Dynamic Flow Experimentation for Data-Rich Polymer Synthesis
- 1Department of Chemical Engineering
- 2Massachusetts Institute of Technology
Abstract
Growing global challenges in healthcare, energy, sustainability, and technology have motivated increased automation and robotics to accelerate material discovery and development. Continuous droplet flow chemistry has provided a solution to non-uniform mixing and transport phenomena and provides the advantages of batch reactions for screening individual reaction conditions and reagent compositions. Dynamic experimentation in flow chemistry enables the exploration of a broad design space while minimizing the use of reagents and time by continuously varying reaction parameters (such as temperature, concentration, reaction time). Here, an automated polymer synthesis platform with dynamic experimentation capabilities was implemented. The platform leveraged syringe pumps, a custom uniform irradiance photochemical reactor with tunable temperature control, inline Fourier transform infrared spectroscopy (FTIR), and an online benchtop nuclear magnetic resonance spectrometry (NMR) at 80 MHz for automated and data-rich polymer synthesis. The combination of inline FTIR, online NMR, and offline (500 MHz) NMR enabled cross-validation between analytical techniques, and parity plots comparing data between the techniques had an R 2 value greater than 0.99 for the dynamic experiment exploring the PET-RAFT homopolymerization of methyl acrylate. Dynamic experimentation was also used to explore the copolymerization of methyl acrylate and dimethylacrylamide. The obtained rich data were used to generate accurate Gaussian process regression models for the polymerization reactions with R 2 values greater than 0.9 for parity plots of the model vs experimental monomer conversion values. The Gaussian process regression model of the copolymerization was used to understand copolymerization reaction kinetics by determining reactivity ratios under various light intensity and temperature conditions.
Related
- Autonomous Experimentation Enables Rapid Optimization of Photochemical Continuous-Flow Synthesis of Diazaxanthilidene Fluorophores
- Multimodal LLM-Assisted Diagnosis of Cryptic Failure Modes in High-Throughput Experimentation: A Case Study in Gold Nanorod Synthesis
- trans -Rich Skeletal Transformation of a Cross-Conjugated Polymer via 1,4-Selective Hydrothiolation
- Structure and Electrical Properties of Carbon-Rich Polymer Derived Silicon Carbonitride (SiCN)
- Ionic Covalent Organic Frameworks for Solid-State Polymer Electrolytes: Synthesis, Ion-Transport Regulation, and Interfacial Stabilization
BibTeX
@misc{walsh2026dynamic,
title = {{Dynamic Flow Experimentation for Data-Rich Polymer Synthesis}},
author = {Dylan J. Walsh and Katharina A. Fransen and Alexander Bentley and Federico Florit and Klavs F. Jensen and Bradley D. Olsen},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15009942/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009942/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Dynamic Flow Experimentation for Data-Rich Polymer Synthesis
AU - Dylan J. Walsh
AU - Katharina A. Fransen
AU - Alexander Bentley
AU - Federico Florit
AU - Klavs F. Jensen
AU - Bradley D. Olsen
PY - 2026
DA - 2026/10/05
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009942/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009942/v1
AB - Growing global challenges in healthcare, energy, sustainability, and technology have motivated increased automation and robotics to accelerate material discovery and development. Continuous droplet flow chemistry has provided a solution to non-uniform mixing and transport phenomena and provides the advantages of batch reactions for screening individual reaction conditions and reagent compositions. Dynamic experimentation in flow chemistry enables the exploration of a broad design space while minimizing the use of reagents and time by continuously varying reaction parameters (such as temperature, concentration, reaction time). Here, an automated polymer synthesis platform with dynamic experimentation capabilities was implemented. The platform leveraged syringe pumps, a custom uniform irradiance photochemical reactor with tunable temperature control, inline Fourier transform infrared spectroscopy (FTIR), and an online benchtop nuclear magnetic resonance spectrometry (NMR) at 80 MHz for automated and data-rich polymer synthesis. The combination of inline FTIR, online NMR, and offline (500 MHz) NMR enabled cross-validation between analytical techniques, and parity plots comparing data between the techniques had an R 2 value greater than 0.99 for the dynamic experiment exploring the PET-RAFT homopolymerization of methyl acrylate. Dynamic experimentation was also used to explore the copolymerization of methyl acrylate and dimethylacrylamide. The obtained rich data were used to generate accurate Gaussian process regression models for the polymerization reactions with R 2 values greater than 0.9 for parity plots of the model vs experimental monomer conversion values. The Gaussian process regression model of the copolymerization was used to understand copolymerization reaction kinetics by determining reactivity ratios under various light intensity and temperature conditions.
ER -