Continuous Gas–Liquid–Solid Slug Flow for Sustainable Heterogeneously Catalyzed PET-RAFT Polymerization

Amide-Containing Bottlebrushes via Continuous-Flow Photoiniferter Reversible Addition-Fragmentation Chain Transfer Polymerization: Micellization Behavior

Herein, a series of ternary amphiphilic amide-containing bottlebrushes were synthesized by photoiniferter (PI-RAFT) polymerization of macromonomers in continuous-flow mode using trithiocarbonate as a chain transfer agent. Visible light-mediated polymerization of macromonomers under mild conditions enabled the preparation of thermoresponsive copolymers with low dispersity and high yields in a very short time, which is not typical for the classical reversible addition-fragmentation chain transfer process. Methoxy oligo(ethylene glycol) methacrylate and alkoxy(C12-C14) oligo(ethylene glycol) methacrylate were used as the basic monomers providing amphiphilic and thermoresponsive properties. The study investigated how modifying comonomers, acrylamide (AAm), methacrylamide (MAAm), and N-methylacrylamide (-MeAAm) affect the features of bottlebrush micelle formation, their critical micelle concentration, and loading capacity for pyrene, a hydrophobic drug model. The results showed that the process is scalable and can produce tens of grams of pure copolymer per day. The unmodified copolymer formed unimolecular micelles at temperatures below the LCST in aqueous solutions, as revealed by DLS and SLS data. The incorporation of AAm, MAAm, and N-MeAAm units resulted in an increase in micelle aggregation numbers. The resulting bottlebrushes formed uni- or bimolecular micelles at extremely low concentrations. These micelles possess a high capacity for loading pyrene, making them a promising choice for targeted drug delivery.

Gas–Liquid Slug Flow Studies in Microreactors: Effect of Nanoparticle Addition on Flow Pattern and Pressure Drop

Colloidal suspensions of nanoparticles (e.g., metals and oxides) have been considered as a promising working fluid in microreactors for achieving significant process intensification. Existing examples include their uses in microflow as catalysts for enhancing the reaction efficiency, or as additives to mix with the base fluid (i.e., to form the so-called nanofluids) for heat/mass transfer intensification. Thus, hydrodynamic characterization of such suspension flow in microreactors is of high importance for a rational design and operation of the system. In this work, experiments have been conducted to investigate the flow pattern and pressure drop characteristics under slug flow between N2 gas and colloidal suspensions in the presence of TiO2 or Al2O3 nanoparticles through polytetrafluoroethylene (PTFE) capillary microreactors. The base fluid consisted of water or its mixture with ethylene glycol. The slug flow pattern with nanoparticle addition was characterized by the presence of a lubricating liquid film around N2 bubbles, in contrast to the absence of liquid film in the case of N2-water slug flow. This shows that the addition of nanoparticles has changed the wall wetting property to be more hydrophilic. Furthermore, the measured pressure drop under N2-nanoparticle suspension slug flow is well described by the model of Kreutzer et al. (AIChE J 51(9):2428–2440, 2005) at the mixture Reynolds numbers ca. above 100 and is better predicted by the model of Warnier et al. (Microfluidics and Nanofluidics 8(1):33–45, 2010) at lower Reynolds numbers given a better consideration of the effect of film thickness and bubble velocity under such conditions in the latter model. Therefore, the employed nanoparticle suspension can be considered as a stable and pseudo single phase with proper fluid properties (e.g., viscosity and density) when it comes to the pressure drop estimation.

Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis

Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.

2D Porphyrinic Metal-Organic Framework Nanosheets as Multidimensional Photocatalysts for Functional Materials

Ultrathin porphyrinic 2D MOFs, ZnTCPP nanosheets (TCPP: 5,10,15,20-(tetra-4-carboxyphenyl) porphyrin) were employed as heterogeneous photocatalysts to activate PET-RAFT polymerization under various wavelengths ranging from violet to orange light. High polymerization rates, oxygen tolerance, and precise temporal control were achieved. The polymers showed narrow molecular weight distributions and good chain-end fidelity. The 2D ZnTCPP nanosheets were applied as photocatalysts in stereolithographic 3D printing in an open-air environment under blue light to yield well-defined 3D printed objects. Apart from providing an efficient catalytic system, 2D ZnTCPP nanosheets reinforced the mechanical properties of the 3D printed materials. The presence of ZnTCPP embedded in the materials conferred effective antimicrobial activity under visible light by production of singlet oxygen, affording 98 % and 93 % anti-bacterial efficiency against Gram-positive and Gram-negative bacteria, respectively.