Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning.

We report a human-in-the-loop implementation of the multi-objective experimental design via a Bayesian optimization platform (EDBO+) towards the optimization of butylpyridinium bromide synthesis under continuous flow conditions. The algorithm simultaneously optimized reaction yield and production rate (or space-time yield) and generated a well defined Pareto front. The versatility of EDBO+ was demonstrated by expanding the reaction space mid-campaign by increasing the upper temperature limit. Incorporation of continuous flow techniques enabled improved control over reaction parameters compared to common batch chemistry processes, while providing a route towards future automated syntheses and improved scalability. To that end, we applied the open-source Python module, nmrglue, for semi-automated nuclear magnetic resonance (NMR) spectroscopy analysis, and compared the acquired outputs against those obtained through manual processing methods from spectra collected on both low-field (60 MHz) and high-field (400 MHz) NMR spectrometers. The EDBO+ based model was retrained with these four different datasets and the resulting Pareto front predictions provided insight into the effect of data analysis on model predictions. Finally, quaternization of poly(4-vinylpyridine) with bromobutane illustrated the extension of continuous flow chemistry to synthesize functional materials.

Deep Learning of Binary Solution Phase Behavior of Polystyrene.

Predicting binary solution phase behavior of polymers has remained a challenge since the early theory of Flory-Huggins, hindering the processing, synthesis, and design of polymeric materials. Herein, we take a complementary data-driven approach by building a machine learning framework to make fast and accurate predictions of polymer solution cloud point temperatures. Using polystyrene, both upper and lower critical solution temperatures are predicted within experimental uncertainty (1-2 °C) with a deep neural network, Gaussian process regression (GPR) model, and a combination of polymer, solvent, and state features. The GPR model also enables intelligent exploration of solution phase space, where as little as 25 cloud points are required to make predictions within 2 °C for polystyrene of arbitrary molecular weight in cyclohexane. This study demonstrates the effectiveness of machine learning for the prediction of liquid-liquid equilibrium of polymer solutions and establishes a framework to incorporate other polymers and complex macromolecular architectures.

Uniaxial Deformation and Crazing in Glassy Polymer-Grafted Nanoparticle Ultrathin Films.

The deformation behavior of neat, glassy polymer-grafted nanoparticle (PGN) monolayer films is studied using coarse-grained molecular dynamics simulations and experiments on polystyrene-grafted silica. In both the simulations and experiments, apparent crazing behavior is observed during deformation. The PGN systems show a relatively more uniform, perforated sheet craze structure and significantly higher strain at break than reference homopolymers of the same length. Short chain, unentangled PGN monolayers are also simulated for comparison; these are brittle and break apart without crazing. The entangled PGN simulations are analyzed in detail for systems at both high and moderate graft density. Stress-strain curves show three distinct regions: yielding and strain localization, craze widening, and strain hardening preceding catastrophic failure. The PGN stress-strain behavior appears more similar to that of longer chain, highly entangled homopolymer films than to the reference homopolymer films of the same length as the graft chains, suggesting that the particles effectively add additional entanglement points. The moderate graft density particles have higher strain-to-failure and maximum stress than the high graft density particles. We suggest this increased robustness for lower graft density systems is due to their increased interpenetration of graft chains between neighboring particles, which leads to increased interparticle entanglements per chain.

Modeling individual and pairs of adsorbed polymer-grafted nanoparticles: structure and entanglements.

We analyze the canopy structure and entanglement network of isolated polymer-grafted nanoparticles (PGNs) adsorbed on a surface. As expected, increasing the monomer-surface adsorption strength causes the polymer chains to spread out to increase contact with the surface, leading to a canopy shape that is in qualitative agreement with recent experimental results. We compare height profiles and other structural features of four PGN systems to show the separate and combined effects of increasing chain length and graft density. At moderate graft density and low surface attraction strength, nearby PGN canopies interpenetrate substantially and their combined shape is similar to that of a single PGN canopy. At high graft density or surface interaction, the interparticle spacing increases significantly. We use a geometrical primitive path analysis to calculate average entanglement properties including canopy-canopy entanglements between pairs of PGNs. The longer chain systems are well entangled at both graft densities considered, and we find that as the monomer-surface interaction strength is increased (and the interparticle distance increases), entanglements between the two PGNs are reduced. We find that the number of inter-PGN entanglements per chain is slightly larger at the lower graft density, likely because steric constraints at high graft density tend to reduce interparticle entanglements.