Authors: Ramirez, Cesar
Source: Rutgers University Community Repository (Rutgers University) (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

High-throughput polymer synthesis and SAXS analysis using automation and machine learning Advancements in polymer and materials science require tools that accelerate both synthesis and characterization. Machine learning (ML) and robotics address this need by improving reproducibility, efficiency, and enabling data-driven experimental planning and analysis. This dissertation presents two integrated approaches for streamlining the design and characterization of biologically relevant synthetic polymers: (1) an automation-assisted, high-throughput platform for atom transfer radical polymerization (ATRP), and (2) a Python-based tool, SAXS Assistant, for automated analysis of small-angle X-ray scattering (SAXS) data. The ATRP platform combines automated liquid handling and experimental planning to enable rapid screening and optimization of polymerization conditions. This system facilitates the translation of complex chemistries from literature into high-throughput formats, demonstrated by successful polymerization of kinetically challenging monomers. A custom Python package, polymerization_planner, was developed to generate actionable pipetting recipes from user-defined stock concentrations, further supporting reproducibility and scalability. Although this platform was applied to ATRP, this is also applicable to reversible-addition fragmentation (RAFT) chemistry. Thus, providing a tool to serve the polymer community by supporting applications in the two most popular reversible deactivation radical polymerization (RDRP) techniques. To complement synthesis, SAXS Assistant automates the analysis of SAXS data by extracting structural parameters such as radius of gyration (Rg), maximum particle dimension (Dmax), and Kratky plots. Built using functionalities from the open-source BioXTAS RAW platform, the tool enforces data quality control through agreement between Guinier- and pair distance distribution function (PDDF)-derived Rg values. A multi-layer perceptron model trained on 1,940 experimental SAXS profiles from the SASBDB predicts Dmax with high accuracy (R² = 0.90; MAE = 11.7 Å). In parallel, a Gaussian mixture model (GMM) classifier was implemented to assign scattering profiles to structural classes, enhancing interpretability of Kratky analyses. The utility of GMM at providing new data insights was demonstrated by the study biologics including a monoclonal antibody and nanobody complexes. Furthermore, this methodology was extended to polymeric single chain nanoparticles, demonstrating its ability to provide informative structural descriptors across domains from methodology previously limited to qualitative interpretation. Together, these tools advance high-throughput experimentation and structure–function analysis in polymer science, reducing barriers to data generation and interpretation. By combining automated synthesis, and ML-guided analysis, this work lays the foundation for closed-loop experimental frameworks in biomaterials research. Small-angle X-ray scattering ; Automation ; Atom-transfer radical-polymerization ; Python (programming language) ; Radius of gyration ; Polymerization ; Polymer Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; biodegradable polymer synthesis and properties