Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents.

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.

Improved ethanol productivity and ethanol tolerance through genome shuffling of Saccharomyces cerevisiae and Pichia stipitis.

Genome shuffling by recursive protoplast fusion between Saccharomyces cerevisiae and Pichia stipitis also known as Scheffersomyces stipitis resulted in a promising yeast hybrid strain with superior qualities than those of the parental strains in enhancing biofuel production. Our study focused on the substrate utilization, ethanol fermentation, and ethanol tolerance of the hybrids and the parental strains. The parental strain S.