A fully automated platform for photoinitiated RAFT polymerization

Lee, Jules; Mulay, Prajakatta; Tamasi, Matthew; Yeow, Jonathan; Stevens, Molly M.; Gormley, Adam J.

doi:10.1039/d2dd00100d

Cited by 11 publications

(7 citation statements)

References 43 publications

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“…A key requirement for this combinatorial approach is improved oxygen tolerance during polymerization. , Recently, high-throughput synthesis of well-defined high molecular weight polymers via enzymatically degassed RAFT polymerization in a 96-well plate was reported . There are also several examples of performing PET-RAFT in a high-throughput manner, − but similar ATRP systems are still rare …”

Section: Introductionmentioning

confidence: 99%

Red-Light-Driven Atom Transfer Radical Polymerization for High-Throughput Polymer Synthesis in Open Air

Hu,

Szczepaniak,

Lewandowska-Andralojc

et al. 2023

J. Am. Chem. Soc.

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Photoinduced reversible-deactivation radical polymerization (photo-RDRP) techniques offer exceptional control over polymerization, providing access to well-defined polymers and hybrid materials with complex architectures. However, most photo-RDRP methods rely on UV/visible light or photoredox catalysts (PCs), which require complex multistep synthesis. Herein, we present the first example of fully oxygen-tolerant red/ NIR-light-mediated photoinduced atom transfer radical polymerization (photo-ATRP) in a high-throughput manner under biologically relevant conditions. The method uses commercially available methylene blue (MB + ) as the PC and [X−Cu II /TPMA] + (TPMA = tris(2-pyridylmethyl)amine) complex as the deactivator. The mechanistic study revealed that MB + undergoes a reductive quenching cycle in the presence of the TPMA ligand used in excess. The formed semireduced MB (MB • ) sustains polymerization by regenerating the [Cu I /TPMA] + activator and together with [X−Cu II /TPMA] + provides control over the polymerization. This dual catalytic system exhibited excellent oxygen tolerance, enabling polymerizations with high monomer conversions (>90%) in less than 60 min at low volumes (50−250 μL) and high-throughput synthesis of a library of well-defined polymers and DNA−polymer bioconjugates with narrow molecular weight distributions (Đ < 1.30) in an open-air 96-well plate. In addition, the broad absorption spectrum of MB + allowed ATRP to be triggered under UV to NIR irradiation (395−730 nm). This opens avenues for the integration of orthogonal photoinduced reactions. Finally, the MB + /Cu catalysis showed good biocompatibility during polymerization in the presence of cells, which expands the potential applications of this method.

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Section: Introductionmentioning

confidence: 99%

Red-Light-Driven Atom Transfer Radical Polymerization for High-Throughput Polymer Synthesis in Open Air

Hu,

Szczepaniak,

Lewandowska-Andralojc

et al. 2023

J. Am. Chem. Soc.

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“…This results in the following expression (10) At monomer concentration c = c**, the number of monomers per correlation blob becomes comparable to the number of monomers per chain Kuhn length g(c**) = b/l. Rewriting this condition in terms of B th , we have (11) In the monomer concentration range such that c > c**, chain sections within the correlation length are rod-like, which is reflected in the concentration dependence of g = (cl 3 ) −0.5 (Figure 1b).…”

Section: Solutionsmentioning

confidence: 99%

Deep Learning for Determination of Properties of Semidilute Polymer Solutions

Sayko,

Jacobs,

Dominijanni

et al. 2023

ACS Appl. Polym. Mater.

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The ability to predict polymer solution viscosity is essential for polymer characterization and processing. Here, we use a synergistic approach combining the scaling theory of polymer solutions and convolutional neural network (CNN) models to obtain system-specific parameters and describe semidilute solution viscosity in the unentangled and entangled solution regimes. The scaling approach relies on the existence of a characteristic microscopic length scale�the solution correlation length (correlation blob size) ξ�which uniquely defines macroscopic solution properties. It is based on a relationship between the solution correlation length ξ = lg ν /B and the number of monomers g per correlation volume for polymers with the monomer projection length l. The system-specific set of parameters B g , B th , and 1 in the corresponding solution regime with the scaling exponent ν = 0.588, 0.5, and 1, respectively. Applying two CNN models, we obtained the sets B g and B th from the solution specific viscosity, η sp , as a function of concentration, c, and weight-average degree of polymerization, N w . The CNN was trained on theoretically generated datasets converted to sparse images representing the normalized specific viscosity η sp /N w (cl 3 ) 1/(3ν−1) in the unentangled Rouse regime. The trained CNN was utilized in automated data analysis of the solution viscosity of polystyrene, poly(ethylene oxide), poly(methyl methacrylate), poly(acrylonitrile-co-itaconic acid), cellulose, sodium hyaluronate, hydroxypropyl methyl cellulose, methyl cellulose, hydroxypropyl cellulose, cellulose tris(phenyl carbamate), xanthan gum, galactomannan, and sodium κ-carrageenan in water, organic solvents, and ionic liquids. This approach produced values of the B-parameters with mean absolute percentage differences of less than 6% from the corresponding values determined by the manual data analysis. The B-parameters are then used to obtain the packing number P e defining the onset of entanglements in polymer solutions and to describe semidilute solution viscosity as a function of concentration and N w .

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“…Although justifiable from an academic and fundamental point of view, it would be highly beneficial to expand the cargo scope and evaluate if PISA can not only serve as an alternative, but also enabling technology for certain cargos. These larger scale screening studies on typically expensive cargos could be facilitated by recently reported oxygen‐tolerant RAFT systems enabling polymerizations in μL volumes, 122,156 simulation studies to minimize the tedious optimization processes typically involved in multi‐parametric PISA systems 169–172 and fully automated RAFT platforms 252 Benchmarking against other DDS.…”

Section: Challenges Toward In Vivo Studies and Clinical Translationmentioning

confidence: 99%

Polymerization‐induced self‐assembly for drug delivery: A critical appraisal

Hochreiner,

van Ravensteijn

2023

Journal of Polymer Science

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Polymerization‐induced self‐assembly (PISA) with (in situ) encapsulation of (therapeutic) cargo has proven to be an efficient preparation method for loaded polymeric micelles and polymersomes, thereby presenting significant opportunities in the field of drug delivery. However, despite extensive research efforts, no significant advances toward systematic in vivo studies or clinical applications have been achieved to date. In this Review, we outline the current state‐of‐the‐art of cargo encapsulation via PISA with a specific focus on developments achieved in the past 5 years. Considering the general requirements for functional drug delivery systems, we identify the major hurdles that still need to be overcome in order to push PISA‐derived systems from a promising academic exercise to viable candidates for clinical translation.

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A fully automated platform for photoinitiated RAFT polymerization

Cited by 11 publications

References 43 publications

Red-Light-Driven Atom Transfer Radical Polymerization for High-Throughput Polymer Synthesis in Open Air

Red-Light-Driven Atom Transfer Radical Polymerization for High-Throughput Polymer Synthesis in Open Air

Deep Learning for Determination of Properties of Semidilute Polymer Solutions

Polymerization‐induced self‐assembly for drug delivery: A critical appraisal

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