How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data Science

Accelerating R&D is essential to address some of the challenges humanity is currently facing, such as achieving the global sustainability goals. Today’s Edisonian approach of trial-and-error still prevalent in R&D labs takes up to two decades of fundamental and applied research for new materials to reach the market. Turning around this situation calls for strategies to upgrade R&D and expedite innovation. By conducting smart experiment planning that is data-driven and guided by AI/ML, researchers can more efficiently search through the complex - often constrained - space of possible experiments and find or hit the global optima much faster than with the current approaches. Moreover, with digitized data management, researchers will be able to maximize the utility of their data in the short and long terms with the aid of statistics, ML and visualization tools. In what follows, we describe a framework and lay out the key technologies to accelerate R&D and optimize experiment plannin

Kinetics of the Regeneration by Iodide of Dye Sensitizers Adsorbed on Mesoporous Titania

Regeneration of dye sensitizer molecules by reducing species contained in the electrolyte is a key mechanism in liquid dye-sensitized solar cells because it competes kinetically with a detrimental charge recombination process. Kinetics of the reduction by iodide ions of the oxidized states (S+) of two RuII complex dyes and four organic π-conjugated bridged donor−acceptor sensitizers were examined as a function of the electrolyte concentration. Results show that two different cases can be distinguished. A sublinear behavior of the regeneration rate and a plateau value reached at high bulk iodide concentrations were found for N820 ruthenium dye and interpreted as being due to an associative interaction involving the formation of (S+, I−)···I− surface complexes prior to the reaction. On the other hand, feeble reaction rates at low electrolyte concentrations and a superlinear behavior are observed predominantly for the organic dyes, pointing to a repulsive interaction between the dyed surface and iodide anions. At higher iodide bulk concentration, a linear behavior is reached, providing an estimate of a second-order rate constant. A correlation of these two opposite behaviors with the structure of the dye is observed, emphasizing the role of sulfur atoms in the association of I− anions in the dye-sensitized layer. These findings allow for a better understanding of the dye−electrolyte interaction and of the effect of the iodide concentration on the photovoltaic performances of dye-sensitized solar cells