature about the reactivity ratios is the similarity in values between monomers that have homologous structures (i.e., VM–EM, VM–CM, and BM–LM). VM, EM, and CM only differ structurally by the p-position formyl, ethyl, and methyl moiety, so their reactivity ratios are expected to be close to unity, which is indeed the case. Deviations from unity are either due to slight reactivity and steric differences between the monomers or minor systematic errors that arise from interpreting overlapping peaks in the NMR spectra. When the VM–EM and VM–CM data are fit assuming ri,j = rj,i = 1, the standard mean error estimates in ri,j only change from 0.02–0.03 to 0.03–0.04, and the coefficients of determination that describe the quality of the fit (r2) only decrease from 0.98 and 0.96 to 0.94 and 0.95 for VM–EM and VM–CM, respectively. These changes are trivial, indicating that a fair assumption for structurally homogeneous bio-oils is that all of the reactivity ratios are unity; hence, we assume that all ri,j in BOM-1 equal one throughout this work. This assumption was checked by select data in two other instances (EM–CM and GM–EM for the case in which fEM ≈ FEM ≈ 0.5 mole-fraction for both samples), further supporting its validity.
Similar assumptions did not apply to the components in BOM-2 as the structural, polarity, and reactivity differences between the monomers are significant. The Q–e model proposed by Alfrey and Price helps to explain the relative values for ri,j in Table 2,64,65 but the necessary information (i.e., the reactivity ratios between each monomer and a standard such as styrene or methyl methacrylate) for such a comparison currently is unavailable for this set of monomers under the employed RAFT polymerization conditions.
The uniquely rigorous, albeit simple, fitting and error analysis methods presented in this work permit calculation of the possible errors resulting from the systematic reduction in resolution (i.e., measurement accuracy) with respect to changes in composition. The development of this model was inspired by the BM–VM data in Fig. 3, in which the 95% confidence intervals from the 1H NMR data were broad at high VM content yet narrow at low VM content. We wanted to measure accurate ri,j while capturing the varying level of confidence in the reported error, which is oversimplified by most least squares fitting procedures.
An example set of ri,j data determined via multiple methods is reported in Table 3 and illustrates the significance of the newly reported analysis procedure (eqn (2)–(4)). As shown, wide-ranging values with non-overlapping confidence intervals were measured for the LM–VM data by traditional least squares fitting approaches. The discrepancies in the data are likely byproducts of the relative magnitudes of ri,j and rj,i, the range of compositions over which the reactivity data were measured, and the level of scatter in the data. In general, averages of the Fineman–Ross66 and Kelen–Tüdös67,68 data were accurate but imprecise, and the unaveraged data were unfavorable in both accuracy and precision. The conventional nonlinear fitting method, which follows eqn (2) but with σn2 = 1 and both Fj terms = 0, gave reasonable and precise values for ri,j, but the precision led to exaggerated error estimates (i.e., misleadingly narrow confidence intervals). Jaacks’ method,69 a linearization approach that can be attractive for its simplicity, was not tested in this work due to the model's dependence on samples of considerable compositional asymmetry (i.e., samples that are ∼95% one monomer),2 which was not representative of the systems studied herein. Eqn (2) provides an elegant and simple solution to these issues, yielding accurate and precise results with aptly represented and low error (eqn (4)). Accordingly, the reported method and equations improve the integrity of the resulting reactivity ratio measurements with minimal added effort.
Method | r VM,LM | r LM,VM |
---|---|---|
a Conventional nonlinear and linear fitting methods disregard error in the individual data points and in this example are measured only for monomer 1 = VM; different values can be generated when monomer 2 = VM but are excluded for brevity. b Fineman and Ross report two linearized forms of the Mayo–Lewis equation that correspond to eqn (3) and (4) in ref. 66. c The confidence intervals from this method are misleadingly narrow. d Mean standard errors from Table 2 (eqn (4)) were converted to 95% confidence intervals using Student's t-distribution. | ||
Fineman–Ross (3)b | 1.59 ± 0.07 | 0.90 ± 0.12 |
Fineman–Ross (4)b | 1.95 ± 0.24 | 1.21 ± 0.12 |
Fineman–Ross (average) | 1.77 ± 0.18 | 1.05 ± 0.12 |
Kelen–Tüdös | 1.72 ± 0.24 | 1.03 ± 0.12 |
Conventional nonlinear fitc | 2.12 ± 0.10 | 1.21 ± 0.05 |
This work (error-weighted fit)d | 1.88 ± 0.06 | 1.06 ± 0.04 |
![]() | ||
Fig. 4 Measured (points) and predicted (lines) cumulative polymer compositions as a function of conversion (x) in the polymerization of BOM-1. The prediction assumes ri,j = 1 for every monomer pair. |
Sample | i | f i,0 | F i,0 |
---|---|---|---|
a Monomer (fi) and polymer (Fi) compositions are mole-fractions. | |||
BOM-1 | GM | 0.27 | 0.27 |
CM | 0.21 | 0.21 | |
EM | 0.28 | 0.28 | |
VM | 0.24 | 0.24 | |
BOM-2 | VM | 0.23 | 0.28 |
PM | 0.29 | 0.34 | |
BM | 0.30 | 0.25 | |
LM | 0.18 | 0.13 |
There are at least two features of note in the data from Fig. 4. First, the VM and GM compositions change slightly with conversion, with slopes of −0.01 and 0.03, respectively. This trend may result from the different electron densities in the p-position moieties for VM (formyl group) and GM (hydrogen) relative to CM and EM (alkyl groups); however, these changes with conversion are within experimental error and may be artificial. If real, the trend is captured by reactivity ratios spanning the measured values of 0.87–0.97 (see ESI†), yet trivial in comparison to the reactivity ratios from BOM-2 that span 0.4 to 1.9. Second, the data in Fig. 4 deviate from the expected trend at low molar conversions (x < 0.2). This deviation may be the result of initiator effects, in which one monomer is initiating and starting to propagate before the others, or concentration effects, in which the interpretation of the NMR spectra is least accurate at low conversions (i.e., when larger monomer peaks obscure smaller polymer peaks).
The positional and cumulative compositions of monomer units distributed along the polymer chain (i.e., monomer distribution profiles) predicted and measured for BOM-2 as a function of conversion are shown in Fig. 5. In this example, the reactivity ratios are not unity (Table 2), and the products of reactivity ratios are predominantly less than one, so a gradient polymer is expected. The profiles in Fig. 5a illustrate how the monomer units are distributed along the polymer chain, and the data in Fig. 5b support that a gradient heteropolymer was synthesized. The amounts of BM and LM could not be quantified individually as their characteristic NMR peaks were indistinguishable in the presence of VM and PM units. Nevertheless, the predicted positional compositions in Fig. 5a indicate that LM is consumed more slowly than BM, and both monomers are consumed more slowly than PM and VM. At full conversion, the heteropolymers would be terminated predominantly by LM units. These results are consistent with the reactivity and steric arguments presented in previous sections and validate the unique methods applied in this manuscript for determining ri,j.
![]() | ||
Fig. 5 (a) Predicted positional polymer composition and (b) measured (points) and predicted (lines) cumulative polymer composition as a function of conversion (x) in the polymerization of BOM-2. The predictions utilize the reactivity ratios from Table 2. LM and BM data are combined in (b) as the individual compositions are indistinguishable by 1H NMR. |
Additional features apparent in Fig. 5b are the accuracy with which the predicted data overlap the measured data in the cumulative composition profiles at high molar conversions (x > 0.2), yet the distinct deviation in the data at low molar conversions (x ≤ 0.2). As with BOM-1, it is unclear whether the mismatch at low conversions was a byproduct of sample concentration (NMR resolution) or initiator effects. However, a reasonable assumption is that initiator effects are not significant, as was the case in Ting et al.'s four-component acrylate system.17 If initiator effects were substantial, the reactivity ratios that were all determined at low conversions (i.e., x = 0.06–0.20 mole-fraction, the compositions at which the data mismatch occurs in Fig. 4 and 5b) would not have depicted accurately either the monomer distribution profiles or the previously discussed apptr data.
Altogether, these compositional profiles suggest that upon polymerization, bio-oils containing similarly structured chemicals will yield random heteropolymers, whereas bio-oils containing structurally diverse monomers will yield gradient, statistical, or blocky heteropolymers. The tendency for non-random monomer distributions can be avoided by applying a priori knowledge about the bio-oil's composition and the constituents’ polymerization behavior. One approach is to mix bio-oils of different compositions in a reactor via controlled feed rates. This tactic (albeit with single-component monomer streams) is employed regularly for synthesizing polymers with predetermined compositional profiles in tapered block copolymers and gradient copolymers.22–24,70–74 Extension of these methods to multicomponent bio-oils would allow for the synthesis of renewable heteropolymers with precise compositions and properties, greatly enhancing the feasibility of numerous bio-oil-based plastics for commercialization.
Conversely, potentially biobased guaiacol methacrylate derivatives that vary only in the p-position substituent all exhibited similar polymerization behavior, with nearly identical kappp's and reactivity ratios near unity. Such structurally homogeneous bio-oils are ideal for the next chapter of cost-effective sustainable materials, as minimal data are necessary for controlling polymerization behavior and estimating resulting material characteristics prior to synthesis.
These examples together illustrated that seemingly subtle variations in potentially biobased monomer structure can have a significant impact on the polymerization behavior and the resulting polymer sequence distribution. Therefore, understanding structure–reactivity relationships between monomers is imperative for avoiding the oversimplification of kinetic models to the point of inaccuracy.
Finally, although the homogenous bio-oils have characteristics that are easier to predict, the heterogeneous bio-oils and mixtures can provide more exciting pathways to designer polymers once the necessary kinetic parameters have been evaluated. In this work, the necessary kinetic data were obtained for a set of potentially biobased monomers by determining kappp and apptr, as well as using an improved method for determining ri,j. The reported equation for
apptr also indicated that unfavorable mixture compositions may exist, in which multicomponent polymers could have larger Đ's than any corresponding homopolymer (a relevant problem for sets of monomers with especially low ri,j). Overall, these mixtures and newly reported correlations will assist future designs of versatile multicomponent polymers with on-demand properties, such as strength, processability, and possibly stimuli-responsiveness or self-healing behavior.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c5py00291e |
This journal is © The Royal Society of Chemistry 2015 |