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Deep eutectic solvents (DESs) are an attractive class of materials with low toxicity, broad commercial availability, low costs and simple synthesis, which allows for tuning of their properties. We develop and demonstrate the use of high-throughput and data-driven strategies to accelerate the investigation of new DES formulations. A cheminformatics approach is used to outline a design space, which results in 3477 hydrogen bond donor (HBD) and 185 quaternary ammonium salt (QAS) molecules identified as good candidate components for DES. The synthesis methodology is then adapted to a high-throughput protocol using liquid handling robots for the rapid synthesis of DES combinations. High-throughput electrochemical characterization and melting point detection systems are used to measure key performance metrics. To demonstrate the new workflow, a total of 600 unique samples are prepared and characterized, corresponding to 50 unique DES combinations at 12 HBD/QAS molar ratios. After synthesis, a total of 230 samples are found liquid at room temperature and further characterized. Several DESs display conductivities above 1 mS cm −1 , with a maximum recorded conductivity of 13.7 mS cm −1 for the combination of acetylcholine chloride (20 mol%) and ethylene glycol. All liquid DES samples show stable potential windows greater than 3 V. We also demonstrate that these DESs are electrochemically limited by viscosity, both in the conductivity and in the limiting processes on their cyclic voltammograms. Comparison with literature reports shows good agreement for properties measured in the high-throughput study, which helps to validate the workflow. This work demonstrates new methods to accelerate the collection of key DES metrics, providing data to formulate robust property prediction models and obtaining insight on interactions between molecular components. Data-driven high-throughput experimentation strategies can accelerate DES development for a variety of applications. Moreover, these approaches can also be extended to tackle other materials challenges with large molecular design spaces. 

Dynamic strain based atomic force microscopy (AFM) modes often fail at the interfaces where the most interesting physics occurs because of their incapability of tracking contact resonance accurately under rough topography. To overcome this difficulty, we develop a high-throughput sequential excitation AFM that captures contact dynamics of probe–sample interactions with high fidelity and efficiency, acquiring the spectrum of data on each pixel over a range of frequencies that are excited in a sequential manner. Using electrochemically active granular ceria as an example, we map both linear and quadratic electrochemical strain accurately across grain boundaries with high spatial resolution where the conventional approach fails. The enhanced electrochemical responses point to the accumulation of small polarons in the space charge region at the grain boundaries, thought to be responsible for the enhanced electronic conductivity in nanocrystalline ceria. The spectrum of data can be processed very efficiently by physics-informed principal component analysis (PCA), speeding data processing by several orders of magnitude. This approach can be applied to a variety of AFM modes for studying a wide range of materials and structures on the nanoscale.