An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations.

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

One-Pot Process for Hydrodeoxygenation of Lignin to Alkanes Using Ru-Based Bimetallic and Bifunctional Catalysts Supported on Zeolite Y.

The synthesis of high-efficiency and low-cost catalysts for hydrodeoxygenation (HDO) of waste lignin to advanced biofuels is crucial for enhancing current biorefinery processes. Inexpensive transition metals, including Fe, Ni, Cu, and Zn, were severally co-loaded with Ru on HY zeolite to form bimetallic and bifunctional catalysts. These catalysts were subsequently tested for HDO conversion of softwood lignin and several lignin model compounds. Results indicated that the inexpensive earth-abundant metals could modulate the hydrogenolysis activity of Ru and decrease the yield of low-molecular-weight gaseous products. Among these catalysts, Ru-Cu/HY showed the best HDO performance, affording the highest selectivity to hydrocarbon products. The improved catalytic performance of Ru-Cu/HY was probably a result of the following three factors: (1) high total and strong acid sites, (2) good dispersion of metal species and limited segregation, and (3) high adsorption capacity for polar fractions, including hydroxyl groups and ether bonds. Moreover, all bifunctional catalysts proved to be superior over the combination catalysts of Ru/Al2 O3 and HY zeolite.