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 crystallized from organic solvents. Porous molecular crystals defy this rule: they typically crystallize with lattice solvent in the interconnected pores. However, the design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystallization. Here, we combine crystal structure prediction (CSP) with a high-throughput crystallization screening method 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 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)

Porosity-engineered carbons for supercapacitive energy storage using conjugated microporous polymer precursors

Conjugated microporous polymers (CMPs) are considered an important material, combining aspects of both microporosity and extended π-conjugation. However, pristine CMP electrodes suffer from poor electrical conductivity which limits the material in electrochemical applications. In this work, direct carbonisation of conjugated microporous polymers (CMPs) yields porosity-engineered carbons, important for the flow of ions through the electrode. These conductive carbonised CMPs show specific capacitance as high as 260 F g−1, excellent rate capability and no loss in performance after 10 000 charge/discharge cycles. This study provides a procedure to enhance the performance of CMP-based materials, opening up a new source of electroactive materials

Powder-Bot: A Modular Autonomous Multi-Robot Workflow for Powder X-Ray Diffraction

Powder X-ray diffraction (PXRD) is a key technique for the structural characterisation of solid-state materials, but compared with tasks such as liquid handling, its end-to-end automation is highly challenging. This is because coupling PXRD experiments with crystallisation comprises multiple solid handling steps that include sample recovery, sample preparation by grinding, sample mounting and, finally, collection of X-ray diffraction data. Each of these steps has individual technical challenges from an automation perspective, and hence no commercial instrument exists that can grow crystals, process them into a powder, mount them in a diffractometer, and collect PXRD data in an autonomous, closed-loop way. Here we present an automated robotic workflow to carry out autonomous PXRD experiments. The PXRD data collected for polymorphs of small organic compounds is comparable to that collected under the same conditions manually. Beyond accelerating PXRD experiments, this workflow involves 13 component steps and integrates three different types of robots, each from a separate supplier, illustrating the power of flexible, modular automation in complex, multitask laboratories.Comment: 11 pages, 4 figures plus Supporting Information (2 videos, 13 supporting figures and one table

Maximising the hydrogen evolution activity in organic photocatalysts by co-polymerisation

The hydrogen evolution activity of a polymeric photocatalyst was maximised by co-polymerisation, using both experimental and computational screening, for a family of 1,4-phenylene/2,5-thiophene co-polymers. The photocatalytic activity is the product of multiple material properties that are affected in different ways by the polymer composition and microstructure. For the first time, the photocatalytic activity was shown to be a function of the arrangement of the building blocks in the polymer chain as well as the overall composition. The maximum in hydrogen evolution for the co-polymer series appears to result from a trade-off between the fraction of light absorbed and the thermodynamic driving force for proton reduction and sacrificial electron donor oxidation, with the co-polymer of p-terphenyl and 2,5-thiophene showing the highest activity

BHPR research: qualitative1. Complex reasoning determines patients' perception of outcome following foot surgery in rheumatoid arhtritis

Background: Foot surgery is common in patients with RA but research into surgical outcomes is limited and conceptually flawed as current outcome measures lack face validity: to date no one has asked patients what is important to them. This study aimed to determine which factors are important to patients when evaluating the success of foot surgery in RA Methods: Semi structured interviews of RA patients who had undergone foot surgery were conducted and transcribed verbatim. Thematic analysis of interviews was conducted to explore issues that were important to patients. Results: 11 RA patients (9 ♂, mean age 59, dis dur = 22yrs, mean of 3 yrs post op) with mixed experiences of foot surgery were interviewed. Patients interpreted outcome in respect to a multitude of factors, frequently positive change in one aspect contrasted with negative opinions about another. Overall, four major themes emerged. Function: Functional ability & participation in valued activities were very important to patients. Walking ability was a key concern but patients interpreted levels of activity in light of other aspects of their disease, reflecting on change in functional ability more than overall level. Positive feelings of improved mobility were often moderated by negative self perception ("I mean, I still walk like a waddling duck”). Appearance: Appearance was important to almost all patients but perhaps the most complex theme of all. Physical appearance, foot shape, and footwear were closely interlinked, yet patients saw these as distinct separate concepts. Patients need to legitimize these feelings was clear and they frequently entered into a defensive repertoire ("it's not cosmetic surgery; it's something that's more important than that, you know?”). Clinician opinion: Surgeons' post operative evaluation of the procedure was very influential. The impact of this appraisal continued to affect patients' lasting impression irrespective of how the outcome compared to their initial goals ("when he'd done it ... he said that hasn't worked as good as he'd wanted to ... but the pain has gone”). Pain: Whilst pain was important to almost all patients, it appeared to be less important than the other themes. Pain was predominately raised when it influenced other themes, such as function; many still felt the need to legitimize their foot pain in order for health professionals to take it seriously ("in the end I went to my GP because it had happened a few times and I went to an orthopaedic surgeon who was quite dismissive of it, it was like what are you complaining about”). Conclusions: Patients interpret the outcome of foot surgery using a multitude of interrelated factors, particularly functional ability, appearance and surgeons' appraisal of the procedure. While pain was often noted, this appeared less important than other factors in the overall outcome of the surgery. Future research into foot surgery should incorporate the complexity of how patients determine their outcome Disclosure statement: All authors have declared no conflicts of interes

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

Mining Predicted Crystal Structure Landscapes with High Throughput Crystallization: Old Molecules, New Insights

Organic molecules tend to close pack to form dense structures when they are crystallized from organic solvents. Porous molecular crystals defy this rule: they typically crystallize with lattice solvent in the interconnected pores. However, the design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystallization. Here, we combine crystal structure prediction (CSP) with a high-throughput crystallization screening method 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 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)

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

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. </p