Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry.

Automation can transform productivity in research activities that use liquid handling, such as organic synthesis, but it has made less impact in materials laboratories, which require sample preparation steps and a range of solid-state characterization techniques. For example, powder X-ray diffraction (PXRD) is a key method in materials and pharmaceutical chemistry, but its end-to-end automation is challenging because it involves solid powder handling and sample processing. Here we present a fully autonomous solid-state workflow for PXRD experiments that can match or even surpass manual data quality, encompassing crystal growth, sample preparation, and automated data capture. The workflow involves 12 steps performed by a team of three multipurpose robots, illustrating the power of flexible, modular automation to integrate complex, multitask laboratories.

Photoresponsive Type III Porous Liquids.

Porous materials are the subject of extensive research because of potential applications in areas such as gas adsorption and molecular separations. Until recently, most porous materials were solids, but there is now an emerging class of materials known as porous liquids. The incorporation of intrinsic porosity or cavities in a liquid can result in free-flowing materials that are capable of gas uptakes that are significantly higher than conventional non-porous liquids. A handful of porous liquids have also been investigated for gas separations. Until now, the release of gas from porous liquids has relied on molecular displacement (e.g., by adding small solvent molecules), pressure or temperature swings, or sonication. Here, we explore a new method of gas release which involves photoisomerisable porous liquids comprising a photoresponsive MOF dispersed in an ionic liquid. This results in the selective uptake of CO2 over CH4 and allows gas release to be controlled by using UV light.

A mobile robotic chemist.

Technologies such as batteries, biomaterials and heterogeneous catalysts have functions that are defined by mixtures of molecular and mesoscale components. As yet, this multi-length-scale complexity cannot be fully captured by atomistic simulations, and the design of such materials from first principles is still rare1-5. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches6-14 but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements required. Here we use a mobile robot to search for improved photocatalysts for hydrogen production from water15. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm16-18. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones. Our strategy uses a dexterous19,20 free-roaming robot21-24, automating the researcher rather than the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis.