In Situ Attenuated Total Reflectance Fourier Transform Infrared (ATR FT-IR) Spectroscopy Monitoring of 1,2-Butylene Oxide Polymerization Reaction by Using Iterative Concentration-Guided Classical Least Squares

Abstract

There has been rapid growth in the application of in situ optical spectroscopy techniques for reaction and process monitoring recently in both academia and industry. Vibrational spectroscopies such as mid-infrared, near-infrared spectroscopy, and Raman spectroscopy have proven to be versatile and informative. Accurate determination of concentrations, based on highly overlapped spectra, remains a challenge. As an example, 1,2-butylene oxide (BO) polymerization, an important industrial reaction, initiated by propylene glycol (PG) and catalyzed by KOH, is studied in this work in a semi-batch fashion by using in situ attenuated total reflectance Fourier transform infrared spectroscopy (ATR FT-IR) monitoring. The weak BO absorbance, the constantly changing interference from the product oligomers throughout the course of the reaction, and the change in BO spectral features with system polarity posed challenges for quantitative spectral analysis based on conventional methods. An iterative concentration-guided classical least-squares (ICG-CLS) method was developed to overcome these challenges. Taking advantage of the concentration-domain information, ICG-CLS enabled the estimation of the pure oligomer product spectra at different stages of the semi-batch process, which in turn was used to construct valid CLS models. The ICG-CLS algorithm provides an in situ calibration method that can be broadly applied to reactions of known order. Caveats in its applications are also discussed.

Keywords

Attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR)In situ 1,2-Butylene oxide polymerization Reaction monitoring

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References

Chew

. “Information–Theoretic Chemometric Analyses of Raman Data for Chemical Reaction Studies”. J. Raman Spectrosc. 2011. 42(1): 36–47.

Assirelli

Chew

. “Reactor Kinetics Studies via Process Raman Spectroscopy, Multivariate Chemometrics, and Kinetics Modeling”. Organic Proc. Res. Develop. 2011. 15(3): 610–621.

Furusjö

Danielsson

L.G.

Könberg

Rentsch-Jonas

Skagerberg

. “Evaluation Techniques for Two-Way Data from In Situ Fourier Transform Mid-Infrared Reaction Monitoring in Aqueous Solution”. Anal. Chem. 1998. 70(9): 1726–1734.

Aarnoutse

P.J.

Westerhuis

J.A.

. “Quantitative Raman Reaction Monitoring Using the Solvent as Internal Standard”. Anal. Chem. 2005. 77(5): 1228–1236.

Wang

Kowalik

Mizaikoff

Kranz

. “Combining Scanning Electrochemical Microscopy with Infrared Attenuated Total Reflection Spectroscopy for in Situ Studies of Electrochemically Induced Processes”. Anal. Chem. 2010. 82(8): 3139–3145.

Bell

W.C.

Booksh

K.S.

Myrick

M.L.

. “Monitoring Anhydride and Acid Conversion in Supercritical–Hydrothermal Water by in Situ Fiber-Optic Raman Spectroscopy”. Anal. Chem. 1998. 70(2): 332–339.

Dearing

T.I.

Thompson

W.J.

Rechsteiner

C.E.

Marquardt

B.J.

. “Characterization of Crude Oil Products Using Data Fusion of Process Raman, Infrared, and Nuclear Magnetic Resonance (NMR) Spectra”. Appl. Spectrosc. 2011. 65(2): 181–186.

Wentzell

P.D.

Vega Montoto

. “Comparison of Principal Components Regression and Partial Least-Squares Regression Through Generic Simulations of Complex Mixtures”. Chemometr. Intel. Lab. Sys. 2003. 65(2): 257–279.

Beebe

K.R.

Pell

R.J.

Seasholtz

M.B.

. “Multivariate Calibration and Prediction”. In: Chemometrics: A Practical Guide. New York: Wiley-Interscience, 1998. Pp. 183–337.

10.

Thomas

E.V.

Haaland

D.M.

. “Comparison of Multivariate Calibration Methods for Quantitative Spectral Analysis”. Anal. Chem. 1990. 62(10): 1091–1099.

11.

Brereton

R.G.

. “Introduction to Multivariate Calibration”. Anal. Chem. Analyst. 2000. 125(11): 2125–2154.

12.

Rajalahti

Kvalheim

O.M.

. “Multivariate Data Analysis in Pharmaceutics: A Tutorial Review”. Intl. J. Pharmaceut. 2011. 417(1, 2): 280–290.

13.

Bezemer

Rutan

S.C.

. “Multivariate Curve Resolution with Nonlinear Fitting of Kinetic Profiles”. Chemometr. Intel. Lab. Sys. 2001. 59(1, 2): 19–31.

14.

de Juan

Maeder

Martıìnez

Tauler

. “Application of a Novel Resolution Approach Combining Soft- and Hard-Modeling Features to Investigate Temperature-Dependent Kinetic Processes”. Anal. Chim. Acta. 2001. 442(2): 337–350.

15.

de Juan

Maeder

Martıìnez

Tauler

. “Combining Hard- and Soft-Modeling to Solve Kinetic Problems”. Chemometr. Intel. Lab. Sys. 2000. 54(2): 123–141.

16.

de Juan

Tauler

. “Multivariate Curve Resolution (MCR) from 2000: Progress in Concepts and Applications”. Crit. Rev. Anal. Chem. 2006. 36(3, 4): 163–176.

17.

Odian

G.G.

. “Ring-Opening Polymerization”. In: Principles of Polymerization. Hoboken, NJ: Wiley-Interscience, 2004. Pp. 545–568.

18.

Malhotra

S.L.

Leborgne

Blanchard

L.-P.

. “Ionic Polymerization of 1,2-Butylene Oxide: GPC, NMR, and IR Characterization of the Reaction Products”. J. Polymer Sci. Polymer Chem. Ed. 1978. 16(3): 561–582.

19.

Peil

K.P.

Neithamer

D.R.

Patrick

D.W.

Wilson

B.E.

Tucker

C.J.

. “Applications of High-Throughput Research at The Dow Chemical Company”. Macromol. Rapid Comm. 2004. 25(1): 119–126.

20.

Blanchard

L.P.

Dinh

K.T.

Moinard

Tahiani

. “Anionic Polymerization of 1,2-Butylene Oxide”. J. Polym. Sci., Part A-1: Polym. Chem. 1972. 10(5): 1353–1362.

21.

Gemperline

Puxty

Maeder

Walker

Tarczynski

Bosserman

. “Calibration-Free Estimates of Batch Process Yields and Detection of Process Upsets Using in Situ Spectroscopic Measurements and Nonisothermal Kinetic Models: 4-(Dimethylamino)pyridine–Catalyzed Esterification of Butanol”. Anal. Chem. 2004. 76(9): 2575–2582.