In Situ Monitoring of Emulsion Polymerization by Raman Spectroscopy

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In situ monitoring of emulsion polymerization by Raman spectroscopy: A robust and versatile chemometric analysis method Xiaoyun Chen, Ken Laughlin, Justin Sparks, Linus Linder, Vince Farozic, Hanqing Masser, and Michael Petr Org. Process Res. Dev., Just Accepted Manuscript • DOI: 10.1021/acs.oprd.5b00045 • Publication Date (Web): 01 Jul 2015 Downloaded from http://pubs.acs.org on July 7, 2015

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Organic Process Research & Development

In situ monitoring of emulsion polymerization by Raman spectroscopy: a robust and versatile chemometric analysis method Xiaoyun Chen*1, Ken Laughlin2, Justin Sparks2, Linus Linder3, Vince Farozic3, Hanqing Masser4, Michael Petr4 1

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Analytical Sciences, 1897 Bldg, The Dow Chemical Company, Midland, MI 48667.

Analytical Sciences, The Dow Chemical Company, 400 Arcola Road, Collegeville, PA 19426.

Coatings Process Technology, The Dow Chemical Company, 400 Arcola Road, Collegeville, PA 19426.

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Plastics Additives R&D, The Dow Chemical Company, 400 Arcola Road, Collegeville, PA 19426.

AUTHOR EMAIL ADDRESS: [email protected]

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Table of Contents Graphic


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ABSTRACT: Emulsion polymerization remains a challenging system for in situ Raman spectroscopic analysis, despite extensive research in the necessary instrumentation and chemometric data analysis methods. In this study we demonstrate a new and facile data analysis method, making in situ Raman spectroscopy a more versatile research tool for monitoring the concentrations of monomers in reactions spanning a wide range of compositions. The method improvement stems from the use of the homopolymer as an internal standard for the corresponding monomer. Classical least squares or indirect hard modeling is used for the spectral analysis to determine the spectral responses of major monomers and polymers within the system. Once the relative response factor ratios for a number of monomerhomopolymer pairs are determined in the calibration, they can be used to calculate the concentration ratio for such pairs based on reaction spectra. This approach offers two important advantages in determining the conversion of monomer to polymer. First, because the polymer internal standard will always be present for the corresponding monomer, it is straightforward to compensate for variable signal intensity due to changes in light scattering or instrumental fluctuations. Second, it is possible to calibrate based on a small set of monomer and homopolymer standards. The appropriate pairs can then be selected to establish a calibration method for any polymer product involving a combination of monomers from this set without the need for re-calibration. To demonstrate this technique, examples of in situ Raman monitoring for both batch and semi-batch emulsion polymerizations are provided.

KEYWORDS: in situ, Raman, emulsion polymerization, CLS, IHM


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Introduction Emulsion polymerization is a widely used process in the chemical and specialty material industry. The products of emulsion polymerization include adhesives, paints, binders, and additives to name just a few.1 The process by which an emulsion polymer is produced has a critical influence on the properties of the product and, therefore it is valuable to understand and control the emulsion polymerization reaction. One of the most important process variables in emulsion polymerization is the monomer concentration. A large number of studies have been published on monitoring monomer concentration during emulsion polymerization reactions. The methods can be broadly classified into off-line analysis, such as headspace gas chromatography (GC),2 ultraviolet-visible (UV-vis) absorption,3 and nuclear magnetic resonance (NMR), and on-line (or in situ) analysis, such as reaction calorimetry,4 near-infrared (NIR) spectroscopy.5, attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR),6 and electrical impedance spectroscopy.7 Raman spectroscopy has attracted much attention recently, as the aqueous media has almost negligible spectral contribution and the unsaturated C=C functional groups in monomers have strong Raman response.8 However, quantitative extraction of monomer concentration information from Raman spectra monitoring emulsion polymerization reactions remains challenging. Previously, univariate analysis methods based on peak height or area were used with or without normalization.8a, 8e The main drawback of this approach is that for most copolymerization systems, it is difficult to spectrally differentiate between different monomers based on only the C=C stretching mode. The other approach is to use sophisticated chemometric methods, particularly partial least squares (PLS), to establish the correlation between spectra and analyte concentration of interest, and sometimes even particle size distribution.9 Although this approach appears to yield relatively successful quantitation results when used appropriately, extensive calibration effort is usually needed for every reaction recipe, and a change in the reaction conditions, such as co-monomer ratio adjustment, often requires re-calibration. This PLS approach is especially onerous for research efforts, where the goal is to ACS Paragon Plus Environment

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change many variables to optimize the product microstructure, reaction rate, or yield for example. Multivariate curve resolution is another powerful technique for the analysis of spectra with overlapping bands.10 It has enabled the extraction of pure component spectra directly from reaction spectra and often can provide useful reaction kinetics with minimal calibration effort. However, proper data augmentation and/or constraints in the spectral or concentration domain are critical for successful quantitative implementation in most practical applications such as emulsion polymerization monitoring. The goal of this study is to demonstrate a new framework of data analysis using appropriate normalization with monomer/polymer pairs enabled by classical least squares (CLS)11 or indirect hard modeling (IHM)12 analysis. Despite the extensive use of CLS in the analysis of model multicomponent mixture systems and in fields such as pharmaceutical and biomedical analysis,13 CLS analysis has been utilized on a rather limited basis for emulsion polymerization reaction monitoring. CLS analysis in the C=C stretch range was used to monitor n-butyl acrylate and vinyl neononanoate copolymerization after normalization by the integrated area of 550-650 cm-1 which remained invariant during the reaction.14 However, such an approach is not general because many copolymerization systems do not have an invariant Raman spectral region for normalization purposes, and the C=C region alone is ambiguous for CLS analysis when similar types of monomers are used. In this study it is shown that, by applying CLS (or IHM) over a broader spectral range beyond the C=C stretch region, the response from both the monomer and polymer can be calculated, and they can serve as internal standards for each other. The new method proposed for the analysis of in situ Raman data is based on normalized CLS (or IHM) responses, as shown in Equations 1 and 2,15 where I stands for peak intensity or CLS/IHM response, the effective Raman cross-section, the effective focal volume, the concentration of the corresponding species, and the instrumentation function, which includes laser power, collection efficiency, detector response, etc. As an emulsion polymerization proceeds, the turbidity, and thus the effective probed volume of a Raman immersion optic changes as the particles form and grow. Therefore, the intensity at any given time during the reaction cannot be correlated to a concentration ACS Paragon Plus Environment

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unless the probed volume is explicitly known. This uncertainty in the probed volume is one of the major challenges in quantitative in situ monitoring of emulsion polymerizations with optical techniques such as Raman spectroscopy. However, the normalization technique presented here (Equation 2) effectively removes the uncertainty in which depends on particle size distribution, and system turbidity, as well as uncertainty in , including laser fluctuation and other experimental variables. Once the relative response ratios ( ) for a number of monomer-homopolymer pairs are determined through a calibration

process, they can be used to calculate the concentration ratio for these pairs based on reaction spectra. To emphasize the general applicability of the normalization technique, two different spectral analysis methods were employed: CLS spectral analysis was used to process the data from a typical batch emulsion polymerization reaction, while IHM spectral analysis was used for the data from a typical semi-batch reaction. The same normalization strategy was used for both examples. =

=

Eq. 1 Eq. 2

This approach offers two important advantages which give the method a broad range of applicability in determining the conversion of monomer to polymer. First, it is straightforward to compensate for variable signal intensity due to changes in light scattering or instrumental fluctuations by using the polymer internal standard. In contrast to the PLS or principle component regression (PCR) approaches, which implicitly model the influence of factors not related to monomer concentration such as particle size distribution, the method proposed here explicitly accounts for such influence and is thus expected to be more robust. Such a method can also be more broadly applied to other heterogeneous systems where the denominator can be another monomer or a solvent.16 Second, it is possible to calibrate based on a small set of monomer and homopolymer standards. The appropriate pairs can then be selected to establish a model for any polymer product involving a combination of monomers from this set. No recalibration will be needed as long as the spectra and response ratio are known for the major monomerACS Paragon Plus Environment

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polymer pairs involved. Therefore, process changes, such as particle size distribution, agitation rate, and composition, are well-tolerated.

Experimental Section Raman spectroscopy Two different Raman systems were used in this study to illustrate the robustness and general applicability of the data analysis method. A B&W Tek iRaman Plus system with 300 mW 785 nm laser excitation was used to monitor a batch reaction. A Kaiser RXN2 Raman system with 400 mW 785 nm laser excitation was used to monitor a semi-batch reaction. A half-inch diameter short-focus Kaiser immersion optic with a sapphire window was used to collect signals in the back scattering geometry for both systems. An adaptor was made to couple the Kaiser immersion optic to the B&W Tek Raman probehead. No fouling of the sapphire window was observed in any experiment. Typical spectral collection conditions were 1 min total acquisition time (5 s exposure with 12 accumulations), continuous collection with about 1 s instrument overhead time between consecutive spectra, and roughly 4 cm-1 spectral resolution for both systems. The maximum laser power was used for all experiments. Batch emulsion polymerization reaction All monomers were supplied by The Dow Chemical Company, and all other reagents were supplied by Sigma-Aldrich with >98% purity and used as received. A batch emulsion polymerization reaction was carried out in an insulated polymerization reactor equipped with a reflux condenser, thermometer, nitrogen purge, and agitator. A monomer emulsion was prepared from methyl methacrylate (MMA), butyl acrylate (BA), deionized water, surfactant, and salt. It was added to the flask, along with the redox initiator catalyst and extra water, and sparged with nitrogen for an hour. Following the sparge, the polymerization was initiated at 25°C under a nitrogen blanket with a redox free radical initiator system


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coupled with an oxygen scavenger. The system temperature gradually rose to about 85 °C due to the reaction exotherm. This reaction was monitored by the B&W Tek iRaman Plus system. Semi-batch emulsion polymerization reaction A monomer mixture was prepared from BA, MMA, and methacrylic acid (MAA), and an initiator solution was prepared from a thermal free radical initiator and deionized water. MMA spectral contribution was negligible due to its concentration. Initially, deionized water and surfactant were added to a polymerization reactor equipped with a reflux condenser, thermometer, nitrogen purge, agitator and addition funnel. The initial reactor contents were heated to 85°C under a nitrogen blanket and agitation, and then a small portion of both the initiator solution and the monomer mixture were added. The reaction was held for 15 minutes while initiation took place, and then the remainder of the monomer mixture and the initiator solution were fed over 1.5 hours. This reaction was monitored by the Kaiser RXN2 system.

Offline 1H NMR Samples were withdrawn from the reactor and immediately quenched with 4-hydroxy-tempo for offline analysis. Offline 1H NMR was carried after stripping off the water and unreacted monomers. The dry polymer was then dissolved in chloroform-d (CDCl3) for 1H NMR analysis to determine the ratio of total BA to total MMA in the copolymer. Gas chromatography GC analysis was carried out using an Agilent 6890 GC with FID Detector and G1888 Headspace auto-sampler. Samples were diluted with an aqueous solution of internal standard as needed based on the calibration range of the method. To a 20 mL headspace vial was added 20-30 mg of diluted latex containing internal standard and sealed with a crimped septum. The vials were heated to 130°C for 10 minutes before fixed loop sampling the headspace to the gas chromatograph. Calibration was done similarly with monomer and internal standards to generate calibration curves using Agilent GC ChemStation software which was also used to process the chromatographic data. Data analysis ACS Paragon Plus Environment

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OMNIC (Thermo Scientific) was used for spectral data visualization and manipulation. For the batch reaction, CLS analysis was carried out using MATLAB (Mathworks). For the semi-batch reaction, indirect hard modeling analysis was used using the PEAXCT software package (version 3.6.3 S.PACT GmbH).

Results and Discussion Calibration experiments A series of mixtures of BA and polyBA (PBA) emulsions as well as MMA and polyMMA (PMMA) emulsions were prepared, and their Raman spectra are shown in Figure 1. Note that a small amount of monomer was still present in the polymer emulsions due to incomplete reaction, and that all concentrations were expressed as weight %. The polymer emulsion spectra, after subtracting the monomer contribution (based on nulling out the C=C stretch peak around 1640 cm-1), and the monomer emulsion spectra were used as the pure component spectra in the CLS models. CLS analysis was carried out for the spectral range of 780-1040 cm-1 and 1100-1800 cm-1 with a gap of 1040-1100 cm-1 to avoid a temperature-dependent sapphire window band caused by photoluminescence. Note that several other pure component spectra, such as water, room light, the sapphire window, and surfactant may also be included in the CLS model, but they were insignificant in this work due to their low signals. Finally, linear baseline correction was the only spectral pre-processing by including a row of ones and a row of consecutive integers in the pure component spectra matrix.17 The CLS analysis results for the MMA and BA polymerization calibration systems are summarized in Table 1. The monomer and polymer concentrations are plotted against their CLS responses for the BA/PBA pair and the MMA/PMMA pair in Figure 2 (a) and (b), respectively. Because the monomer emulsion and the polymer emulsion were defined as the two pure components, by definition, the monomer CLS response is 1 for sample a (neat monomer emulsion) and close to 0 for sample f ACS Paragon Plus Environment

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(polymer emulsion), and the polymer CLS response is 0 for sample a and 1 for sample f. For an ideal system all the data points in Figure 2 should fall onto the diagonal line connecting the origin to the data point of pure component spectra (highlighted in Figure 2 with bolded outlines). It is interesting to note that, while this trend was observed for the polymer correlation, a linear correlation was not observed between the monomer CLS response and the monomer concentration. These observations strongly suggest that, while a linear behavior as shown in Eq. 1 may be sufficient to describe the intensityconcentration correlation for submicron particles dispersed in a medium, such as in a polymer emulsion, this linear relationship breaks down in an aqueous dispersion with larger particle sizes. For example, it is interesting to note that sample b (34.68 % MMA) in the MMA-PMMA series actually has a slightly stronger C=C peak and a correspondingly larger monomer response than sample a (43.2 % MMA). In this case, the presence of the polymer led to a larger monomer CLS response. This can be rationalized by considering the fact that the large droplet size in the monomer emulsion can be effectively reduced when the monomer is absorbed into polymer particles. Such behavior was reproducibly observed in all the monomer-polymer pairs to varying degrees. Furthermore, it is well known that absolute Raman intensity depends on many factors including solution turbidity and the particle size distribution of the dispersed phase. The results presented above imply that quantitative methods directly relating absolute Raman intensity to concentrations will likely have large errors in monomer concentration estimation. To account for this effect, normalization of the monomer CLS response by the polymer response is found to be an effective approach to remove most of the particle-size dependence, as shown in Figure 2 (c) and (d). The ratio of the monomer to polymer concentration exhibited a linear relationship to the ratio of the monomer to polymer CLS response, and the slope of the linear fit shown in Figure 2 (c) and

(d) provides the value for the relative response ratio ( in Equation 2).

The concentration ratio represents the conversion of monomer to polymer. The absolute concentrations of both monomer and polymer can be calculated from the conversion if the total concentration of a monomer-polymer pair is known. For a batch reaction, nothing is fed into the reactor over the course of ACS Paragon Plus Environment

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the reaction, and the total concentration is the same as the starting monomer concentration. For a semibatch (or starved-feed) emulsion polymerization reaction, the total monomer + polymer concentration varies throughout a reaction, but is usually known based on the feed rates of the monomer(s).

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Figure 1. Raman spectra from BA/PBA (top) and MMA/PMMA (bottom) calibration emulsion mixtures. See Table 1 for details of sample a-f. Spectra are offset for clarity.


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Table 1. Composition of the calibration samples and their CLS responses.

system sample [monomer]

BA -

monomer

polymer

CLS

CLS

CLS

response

concentration

[polymer]

response

response

ratio

ratio

a

43.20%

0.00%

1.00

0.00

b

35.26%

7.94%

0.97

0.13

7.42

4.44

c

27.32%

15.88%

1.03

0.34

2.98

1.72

d

19.38%

23.82%

0.78

0.56

1.38

0.81

e

11.44%

31.76%

0.48

0.80

0.60

0.36

f

3.50%

39.70%

0.15

1.00

0.15

0.09

a

43.20%

0.00%

1.00

0.00

b

34.68%

8.52%

1.12

0.14

8.14

4.07

c

26.16%

17.04%

1.10

0.37

2.94

1.54

d

17.64%

25.56%

0.77

0.59

1.31

0.69

e

9.12%

34.08%

0.39

0.81

0.48

0.27

f

0.60%

42.60%

0.03

1.00

0.03

0.01

PBA pair

MMA PMMA pair


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Figure 2. Correlation between the monomer and polymer emulsion concentration and the corresponding CLS responses for the BA-PBA (a) and MMA-PMMA (b) system. The points with bolded symbol are from the pure component spectrum samples. Correlation between the monomer to polymer emulsion concentration ratio and the corresponding CLS response ratios for the BA-PBA (c) and MMA-PMMA (d) system.

The above method was applied to analyze a series of validation samples composed of BA/PBA/MMA/PMMA emulsion. The total solid content was kept at 43.2 wt% while the relative concentrations of the monomer emulsion and polymer emulsion were varied to simulate various system compositions for validation of monomer differentiation. The compositions of the validation samples and the CLS analysis results are summarized in Table 2. As discussed above, the absolute CLS responses are of little meaning, therefore, only the concentration ratios (actual and predicted) are compared in Table 2. Among the three ratios calculated, the largest discrepancy between CLS prediction and actual ratio was ACS Paragon Plus Environment

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observed for the BA/PBA. It is believed that the similarity between the PMMA and PBA caused spectral ambiguity, which led to an under-estimation of PBA and an over-estimation of PMMA. For sample d in Table 2 the CLS method even predicted a negative PBA concentration which was not physically possible. On the other hand, the MMA/PMMA ratio was found to be consistently underestimated, which is consistent with the over-estimation of the PMMA CLS response. Even with these errors, relatively good agreement was found for the predicted and actual BA/MMA ratio. Table 2. Comparison of the concentration ratios and CLS response ratios of validation samples. concentrations (wt%)

validation

concentration ratio

CLS predicted ratio

samples

BA

PBA

MMA

PMMA

BA/PBA

MMA/PMMA

BA/MMA

BA/PBA

MMA/PMMA

BA/MMA

a

9.0%

4.0%

8.9%

21.3%

2.26

0.42

1.00

3.64

0.37

0.89

b

11.7%

9.9%

11.0%

10.6%

1.17

1.03

1.07

1.31

0.89

0.88

c

2.3%

6.6%

2.2%

32.1%

0.35

0.07

1.06

0.38

0.06

0.97

d

7.1%

1.6%

17.5%

17.0%

4.43

1.03

0.40

-9.77

0.90

0.37

A Batch Reaction A batch reaction was carried out with 35% MMA and 7.7% BA as the starting composition. The raw (not normalized) CLS responses for this reaction are shown in Figure 3 (a). BA and MMA monomer emulsion were fed into the reactor in the first two minutes and initiator feeding was started soon after. It is interesting to note that both the BA and MMA CLS responses started to increase after about 0.4 hr, concurrently with the increase of PMMA CLS response. This is consistent with the observations made in the calibration experiments and likely due to the smaller particle size and/or system turbidity change and the multiplicative scatter effect. No significant PBA response was observed until roughly 1.6 hr into the reaction. Figure 3 (b) shows the normalized results. The calibration established in Figure 2 (c) and (d) was used to convert the CLS response to concentration ratio of BA/PBA and MMA/PMMA, which in turn was used to calculate the percentage of remaining BA and MMA. The BA/MMA weight ratio was directly calculated based on the BA and MMA CLS response ratio using a single-point calibration, which was possible because the starting concentration ratio was known to be 0.22. For off-line ACS Paragon Plus Environment

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validation, NMR analysis was performed based on samples withdrawn periodically for a previous reaction carried out using the exact same procedure. The Raman prediction results are compared to the NMR results in Figure 3 (b). The trends predicted by Raman are generally consistent with those determined by NMR for the MMA conversion and BA/MMA ratio. A significant deviation was observed for the BA conversion prior to 2.3 hr. This is believed to be mainly from the weak PBA signals in the early stage of this reaction, which is consistent with the observations made in the validation experiments as shown in Table 2. On the other hand, the BA/MMA ratio is one of the most important process variables as it determines the instantaneous chemical composition distribution of the product, and good agreement was found between the NMR and Raman results. The BA/MMA ratio increased steadily throughout the batch, from 0.22 initially to about 0.4 after 2 hours, and increasing further after that. This observation is qualitatively expected based on the reactivity ratios of the two monomers.18 MMA reacts preferentially with polymer chain radicals, regardless of whether the radical comes from MMA or BA addition. The changing monomer ratio results in a substantial drift in the instantaneously produced polymer composition. The radical lifetime is typically very short, about 1 second or less, and the polymer chains produced late in the reaction have substantially higher BA content.18


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a)

1 BA

0.9

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MMA CLS Response (a.u.)

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PMMA

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1.00

b)

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60%

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BA/MMA Ratio

Monomer Conversion (%)

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0.25 20%

BA/(BA+PBA) BA/(BA+PBA) - NMR BA/MMA - NMR

MMA/(MMA+PMMA) MMA/(MMA+PMMA) - NMR BA/MMA

0.00

0% 0

0.5

1

1.5

2

2.5

Time (hr)

Figure 3. (a) Raw CLS responses for a batch reaction. (b) Percent of unreacted BA and MMA (left axis) and ratio of BA/MMA concentration (right axis).

For a batch polymerization, the system temperature usually increases as the reaction proceeds due to the reaction exotherm. Temperature is another factor that could influence the CLS analysis if the pure component spectra were collected at a temperature different from reaction temperatures. Figure 4 (a) and (b) show the temperature influence on a MMA/PMMA mixture and a BA/PBA emulsion, respectively, in the C=C and C=O stretch spectral range (sample c in Table 1). Higher temperature led ACS Paragon Plus Environment

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to not only a decrease in absolute signal intensity, which is expected partly due to lower number molecule density as a result of thermal expansion, but also a change in peak position and peak width.19 While the absolute signal intensity change can be accounted for by the normalization process outlined above, the spectral feature changes are expected to lead to error in the CLS analysis. One way to overcome this is to use the monomer emulsion and polymer emulsion spectra at corresponding temperatures as input for the CLS model, but this is beyond the scope of this work. Exclusion of the C=O stretch bands in the CLS analysis may improve the quantitation results.

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Figure 4. Raman spectra of the mixture calibration sample c in Table 1 at 20°C, 50 °C, 80 °C , and then back to 20 °C for (a) the MMA/PMMA pair (sample C), and (b) the BA/PBA pair (sample C).

A Semi-batch Reaction In cases where pure component spectra are not easily obtained, the IHM method can be employed, which is analogous to the CLS method used above but more suited for this situation because it allows the peak positions to shift. To demonstrate that the normalized IHM monomer/polymer response results are accurate, a semi-batch emulsion BA and MMA co-polymerization reaction was carried out. Since no pure PMMA or PBA emulsion was available at the time, pure component spectra were collected from neat PMMA and PBA polymer (from Sigma Aldrich) films prepared by solvent casting. This data was then fed into the IHM model.12, 20 The raw IHM responses for the two monomers and polymers are shown in Figure 5 (a), and the monomer concentration based on the normalized IHM responses are shown in Figure 5 (b). It is interesting to note that the overall monomer profiles in Figure 5 (a) and (b) resemble each other, in contrast to the substantial difference observed between Figure 3 (a) and (b). This is believed to be due to the presence of polymer emulsion from the beginning, which prevented the formation of large monomer droplets. This example demonstrates the utility of Raman spectroscopy in providing real time information because it shows a whole series of reaction events in real time. The initial monomer mix was added shortly after 30 min. Monomer concentrations stayed relatively stable until the addition of initiator, which immediately caused the monomer concentrations to drop precipitously. A constant monomer mix feed together with an initiator feed were started soon after. Then the monomer mix feed rate was doubled at 56 min, which led to a higher pseudo-steady state monomer concentration for both BA and MMA, and finally, the monomer mix feed was completed at 110 min. Inhibitor was added to the reactor at 135 min in order to carry out monomer spiking tests to determine the effectiveness in distinguishing between the two monomers. Each monomer spike was 0.45 wt%; however, the first several additions of ACS Paragon Plus Environment

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monomer only led to transient accumulation of monomers, indicating that the inhibition was not sufficient. The last spikes of MMA and BA monomers did lead to stable responses, indicating that sufficient inhibition was achieved. From these final spikes, the standard deviation was calculated to be approximately 0.02 wt% based on the concentration plateau between 214 and 226 min, and this low standard deviation indicates that good signal-to-noise was obtained with 1 minute acquisition time. As an off-line cross check, the IHM analysis results were compared to the headspace GC analysis results in Figure 5 (b). Both methods showed similar trends; however, the absolute values of the Raman/IHM prediction results were higher than that of GC. One explanation is that further reaction took place in the withdrawn GC samples because the Raman and GC methods yielded similar results for the highly inhibited samples at 210 and 220 minutes.


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[MMA] [MMA]GC

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Figure 5. (a) Raw IHM responses for the semi-batch reaction. The monomer and polymer responses are plotted on the left and right axes, respectively. (b) BA and MMA concentration predicted based on the normalized Raman IHM responses and comparison to off-line head-space GC analysis results. Vertical lines indicate the major steps of the reaction.

Discussion The biggest advantage of the method described herein lies in the ease of calibration and the robustness of the method. The widely used PLS or PCR method usually requires a large number of calibration standards to cover the range of operation conditions, including temperature, monomer and polymer concentrations, and particle size distribution.8d, 8e, 9 Changes in recipe or process conditions will often ACS Paragon Plus Environment

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necessitate laborious re-calibration. Conversely, the CLS and IHM spectral analysis methods are more robust and can generally tolerate changes as long as no new species are introduced into the system. For example, surfactants are also often present in emulsion polymerization reactions, though their concentrations were negligibly low in the examples included here. However, if a surfactant’s Raman signal became detectable in the range of spectral analysis, its pure component spectra collected at experimental conditions should also be included in the CLS or IHM analysis. If the concentration of the surfactant is of no interest, then no calibration is needed. Model maintenance can thus be greatly simplified. As demonstrated in the results based on the calibration samples, variation in particle size distribution is expected to change the absolute Raman intensity, although the exact size-dependence was not systematically studied here. Such variation has to be implicitly modeled in PLS or PCR models. The polymer CLS response served as a highly effective internal standard (see Figure 2), which should be utilized. It is unlikely that a PLS or PCR model can account for the non-linear correlation between the C=C band intensity and the concentration without the use of such an internal standard, and typical normalization methods employed in PLS or PCR analysis, such as multiplicative scatter correction, standard normal variate, or normalization to the strongest peak or unit area do not offer a solution.21 For example, if monomer concentrations, as shown in Table 1, are used as the input for dependent variables and the spectra of the calibration samples are used as the input for independent variables, it is difficult for the PLS or PCR algorithm to implicitly account for the non-linear and non-monotonic correlation between the C=C peak and the monomer concentration (see Figure 2). Previous normalization approaches have failed in the past because a good internal standard is required. Water was previously proposed as an internal standard;22 however, the weak water signal and the fact that water does not co-localize with the polymer and monomer phase make it a poor internal standard. The approach taken here with CLS or IHM spectral analysis enabled the use of the monomer or polymer response as the internal standard. The exact choice of the species for internal standard depends on which ACS Paragon Plus Environment

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species can be most reliably quantified. For the batch-reaction example above, the monomer concentration remained high except for the last stage of the reaction and thus was used as the internal standard. For the semi-batch reaction example, the concentrations of both polymers remained high, so they were used as the internal standard for the corresponding monomer. Based on the batch reaction and semi-batch examples given above, the typical error in monomer concentration estimation was found to be on the order of one percent (absolute) or less, with the precision to be on the order of 0.02 wt%. The error depends on the spectral similarity of all the components involved. While a smaller error may be possible by using PLS and extensive calibration, whether or not the accuracy thus achieved justifies the effort of extensive calibration depends on the requirements of each application. Finally, two examples are presented, but indeed the general methodology described herein has been applied to more than ten different Dow proprietary processes and found to be robust. This fact further demonstrates the key advantage of this method, which is the ease of model construction and calibration enabled by the CLS or IHM model. Still, it is important to remember that the monitoring metrics of any individual application of this method, such as accuracy, precision, and detection limit, are systemdependent. For example, the detection limit for styrene is much lower than that of many of the acrylic monomers due the greater Raman cross-section of styrene.

Conclusion The use of internal homopolymer or monomer standards for in situ Raman facilitates its use in emulsion polymerization. Three sets of Raman data from calibration samples, a batch reaction, and a semi-batch reaction were analyzed by CLS or IHM. It was shown that the absolute Raman signal intensity is sensitive to particle size distribution which can lead to error in methods that rely on absolute intensities. It was also demonstrated that, through the use of an internal standard, the normalized Raman responses (CLS or IHM responses) are mostly independent of the particle size distribution and many other process ACS Paragon Plus Environment

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dependent variables; therefore, it can be used for quantification of monomer and polymer concentrations. The Raman results as analyzed by the CLS or IHM methods were consistent with those determined by NMR and GC, confirming their reliability.

Acknowledgement. The authors are grateful for the help with the semi-batch reactions by Scott Brockett, Timothy Capparella, Anthony Tiano, Bruce King, Travis McIntire, for the help with GC analysis from Grant Von Wald and Bhavin Patel, for help with NMR from Xiaohua Qiu, Herb Praay, and Jim DeFelippis, for the inspiring discussion from Anne Leugers and Mark Rickard, and for the help with instrument from Sharon Deram (SBI Analytical) and Jennifer Howard (Kaiser Optical).

References 1.

Asua, J. M. J. Polym. Sci., Part A: Polym. Chem. 2004, 42, 1025-1041.

2.

Zhong, J.-F.; Chai, X.-S.; Fu, S.-Y.; Qin, X.-L. J. Appl. Polym. Sci. 2012, 124, 3525-3528.

3.

Alb, A. M.; Reed, W. F. Macromolecules 2008, 41, 2406-2414.

4.

(a) Elizalde, O.; Azpeitia, M.; Reis, M. M.; Asua, J. M.; Leiza, J. R. Ind. Eng. Chem. Res. 2005,

44, 7200-7207; (b) BenAmor, S.; Colombié, D.; McKenna, T. Ind. Eng. Chem. Res. 2002, 41, 42334241. 5.

(a) Chicoma, D.; Carranza, V.; Sayer, C.; Giudici, R. Macromol. Symp. 2010, 289, 140-148; (b)

Chicoma, D. L.; Sayer, C.; Giudici, R. Macromol. React. Eng. 2011, 5, 150-162. 6.

Dubé, M. A.; Li, L. Polym. Plast. Technol. Eng. 2010, 49, 648-656.

7.

Ghasemi, S.; Darestani, M. T.; Abdollahi, Z.; Gomes, V. G. Polym. Int. 2014, n/a-n/a.

8.

(a) Feng, L.; Ng, K. Y. S. Macromolecules 1990, 23, 1048-1053; (b) Wang, C.; Vickers, T. J.;

Schlenoff, J. B.; Mann, C. K. Appl. Spectrosc. 1992, 46, 1729-1731; (c) Pepers, M. On-line monitoring and control of copolymerizations: a road map for the use of non-invasive on-line Raman spectroscopy. ACS Paragon Plus Environment

23


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 25

PhD. Dissertation, Eindhoven University of Technology, Eindhoven, Netherlands, 2004; (d) Bauer, C.; Amram, B.; Agnely, M.; Charmot, D.; Sawatzki, J.; Dupuy, N.; Huvenne, J.-P. Appl. Spectrosc. 2000, 54, 528-535; (e) Reis, M. M.; Uliana, M.; Sayer, C.; Araújo, P. H. H.; Giudici, R. Braz. J. Chem. Eng. 2005, 22, 61-74; (f) Al-Khanbashi, A.; Hansen, M. G.; Wachter, E. A. Appl. Spectrosc. 1996, 50, 10891092; (g) Brink, M. v. d. On-line Monitoring of Polymerizaiton Reactions by Raman Spectrosocpy: Applicaiton to Control of Emulsion Copolymerizations and Copolymerization Kinetics PhD. Dissertation, Eindhoven University of Technology, Eindhoven, Netherlands, 2000; (h) Ludwig, I. Drying, film formaiton and open time of aqueous polymer dispersions PhD. Dissertation, University of Karlsruhe, Karlsruhe, Germany, 2008. 9.

(a) Elizalde, O.; Leiza, J. R.; Asua, J. M. Macromol. Symp. 2004, 206, 135-148; (b) Elizalde, O.;

Asua, J. M.; Leiza, J. R. Appl. Spectrosc. 2005, 59, 1280-1285. 10.

(a) Jiang, J.-H.; Ozaki, Y. Appl. Spectrosc. Rev. 2002, 37, 321-345; (b) de Juan, A.; Tauler, R.

Crit. Rev. Anal. Chem. 2006, 36, 163-176; (c) Garrido, M.; Rius, F. X.; Larrechi, M. S. Anal. Bioanal. Chem. 2008, 390, 2059-2066; (d) Pell, R. J.; Chen, X. J. Chemom. 2014, 28, 411-419. 11.

(a) Haaland, D. M.; Easterling, R. G. Appl. Spectrosc. 1982, 36, 665-673; (b) Haaland, D. M.;

Easterling, R. G.; Vopicka, D. A. Appl. Spectrosc. 1985, 39, 73-84; (c) Haaland, D. M.; Easterling, R. G. Appl. Spectrosc. 1980, 34, 539-548. 12.

(a) Alsmeyer, F.; Koß, H.-J.; Marquardt, W. Appl. Spectrosc. 2004, 58, 975-985; (b) Kriesten,

E.; Alsmeyer, F.; Bardow, A.; Marquardt, W. Chemom. Intell. Lab. Syst. 2008, 91, 181-193; (c) Kriesten, E.; Mayer, D.; Alsmeyer, F.; Minnich, C. B.; Greiner, L.; Marquardt, W. Chemom. Intell. Lab. Syst. 2008, 93, 108-119. 13.

(a) Ravn, C.; Skibsted, E.; Bro, R. J. Pharm. Biomed. Anal. 2008, 48, 554-561; (b) Notingher, I.;

Jell, G.; Notingher, P. L.; Bisson, I.; Tsigkou, O.; Polak, J. M.; Stevens, M. M.; Hench, L. L. J. Mol. Struct. 2005, 744, 179-185. 14.

van den Brink, M.; Pepers, M.; van Herk, A. M.; German, A. L. Polym. React. Eng. 2001, 9,

101-133. ACS Paragon Plus Environment

24

Page 25 of 25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


15.

Pelletier, M. J. Appl. Spectrosc. 2003, 57, 20A-42A.

16.

Chen, X.; Meece, C.; Gonzalez, M.; Chiang, H.; Qiu, X.; He, Y.; Rickard, M. A.; Friedhoff, D.

Org. Process Res. Dev. 2015, 19, 139-144. 17.

Haaland, D. M.; Melgaard, D. K. Appl. Spectrosc. 2000, 54, 1303-1312.

18.

Elizalde, O.; Arzamendi, G.; Leiza, J. R.; Asua, J. M. Ind. Eng. Chem. Res. 2004, 43, 7401-

7409. 19.

Witjes, H.; van den Brink, M.; Melssen, W. J.; Buydens, L. M. C. Chemom. Intell. Lab. Syst.

2000, 52, 105-116. 20.

Alsmeyer, F.; Marquardt, W. Appl. Spectrosc. 2004, 58, 986-994.

21.

Rinnan, Å.; Berg, F. v. d.; Engelsen, S. B. TrAC, Trends Anal. Chem. 2009, 28, 1201-1222.

22.

Wang, C.; Vickers, T. J.; Mann, C. K. Appl. Spectrosc. 1993, 47, 928-932.


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In Situ Monitoring of Emulsion Polymerization by Raman Spectroscopy

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