Copolymerization curves and reactivity-ratio estimates are traditionally derived from sparse offline data collected across many separate experiments. Here, we propose a dataefficient workflow for apparent finite-conversion reactivity-ratio estimation that combines gradient continuous-flow free-radical copolymerization, inline ATR-FTIR spectroscopy, and sparse linear regression to reconstruct dense concentration and composition trajectories from minimal offline quantitation. Using styrene/ methyl methacrylate as a benchmark system, we performed a timeprogrammed zigzag sweep of the styrene/MMA feed ratio at constant total flow and acquired more than 700 inline ATR-FTIR spectra in a single ∼4 h run at each of 70, 80, and 90 °C. Only ten offline UHPLC anchor points per run were used to calibrate Lasso models for monomer concentration prediction, enabling highdensity reconstruction of the reference monomer composition f1(t), total conversion Xtot(t), and cumulative consumption-based copolymer-composition proxy F1,cum(t). The reconstructed finite-conversion trajectories were analyzed using a Skeist-type finiteconversion terminal-model formulation, in which the Mayo−Lewis equation was used as the instantaneous composition relation. The maximum Xtot values spanned 0.36−0.62, and the full-data IR-assisted estimates spanned r1 = 0.33−0.48 and r2 = 0.39−0.44, remaining within the broad range of benchmark literature values for this system. Residual-based Monte Carlo sampling showed that pointwise concentration-prediction uncertainty led to compact r1−r2 ensembles, whereas block-removal and UHPLC-only subset analyses revealed sensitivity to sparse-anchor selection, most notably at 70 °C. Because the workflow uses finite-conversion trajectory data, aligned reference/reacting concentration differences, and this F1,cum proxy, the reported values are interpreted as apparent finite-conversion estimates under the present flow and analysis workflow. This approach provides a practical route to dense, uncertainty-aware copolymerization-trajectory mapping from minimal offline measurements and enables unified comparison of apparent finite-conversion reactivity-ratio estimates across temperatures.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)