Electrochemical Investigation of Iron-Catalyzed Atom Transfer Radical Polymerization.

Use of iron-based catalysts in atom transfer radical polymerization (ATRP) is very interesting because of the abundance of the metal and its biocompatibility. Although the mechanism of action is not well understood yet, iron halide salts are usually used as catalysts, often in the presence of nitrogen or phosphorous ligands (L). In this study, electrochemically mediated ATRP (eATRP) of methyl methacrylate (MMA) catalyzed by FeCl3, both in the absence and presence of additional ligands, was investigated in dimethylformamide. The electrochemical behavior of FeCl3 and FeCl3/L was deeply investigated showing the speciation of Fe(III) and Fe(II) and the role played by added ligands. It is shown that amine ligands form stable iron complexes, whereas phosphines act as reducing agents. eATRP of MMA catalyzed by FeCl3 was investigated in different conditions. In particular, the effects of temperature, catalyst concentration, catalyst-to-initiator ratio, halide ion excess and added ligands were investigated. In general, polymerization was moderately fast but difficult to control. Surprisingly, the best results were obtained with FeCl3 without any other ligand. Electrogenerated Fe(II) effectively activates the dormant chains but deactivation of the propagating radicals by Fe(III) species is less efficient, resulting in dispersity > 1.5, unless a high concentration of FeCl3 is used.

Simple Iron Halides Enable Electrochemically Mediated ATRP in Nonpolar Media.

An electrochemically controlled atom transfer radical polymerization (eATRP) was successfully carried out with a minimal amount (ppm-level) of FeBr3 catalyst in a nonpolar solvent, specifically anisole. Traditionally, nonpolar media have been advantageous for Fe-based ATRP, but their low conductivity has hindered any electrochemical application. This study introduces the application of electrocatalytic methods in a highly nonpolar polymerization medium. Precise control over the polymerization was obtained by employing anhydrous anisole with only 400 ppm of FeBr3 and applying a negative overpotential of 0.3 V. Additionally, employing an undivided cell setup with two simple iron wire electrodes resulted in a significant 15-fold reduction in electrical resistance compared to traditional divided cell setups. This enabled the production of polymers with a dispersity of ≤1.2. Lastly, an examination of kinetic and thermodynamic aspects indicated that the ppm-level catalysis was facilitated by the high ATRP equilibrium constant of Fe catalysts in nonpolar environments.

Oxygen Tolerance during Surface-Initiated Photo-ATRP: Tips and Tricks for Making Brushes under Environmental Conditions.

Achieving tolerance toward oxygen during surface-initiated reversible deactivation radical polymerization (SI-RDRP) holds the potential to translate the fabrication of polymer brush-coatings into upscalable and technologically relevant processes for functionalizing materials. While focusing on surface-initiated photoinduced atom transfer radical polymerization (SI-photoATRP), we demonstrate that a judicious tuning of the composition of reaction mixtures and the adjustment of the polymerization setup enable to maximize the compatibility of this grafting technique toward environmental conditions. Typically, the presence of O2 in the polymerization medium limits the attainable thickness of polymer brushes and causes the occurrence of "edge effects", i.e., areas at the substrates' edges where continuous oxygen diffusion from the surrounding environment inhibits brush growth. However, the concentrations of the Cu-based catalyst and "free" alkyl halide initiator in solution emerge as key parameters to achieve a more efficient consumption of oxygen and yield uniform and thick brushes, even for polymerization mixtures that are more exposed to air. Precise variation of reaction conditions thus allows us to identify those variables that become determinants for making the synthesis of brushes more tolerant toward oxygen, and consequently more practical and upscalable.

Coping with complexity: machine learning optimization of cell-free protein synthesis.

Biological systems contain complex metabolic pathways with many nonlinearities and synergies that make them difficult to predict from first principles. Protein synthesis is a canonical example of such a pathway. Here we show how cell-free protein synthesis may be improved through a series of iterated high-throughput experiments guided by a machine-learning algorithm implementing a form of evolutionary design of experiments (Evo-DoE). The algorithm predicts fruitful experiments from statistical models of the previous experimental results, combined with stochastic exploration of the experimental space. The desired experimental response, or evolutionary fitness, was defined as the yield of the target product, and new experimental conditions were discovered to have ∼ 350% greater yield than the standard. An analysis of the best experimental conditions discovered indicates that there are two distinct classes of kinetics, thus showing how our evolutionary design of experiments is capable of significant innovation, as well as gradual improvement.

High-Throughput Optimization Cycle of a Cell-Free Ribosome Assembly and Protein Synthesis System.

Building variant ribosomes offers opportunities to reveal fundamental principles underlying ribosome biogenesis and to make ribosomes with altered properties. However, cell viability limits mutations that can be made to the ribosome. To address this limitation, the in vitro integrated synthesis, assembly and translation (iSAT) method for ribosome construction from the bottom up was recently developed. Unfortunately, iSAT is complex, costly, and laborious to researchers, partially due to the high cost of reaction buffer containing over 20 components. In this study, we develop iSAT in Escherichia coli BL21Rosetta2 cell lysates, a commonly used bacterial strain, with a cost-effective poly sugar and nucleotide monophosphate-based metabolic scheme. We achieved a 10-fold increase in protein yield over our base case with an evolutionary design of experiments approach, screening 490 reaction conditions to optimize the reaction buffer. The computationally guided, cell-free, high-throughput technology presented here augments the way we approach multicomponent synthetic biology projects and efforts to repurpose ribosomes.

Automated discovery of novel drug formulations using predictive iterated high throughput experimentation.

BACKGROUND: We consider the problem of optimizing a liposomal drug formulation: a complex chemical system with many components (e.g., elements of a lipid library) that interact nonlinearly and synergistically in ways that cannot be predicted from first principles. METHODOLOGY/PRINCIPAL FINDINGS: The optimization criterion in our experiments was the percent encapsulation of a target drug, Amphotericin B, detected experimentally via spectrophotometric assay. Optimization of such a complex system requires strategies that efficiently discover solutions in extremely large volumes of potential experimental space. We have designed and implemented a new strategy of evolutionary design of experiments (Evo-DoE), that efficiently explores high-dimensional spaces by coupling the power of computer and statistical modeling with experimentally measured responses in an iterative loop. CONCLUSIONS: We demonstrate how iterative looping of modeling and experimentation can quickly produce new discoveries with significantly better experimental response, and how such looping can discover the chemical landscape underlying complex chemical systems.