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.

From Concept to Crystals via Prediction: Multi-Component Organic Cage Pots by Social Self-Sorting.

We describe the a priori computational prediction and realization of multi-component cage pots, starting with molecular predictions based on candidate precursors through to crystal structure prediction and synthesis using robotic screening. The molecules were formed by the social self-sorting of a tri-topic aldehyde with both a tri-topic amine and di-topic amine, without using orthogonal reactivity or precursors of the same topicity. Crystal structure prediction suggested a rich polymorphic landscape, where there was an overall preference for chiral recognition to form heterochiral rather than homochiral packings, with heterochiral pairs being more likely to pack window-to-window to form two-component capsules. These crystal packing preferences were then observed in experimental crystal structures.

Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights.

Organic molecules tend to close pack to form dense structures when they are crystallised from organic solvents. Porous molecular crystals defy this rule: they contain open space, which is typically stabilised by inclusion of solvent in the interconnected pores during crystallisation. The design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystal form stability. Here, we combine crystal structure prediction (CSP) with a robotic crystallisation screen to accelerate the discovery of stable hydrogen-bonded frameworks. We exemplify this strategy by finding new phases of two well-studied molecules in a computationally targeted way. Specifically, we find a new 'hidden' porous polymorph of trimesic acid, δ-TMA, that has a guest-free hexagonal pore structure, as well as three new solvent-stabilized diamondoid frameworks of adamantane-1,3,5,7-tetracarboxylic acid (ADTA). Beyond porous solids, this hybrid computational-experimental approach could be applied to a wide range of materials problems, such as organic electronics and drug formulation.

Accelerated robotic discovery of type II porous liquids.

Porous liquids are an emerging class of materials and to date little is known about how to best design their properties. For example, bulky solvents are required that are size-excluded from the pores in the liquid, along with high concentrations of the porous component, but both of these factors may also contribute to higher viscosities, which are undesirable. Hence, the inherent multivariate nature of porous liquids makes them amenable to high-throughput optimisation strategies. Here we develop a high-throughput robotic workflow, encompassing the synthesis, characterisation and property testing of highly-soluble, vertex-disordered porous organic cages dissolved in a range of cavity-excluded solvents. As a result, we identified 29 cage-solvent combinations that combine both higher cage-cavity concentrations and more acceptable carrier solvents than the best previous examples. The most soluble materials gave three times the pore concentration of the best previously reported scrambled cage porous liquid, as demonstrated by increased gas uptake. We were also able to explore alternative methods for gas capture and release, including liberation of the gas by increasing the temperature. We also found that porous liquids can form gels at higher concentrations, trapping the gas in the pores, which could have potential applications in gas storage and transportation.

Computationally-inspired discovery of an unsymmetrical porous organic cage.

A completely unsymmetrical porous organic cage was synthesised from a C2v symmetrical building block that was identified by a computational screen. The cage was formed through a 12-fold imine condensation of a tritopic C2v symmetric trialdehyde with a ditopic C2 symmetric diamine in a [4 + 6] reaction. The cage was rigid and microporous, as predicted by the simulations, with an apparent Brunauer-Emmett-Teller surface area of 578 m2 g-1. The reduced symmetry of the tritopic building block relative to its topicity meant there were 36 possible structural isomers of the cage. Experimental characterisation suggests a single isomer with 12 unique imine environments, but techniques such as NMR could not conclusively identify the isomer. Computational structural and electronic analysis of the possible isomers was used to identify the most likely candidates, and hence to construct a 3-dimensional model of the amorphous solid. The rational design of unsymmetrical cages using building blocks with reduced symmetry offers new possibilities in controlling the degree of crystallinity, porosity, and solubility, of self-assembled materials.

Functionalised microscale nanoband edge electrode (MNEE) arrays: the systematic quantitative study of hydrogels grown on nanoelectrode biosensor arrays for enhanced sensing in biological media.

Nanoelectrodes and nanoelectrode arrays show enhanced diffusion and greater faradaic current densities and signal-to-noise ratios compared to macro and microelectrodes, which can lead to enhanced sensing and detection. One example is the microsquare nanoband edge electrode (MNEE) array system, readily formed through microfabrication and whose quantitative response has been established electroanalytically. Hydrogels have been shown to have applications in drug delivery, tissue engineering, and anti-biofouling; some also have the ability to be grown electrochemically. Here, we combine these two emerging technologies to demonstrate the principles of a hydrogel-coated nanoelectrode array biosensor that is resistant to biofouling. We first electrochemically grow and analyze hydrogels on MNEE arrays. The structure of these gels is shown by imaging to be electrochemically controllable, reproducible and structurally hierarchical. This structure is determined by the MNEE array diffusion fields, consistent with the established hydrogel formation reaction, and varies in structural scale from nano (early time, near electrode growth) to micro (for isolated elements in the array) to macro (when there is array overlap) with distance from the electrode, forming a hydrogel mesh of increasing density on progression from solution to electrode. There is also increased hydrogel structural density observed at electrode corners, attributable to enhanced diffusion. The resulting hydrogel structure can be formed on (and is firmly anchored to/through) an established clinically relevant biosensing layer without compromising detection. It is also shown to be capable, through proof-of-principle model protein studies using bovine serum albumin (BSA), of preventing protein biofouling whilst enabling smaller molecules such as DNA to pass through the hydrogel matrix and be sensed. Together, this demonstrates a method for developing reproducible, quantitative electrochemical nanoelectrode biosensors able to sense selectively in real-world sample matrices through the tuning of their interfacial properties.

Computationally-Guided Synthetic Control over Pore Size in Isostructural Porous Organic Cages.

The physical properties of 3-D porous solids are defined by their molecular geometry. Hence, precise control of pore size, pore shape, and pore connectivity are needed to tailor them for specific applications. However, for porous molecular crystals, the modification of pore size by adding pore-blocking groups can also affect crystal packing in an unpredictable way. This precludes strategies adopted for isoreticular metal-organic frameworks, where addition of a small group, such as a methyl group, does not affect the basic framework topology. Here, we narrow the pore size of a cage molecule, CC3, in a systematic way by introducing methyl groups into the cage windows. Computational crystal structure prediction was used to anticipate the packing preferences of two homochiral methylated cages, CC14-R and CC15-R, and to assess the structure-energy landscape of a CC15-R/CC3-S cocrystal, designed such that both component cages could be directed to pack with a 3-D, interconnected pore structure. The experimental gas sorption properties of these three cage systems agree well with physical properties predicted by computational energy-structure-function maps.