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Data-Efficient Mapping of Copolymerization Curves and Reactivity Ratios via Gradient Flow Polymerization, Inline
Araki Wakiuchi1,2, Aniruddha Nag2, Swarit Jasial2,3
1JSR Corporation, 3-103-9 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-0821, Japan.
ACS Omega
|August 11, 2026
Summary
This study introduces a data-efficient workflow for estimating copolymerization reactivity ratios using continuous-flow chemistry and inline spectroscopy. The new method reconstructs detailed polymerization data from minimal offline measurements, improving accuracy and efficiency.
Area of Science:
- Polymer Chemistry
- Chemical Engineering
- Spectroscopy
- Data Science
Background:
- Traditional reactivity-ratio estimation relies on sparse offline data, requiring numerous experiments and limiting accuracy.
- Accurate reactivity ratios are crucial for predicting and controlling copolymer composition and properties.
- Existing methods struggle with data efficiency and capturing dynamic changes during polymerization.
Purpose of the Study:
- To develop a data-efficient workflow for apparent finite-conversion reactivity-ratio estimation.
- To combine continuous-flow polymerization, inline spectroscopy, and sparse regression for dense trajectory reconstruction.
- To enable accurate estimation of reactivity ratios from minimal offline data.
Main Methods:
- Gradient continuous-flow free-radical copolymerization of styrene/methyl methacrylate.
- Inline Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy for real-time data acquisition.
- Sparse linear regression (Lasso models) calibrated with minimal offline Ultra-High-Performance Liquid Chromatography (UHPLC) data for concentration prediction.
Main Results:
- Successfully reconstructed high-density monomer composition, total conversion, and cumulative composition trajectories from limited offline data.
- Obtained apparent finite-conversion reactivity ratios (r1 = 0.33-0.48, r2 = 0.39-0.44) consistent with literature values.
- Demonstrated the workflow's ability to map copolymerization trajectories with uncertainty awareness.
Conclusions:
- The proposed workflow offers a practical and efficient route to dense, uncertainty-aware copolymerization-trajectory mapping.
- It significantly reduces the need for extensive offline measurements while improving data density and accuracy.
- This approach facilitates unified comparison of apparent finite-conversion reactivity-ratio estimates across different conditions.
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