Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents.

ABSTRACT: The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially perfo

SUBMITTER: Abranches DO 

PROVIDER: S-EPMC11423404 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature