Publications by authors named "Gianluca Gazzola"

An electrochemically controlled atom transfer radical polymerization (ATRP) was successfully carried out with a minimal amount (ppm-level) of FeBr 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.

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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 O in the polymerization medium limits the attainable thickness of polymer brushes and causes the occurrence of "edge effects", , areas at the substrates' edges where continuous oxygen diffusion from the surrounding environment inhibits brush growth.

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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.

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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.

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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).

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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.

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