Andrew I. Cooper

Organic cages can possess complex, functionalised internal cavities that make them promising candidates for synthetic enzyme mimics. Conformationally flexible but chemically robust structures are needed for adaptable guest binding and catalysis, but these rapidly exchanging systems are difficult to resolve in solution. Here, we use inexpensive calculations and high-throughput crystallisation experiments to identify accessible cage conformations for a recently reported organic cage by ‘locking’ them in the solid state. The conformers identified exhibit a range of distances between the carboxylic acid groups in the internal cavity, suggesting adaptability towards binding a wide array of target guest molecules. The complexity of the observed crystal structures goes beyond what is possible with state-of-the-art crystal structure prediction.

Discovering and optimizing multicomponent organic semiconductors is typically a laborious process. High-throughput experimentation can accelerate this, but the results of small-scale screening trials are not always transferable to bulk materials production. Here we report the accelerated discovery of molecular nanojunction photocatalysts based on a combinatorial donor–acceptor molecular library assisted by high-throughput automated screening. The knowledge gained from this high-throughput batch screening is then transferred to a scaled-up, flow-based synthesis process. The scaled-up molecular nanojunction MTPA-CA:CNP147 (3-(4-(bis(4-methoxyphenyl)amino)phenyl)-2-cyanoacrylic acid:2,6-bis(4-cyanophenyl)-4-(4′-fluoro-[1,1′-biphenyl]-4-yl)pyridine-3,5-dicarbonitrile) exhibits a sacrificial hydrogen evolution rate of 330.3 mmol h−1 g−1 with an external quantum efficiency of 80.3% at 350 nm …

The solar‐driven photocatalytic production of hydrogen peroxide (H2O2) from water and oxygen using semiconductor catalysts offers a promising approach for converting solar energy into storable chemical energy. However, the efficiency of photocatalytic H2O2 production is often restricted by the low photo‐generated charge separation, slow surface reactions and inadequate stability. Here, we developed a mixed‐linker strategy to build a donor‐acceptor‐acceptor (D–A–A) type covalent organic framework (COF) photocatalyst, FS‐OHOMe‐COF. The FS‐OHOMe‐COF structure features extended π–π conjugation that improves charge mobility, while the introduction of sulfone units not only as active sites facilitates surface reactions with water but also bolsters stability through increased interlayer forces. The resulting FS‐OHOMe‐COF has a low exciton binding energy, long excited‐state lifetime and high photo …

Conjugated polymers are promising materials for photocatalytic hydrogen evolution. However, most reported materials are not solution‐processible, limiting their potential for large‐scale application, for example as solution cast films. Flexible side‐chains are commonly introduced to provide solubility, but these often impart unfavorable properties, such as hydrophobicity, which lowers photocatalytic activity. Here, computational predictions are employed to aid in the design of chloroform soluble polymer photocatalysts that show increased planarity through favorable intramolecular interactions. Using this approach, three conjugated polymer photocatalysts with identical poly(benzene‐dibenzo[b,d]thiophene sulfone) backbones but different solubilizing side‐chains on the benzene‐ring are explored, i.e., tri(ethylene glycol), n‐decyloxy, and n‐dodecyl. These side‐chain variations significantly alterr the properties of the …

Biohybrid photocatalysts are composite materials that combine the efficient light-absorbing properties of synthetic materials with the highly evolved metabolic pathways and self-repair mechanisms of biological systems. Here, we show the potential of conjugated polymers as photosensitisers in biohybrid systems by combining a series of polymer nanoparticles with engineered Escherichia coli (E. coli) cells. Under simulated solar light irradiation, the biohybrid system consisting of fluorene/dibenzo [b, d] thiophene sulfone copolymer (LP41) and recombinant E. coli (ie, a LP41/HydA BL21 biohybrid) show a sacrificial hydrogen evolution rate of 3.442 mmol g-1 h-1 (normalised to polymer amount). It is over 30 times higher than the polymer photocatalyst alone (0.105 mmol g-1 h-1), while no detectable hydrogen was generated from the E. coli cells alone, demonstrating the strong synergy between the polymer nanoparticles and bacterial cells. The differences in the physical interactions between synthetic materials and microorganisms, as well as redox energy level alignment, elucidate the trends in photochemical activity. Our results suggest that organic semiconductors may offer advantages, such as solution processability, low toxicity, and more tunable surface interactions with the biological components over inorganic materials.

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.

Robotic automation is proving itself indispensable in the modern Chemistry laboratory, but adoption is slowed down by the technical challenges of implementing such systems. This paper reports on a novel adaptive gripper mechanism that can easily and reliably grasp cylindrical and prismatic objects of various sizes with limited clearance required. The proposed design exploits the inherent compliance of a cable that is driven to fully envelope the target object. The cable is run through a rigid finger, allowing the loop to be placed around objects with minimal clearance required and to provide support for the object once the grip is complete. Thanks to the compliant nature of the mechanism, the gripper requires minimal control effort to complete a gasping task. A prototype of the gripper has been designed and built for chemistry automation tasks, where it showed very high grasp reliability with grasp failures.

We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce via random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues.

Hydrogenases are microbial metalloenzymes capable of catalyzing the reversible interconversion between molecular hydrogen and protons with high efficiency, and have great potential in the development of new electrocatalysts for renewable fuel production. Here, we engineered the intact proteinaceous shell of the carboxysome, a self-assembling protein organelle for CO2 fixation in cyanobacteria and proteobacteria, and sequestered heterologously produced [NiFe]-hydrogenases into the carboxysome shell. The protein-based hybrid catalyst produced in E. coli shows substantially improved hydrogen production under both aerobic and anaerobic conditions and enhanced material and functional robustness, compared to unencapsulated [NiFe]-hydrogenases. The catalytically functional nanoreactor as well as the self-assembling and encapsulation strategies provide a framework for engineering new bioinspired …

Hydrogen-Bonded Frameworks. A hydrogen-bonded framework that was predicted to have 3-D porosity has been discovered experimentally by Anna G. Slater, John W. Ward, Marc A. Little, Andrew I. Cooper et al. in their Research Article (e202303167). It is one of the most porous hydrogen-bonded frameworks synthesized to date.

Soft porous crystals combine flexibility and porosity, allowing them to respond structurally to external physical and chemical environments. However, striking the right balance between flexibility and sufficient rigidity for porosity is challenging, particularly for molecular crystals formed by using weak intermolecular interactions. Here, we report a flexible oxygen-bridged prismatic organic cage molecule, Cage-6-COOH, which has three pillars that exhibit “hinge-like” rotational motion in the solid state. Cage-6-COOH can form a range of hydrogen-bonded organic frameworks (HOFs) where the “hinge” can accommodate a remarkable 67° dihedral angle range between neighboring units. This stems both from flexibility in the noncovalent hydrogen-bonding motifs in the HOFs and the molecular flexibility in the oxygen-linked cage hinge itself. The range of structures for Cage-6-COOH includes two topologically complex …

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 …

Donor–acceptor heterojunctions in organic photocatalysts can provide enhanced exciton dissociation and charge separation, thereby improving the photocatalytic activity. However, the wide choice of possible donors and acceptors poses a challenge for the rational design of organic heterojunction photocatalysts, particularly for large ternary phase spaces. We accelerated the exploration of ternary organic heterojunction photocatalysts (TOHP) by using a combination of machine learning and high-throughput experimental screening. This involved 736 experiments in all, out of possible 4320 ternary combinations. The top ten most active TOHPs discovered using this strategy showed outstanding sacrificial hydrogen production rates of more than 500 mmol g–1 h–1, with the most active ternary material reaching a rate of 749.8 mmol g–1 h–1 under 1 sun illumination. These rates of photocatalytic hydrogen generation …

The design of molecular organic photocatalysts for reactions such as water splitting requires consideration of factors that go beyond electronic band gap and thermodynamic driving forces. Here, we carried out a theoretical investigation of three molecular photocatalysts (1–3) that are structurally similar but that show different hydrogen evolution activities (25, 23 & 0 μmol h−1 for 1–3, respectively). We used density functional theory (DFT) and time-dependent DFT calculations to evaluate the molecules’ optoelectronic properties, such as ionization potential, electron affinity, and exciton potentials, as well as the interaction between the molecular photocatalysts and an idealized platinum cocatalyst surface. The ‘static’ picture thus obtained was augmented by probing the nonadiabatic dynamics of the molecules beyond the Born–Oppenheimer approximation, revealing a different picture of exciton recombination and …

Hydrogen‐bonded organic frameworks (HOFs) with low densities and high porosities are rare and challenging to design because most molecules have a strong energetic preference for close packing. Crystal structure prediction (CSP) can rank the crystal packings available to an organic molecule based on their relative lattice energies. This has become a powerful tool for the a priori design of porous molecular crystals. Previously, we combined CSP with structure‐property predictions to generate energy‐structure‐function (ESF) maps for a series of triptycene‐based molecules with quinoxaline groups. From these ESF maps, triptycene trisquinoxalinedione (TH5) was predicted to form a previously unknown low‐energy HOF (TH5‐A) with a remarkably low density of 0.374 g cm−3 and three‐dimensional (3D) pores. Here, we demonstrate the reliability of those ESF maps by discovering this TH5‐A polymorph …

The combination of covalent organic framework (COF) photosensitizers with molecular cocatalysts is a promising avenue for photocatalytic carbon dioxide (CO2) reduction. Here, a series of isostructural COFs was synthesized using linkers of different lengths, with or without partial fluorination. These COFs were investigated for photocatalytic CO2 reduction under visible-light irradiation when combined with cobalt(II) bipyridine complexes as a cocatalyst. Fluorination was found to enhance both CO2 affinity and catalytic activity, and a partially fluorinated COF, FBP-COF, achieved the highest CO2-to-CO conversion efficiency, showing a carbon monoxide (CO) generation rate of 2.08 mmol h−1 g−1 and a 90% CO selectivity. FBP-COF also showed good stability under sacrificial conditions, generating CO for 50 h with a turnover number of 91.5. This activity is much higher than a homogeneous system using ruthenium …

Closed-loop experiments can accelerate material discovery by automating both experimental manipulations and decisions that have traditionally been made by researchers. Fast and non-invasive measurements are particularly attractive for closed-loop strategies. Viscosity is a physical property for fluids that is important in many applications. It is fundamental in application areas such as coatings; also, even if viscosity is not the key property of interest, it can impact our ability to do closed-loop experimentation. For example, unexpected increases in viscosity can cause liquid-handling robots to fail. Traditional viscosity measurements are manual, invasive, and slow. Here we use convolutional neural networks (CNNs) as an alternative to traditional viscometry by non-invasively extracting the spatiotemporal features of fluid motion under flow. To do this, we built a workflow using a dual-armed collaborative robot that …

Chirality was traditionally considered a binary property of periodic lattices and crystals. However, the classes of two‐dimensional lattices modulo rigid motion form a continuous space, which was recently parametrized by three geographic‐style coordinates. The four non‐oblique Bravais classes of two‐dimensional lattices form low‐dimensional singular subspaces in the full continuous space. Now, the deviations of a lattice from its higher symmetry neighbors can be continuously quantified by real‐valued distances satisfying metric axioms. This article analyzes these and newer G‐chiral distances for millions of two‐dimensional lattices that are extracted from thousands of available two‐dimensional materials and real crystal structures in the Cambridge Structural Database.

The synthesis of a new porous organic cage decorated with isopropyl moieties (CC21) was achieved from the reaction of triformylbenzene and an isopropyl functionalised diamine. Unlike structurally analogous porous organic cages, its synthesis proved challenging due to competitive aminal formation, rationalised using control experiments and computational modelling. The use of an additional amine was found to increase conversion to the desired cage.

Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional composition, in which intermediate GP layers warp the original inputs, providing flexibility to model nonstationary dynamics. Two DGP regimes have emerged in recent literature. A “big data” regime, prevalent in machine learning, favors approximate, optimization-based inference for fast, high-fidelity prediction. A “small data” regime, preferred for computer surrogate modeling, deploys posterior integration for enhanced uncertainty quantification (UQ). We aim to bridge this gap by expanding the capabilities of Bayesian DGP posterior inference through the incorporation of the Vecchia approximation, allowing linear computational scaling without compromising accuracy or UQ. We are motivated by surrogate modeling of simulation campaigns with upwards of 100,000 runs—a size too large for previous fully-Bayesian implementations—and …