We present a direct strategy to assemble porous tetrapyrrolic materials with tunable gas uptake selectivity by varying the coordinated cation. Co-OX1 shows improved CO2 uptake of 51.66 cm3 g-1 at 298 K, while free base-OX1 demonstrates a CO2/N2 selectivity of 202.9. This approach offers a viable route to CO2 capture technologies.

The use of robotics and automation in self-driving laboratories (SDLs) can introduce additional safety complexities, beyond those already present in conventional research laboratories. Personal protective equipment (PPE) is an essential requirement for ensuring the safety and well-being of workers in all laboratories, self-driving or otherwise. Fires are another important risk factor in chemical laboratories. In SDLs, fires that occur close to mobile robots, which use flammable lithium batteries, could have increased severity. Here, we present Chemist Eye, a distributed safety monitoring system designed to enhance situational awareness in SDLs. The system integrates multiple stations equipped with RGB, depth, and infrared cameras, designed to monitor incidents in SDLs. Chemist Eye is also designed to spot workers who have suffered a potential accident or medical emergency, PPE compliance and fire hazards. To do this, Chemist Eye uses decision-making driven by a vision-language model (VLM). Chemist Eye is designed for seamless integration, enabling real-time communication with robots. Based on the VLM recommendations, the system attempts to drive mobile robots away from potential fire locations, exits, or individuals not wearing PPE, and issues audible warnings where necessary. It also integrates with third-party messaging platforms to provide instant notifications to lab personnel. We tested Chemist Eye with real-world data from an SDL equipped with three mobile robots and found that the spotting of possible safety hazards and decision-making performances reached 88% and 95%, respectively.

Fume hoods protect chemists and the environment from hazardous vapours and airborne substances produced during experiments. They are standard in chemistry laboratories worldwide. However, fume hoods were designed for manual chemistry, and there are still relatively few robotic systems designed to operate within these inherently confined spaces. It is challenging to design robotic systems that can perform the same variety of operations within fume hoods that can be performed by a dexterous human chemist. Here, we present an automated platform comprising a robotic arm that can perform liquid handling, solid handling, capping/decapping, heating and stirring, filtration, and sample imaging within a standard laboratory fume hood (50 cm × 120 cm × 170 cm). The broad applicability of this system was demonstrated in two materials research problems (a dye-based porosity screening workflow and the synthesis of a porous organic cage) and in a phthalimide synthesis. The success of the synthesis workflows was validated offline by NMR, X-ray diffraction, mass spectrometry and FTIR.

Process chemistry creates scalable routes for new lead molecules and is a crucial but laborious stage in pharmaceutical and agrochemical development cycles. We have built an automated process chemistry platform that tackles late-stage process development. The modular workflow integrates both industry-standard tools and bespoke devices to enable process scale synthesis, work-up, and analysis. A multitasking mobile robot works between an automated synthesis reactor and an ultra-high-performance liquid chromatography-mass spectrometer (UHPLC-MS) for product analysis, cleaning the reactor between runs. The robot's anthropomorphic manipulation capabilities allow it to interface with minimally redesigned equipment that can be shared with human researchers. Reaction yields and purity match human chemist performance. Timings for round-the-clock, back-to-back experiments suggest that the weekly reaction output of the robot operating multiple reactors could exceed that of a human process chemist by a factor of 12 in an industrial setting.

A high-throughput (HTE) robotic colourimetric titration workstation was developed using a commercial liquid handling robot (Opentrons OT-2) and computer vision-based analysis. While designed for multiple titration applications, hydrogen peroxide (H2O2) determination serves as the most elaborate and well-characterized demonstration of its capabilities. Specifically, potassium permanganate (KMnO4) redox titration was employed to quantify the hydrogen peroxide (H2O2) concentration, leveraging the distinct colourimetric transition from colourless to pale pink at the titration endpoint. To monitor this colour change, a webcam was installed on the OT-2 pipette mount, capturing real-time titration progress. Image analysis was enhanced through VGG-augmented UNet for segmentation and the CIELab colour model, ensuring robust and reproducible detection of subtle colour changes. The sensitivity test of the computer vision-aided colour analysis was strongly correlated to UV-vis spectroscopy (R2 = 0.9996), with a good linear dynamic range at low concentrations. The analytical accuracy of this workstation was +/- 11.9% in a 95% confidence interval and its corresponding absolute concentration difference was only 0.50 mM. To validate its real-world applicability, this workstation was first deployed to monitor the photoproduction of H2O2 over a conjugated polymer photocatalyst, DE7. In addition to performing redox titrations, we demonstrated that this workstation can also be used for acid-base titration and complexometric titration, capturing a diverse range of colour changes.

Recently, we reported the reconstruction of two-dimensional (2D) to three-dimensional (3D) covalent organic frameworks (COFs) via base-catalyzed boronate ester to spiroborate linkage conversion. In that work, we tentatively attributed the interlayer close-packing in the 2D BPDA-COF as the main cause for the long reaction time-40 days-required to complete the structure reconstruction in N,N-diethylformamide (DEF). Here, we address this hypothesis by designing methyl-substituted 4,4'-biphenyldiboronic acid (BPDA) with large molecular twist to weaken the packing between boronate esters. Experiments show that the spiroborate COF formation is accelerated by increased molecular twist in three linear diboronic acids linkers, with the pure 3D spiroborate phase obtained in 3 days via reaction of Co(ii) 2,3,9,10,16,17,23,24-octahydroxyphthalocyaninato ((OH)8PcCo) in N,N-diethylformamide (DEF). Mechanistic studies reveal that methyl-substituted linear diboronic acids are more liable to protodeboronation, which also contributes to the accelerated spiroborate structure formation.

Porous materials are important for many technologies, but the measurement of porosity by gas adsorption isotherms is slow, taking around one day per sample using a single-port gas sorption analyzer, even when using a "quick" analysis method with relatively few data points. With the increased use of automated platforms for material generation, porosity analysis is now frequently the bottleneck in the discovery of new porous materials. Here, we present a semiautomated pre-screening strategy that uses dye adsorption to create a colorimetric array that is combined with computer vision analysis for porosity screening. By using a six-dye multichannel array and a defined porosity threshold, our method rapidly screened 50 candidate materials that spanned molecular solids, polymers, and metal-organic frameworks. The method showed a 98-100% classification accuracy compared with gas uptake measurements. While this method is more qualitative than quantitative, it is more than 30 times faster than conventional gas sorption measurements, and it has the scope to be made much faster with greater parallelization and automation. This makes this colorimetric method suitable for pre-screening arrays of materials to choose samples that merit more detailed conventional porosity analysis.

In the context of self-driving laboratories (SDLs), ensuring automated and error-free capping is crucial, as it is a ubiquitous step in sample preparation. Automated capping in SDLs can occur in both large and small workspaces (e.g., inside a fume hood). However, most commercial capping machines are designed primarily for large spaces and are often too bulky for confined environments. Moreover, many commercial products are closed-source, which can make their integration into fully autonomous workflows difficult. This paper introduces an open-source capping machine suitable for compact spaces, which also integrates a vision system that recognises capping failure. The capping and uncapping processes are repeated 100 times each to validate the machine’s design and performance. As a result, the capping machine reached a 100

Computer vision is a useful reaction monitoring and characterisation tool for scientists seeking to accelerate discovery processes using automation and machine learning (ML). Here we report a non-invasive laser-based method that combines computer vision and deep learning models to classify the solubility of different polymeric compounds across a range of solvents. Classifications were conducted using two to four solubility classes (soluble, soluble-colloidal, partially soluble, and insoluble), achieving high test accuracy rates ranging from 94.1% (2 classes), to 89.5% (4 classes). Using results from our solubility screening method, we also determined the Hansen Solubility Parameters (HSP) of the polymers using an optimisation algorithm. The calculated percentage Euclidean distance between the HSP values obtained from our dataset and the literature HSP values for the polymers, ranged from 11-32%. Finally, we developed the feature-wise linear modulation (FiLM) conditioned Convolutional Neural Network (CNN) regression model to estimate the size of polymeric nanoparticles between 20-440 nm and achieved a Mean Absolute Error (MAE) of 9.53 nm.

UTSA-16(Zn) is a zinc and citrate-based metal-organic framework (MOF) which has shown highly promising performance for CO2 capture. However, the transition of this MOF to industrial application has been hindered as a scalable synthesis method has not yet been reported. Herein we report the first scalable continuous flow synthesis of UTSA-16(Zn), demonstrating a production rate of 173 g/h, which is a 77-fold increase compared to previously reported batch methods. Sustainability of the synthesis was maximised using low-cost non-toxic reagents and a low-energy flow reactor operating at atmospheric pressure. Chemical (reactant ratios, Zn/Mg mixed-metal) and process parameters (solvent ratio, flow rate, temperature) were optimised to continuously produce UTSA-16(Zn) which also demonstrated a high CO2 adsorption capacity up to 3.8 mmol/g and conversion yield of up to 66 %. Pristine MOFs are typically thermally insulating, thus thermal regeneration is challenging. To overcome this limitation, magnetic nanoparticles can be embedded within the MOF. This enables fast and energy efficient regeneration through magnetic induction heating. Here, citrate-coated Fe3O4 magnetic nanoparticles (MNP-CA) were successfully incorporated into the flow synthesis process of UTSA-16(Zn) to form UTSA-16(Zn)@MNP-CA magnetic framework composites (MFCs), representing the highest production rate reported of any MFC to date (152 g/h c.f. 13 g/h for MgFe2O4@UiO-66-NH2). UTSA-16(Zn)@MNP-CA MFCs demonstrate rapid heating under a magnetic field (26-150 degrees C in 60 s). The flow method developed herein is also widely applicable for scalable manufacture of other MOFs and MFCs, enabling their broader transition towards industrial applications.

Metal-organic polyhedra (MOPs) can exhibit tunable porosity and functionality, suggesting potential for applications such as molecular separations. MOPs are typically constructed by the bottom-up multicomponent self-assembly of organic ligands and metal ions, and the final functionality can be hard to program. Here, we used trianglsalen macrocycles as preorganized building blocks to assemble octahedral-shaped MOPs. The resultant MOPs inherit most of the preorganized properties of the macrocyclic ligands, including their well-defined cavities and chirality. As a result, the porosity in the MOPs could be tuned by modifying the structure of the macrocycle building blocks. Using this strategy, we could systematically enlarge the size of the MOPs from 26.3 to 32.1 & Aring; by increasing the macrocycle size. The family of MOPs shows experimental surface areas of up to 820 m(2)/g, and they are stable in water. One of these MOPs can efficiently separate the rare gases Xe from Kr because the prefabricated macrocyclic windows of MOPs can be modified to sit at the Xe/Kr size cutoff range.

Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making1,2. Most autonomous laboratories involve bespoke automated equipment3-6, and reaction outcomes are often assessed using a single, hard-wired characterization technique7. Any decision-making algorithms8 must then operate using this narrow range of characterization data9,10. By contrast, manual experiments tend to draw on a wider range of instruments to characterize reaction products, and decisions are rarely taken based on one measurement alone. Here we show that a synthesis laboratory can be integrated into an autonomous laboratory by using mobile robots11-13 that operate equipment and make decisions in a human-like way. Our modular workflow combines mobile robots, an automated synthesis platform, a liquid chromatography-mass spectrometer and a benchtop nuclear magnetic resonance spectrometer. This allows robots to share existing laboratory equipment with human researchers without monopolizing it or requiring extensive redesign. A heuristic decision-maker processes the orthogonal measurement data, selecting successful reactions to take forward and automatically checking the reproducibility of any screening hits. We exemplify this approach in the three areas of structural diversification chemistry, supramolecular host-guest chemistry and photochemical synthesis. This strategy is particularly suited to exploratory chemistry that can yield multiple potential products, as for supramolecular assemblies, where we also extend the method to an autonomous function assay by evaluating host-guest binding properties.

Synthetic photobiocatalysts are promising catalysts for valuable chemical transformations by harnessing solar energy inspired by natural photosynthesis. However, the synergistic integration of all of the components for efficient light harvesting, cascade electron transfer, and efficient biocatalytic reactions presents a formidable challenge. In particular, replicating intricate multiscale hierarchical assembly and functional segregation involved in natural photosystems, such as photosystems I and II, remains particularly demanding within artificial structures. Here, we report the bottom-up construction of a visible-light-driven chemical-biological hybrid nanoreactor with augmented photocatalytic efficiency by anchoring an alpha-carboxysome shell encasing [FeFe]-hydrogenases (H-S) on the surface of a hydrogen-bonded organic molecular crystal, a microporous alpha-polymorph of 1,3,6,8-tetra(4 '-carboxyphenyl)pyrene (TBAP-alpha). The self-association of this chemical-biological hybrid system is facilitated by hydrogen bonds, as revealed by molecular dynamics simulations. Within this hybrid photobiocatalyst, TBAP-alpha functions as an antenna for visible-light absorption and exciton generation, supplying electrons for sacrificial hydrogen production by H-S in aqueous solutions. This coordination allows the hybrid nanoreactor, H-S|TBAP-alpha, to execute hydrogen evolution exclusively driven by light irradiation with a rate comparable to that of photocatalyst-loaded precious cocatalyst. The established approach to constructing new light-driven biocatalysts combines the synergistic power of biological nanotechnology with the multilength-scale structure and functional control offered by supramolecular organic semiconductors. It opens up innovative opportunities for the fabrication of biomimetic nanoreactors for sustainable fuel production and enzymatic reactions.

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.

CO2 adsorption and its subsequent utilization represent a promising avenue for mitigating climate change. The conver-sion of CO2 into valuable and useful products like carbon monoxide, methane, and methanol offers significant economic benefits. However, due to the low reactivity of CO2, the incorporation of CO2 adsorbents alongside catalytic materials has been pivotal in increasing the concentration of CO2 molecules around the catalytic sites. This strategy frequently relies on the precise deposition of the catalyst onto the adsorbent material. In this work, we explore NU-1000, a zirconium-based metal-organic framework originally designed as a CO2 adsorbent, to act as a selective photocatalyst for gas-phase CO2 reduction to CH4. NU-1000 contains UVA light-absorbing chromophore linkers, endowing it with the dual functionality of CO2 adsorbent and photocatalyst, which is crucial for efficient CO2 reutilization. Our research showcases an easily reproducible, and greener synthesis method for NU-1000 using micro-waves. We study the activity of NU-1000, including a functionalised variant, in the gas-phase photoreduction of CO2 to CH4 at room temperature and atmospheric pressure with electrons and protons derived from water. Remarkably, both the native and functionalised MOFs exhibit a rate of 170 and 800 μmol∙g-1∙h-1, respectively, alongside an exceptional selectivity of over 99%. These findings represent some of the highest reported values for gas phase CO2 photoreduction under atmospheric conditions. Our results provide a foundation for exploring materials that can serve as both catalysts and sorbents in the photocatalytic transformation of CO2 to value-added products.

Metal-organic frameworks (MOFs) are useful synthetic materials that are built by the programmed assembly of metal nodes and organic linkers 1 . The success of MOFs results from the isoreticular principle 2 , which allows families of structurally analogous frameworks to be built in a predictable way. This relies on directional coordinate covalent bonding to define the framework geometry. However, isoreticular strategies do not translate to other common crystalline solids, such as organic salts 3-5 , in which the intermolecular ionic bonding is less directional. Here we show that chemical knowledge can be combined with computational crystal-structure prediction 6 (CSP) to design porous organic ammonium halide salts that contain no metals. The nodes in these salt frameworks are tightly packed ionic clusters that direct the materials to crystallize in specific ways, as demonstrated by the presence of well-defined spikes of low-energy, low-density isoreticular structures on the predicted lattice energy landscapes 7,8 . These energy landscapes allow us to select combinations of cations and anions that will form thermodynamically stable, porous salt frameworks with channel sizes, functionalities and geometries that can be predicted a priori. Some of these porous salts adsorb molecular guests such as iodine in quantities that exceed those of most MOFs, and this could be useful for applications such as radio-iodine capture 9-12 . More generally, the synthesis of these salts is scalable, involving simple acid-base neutralization, and the strategy makes it possible to create a family of non-metal organic frameworks that combine high ionic charge density with permanent porosity. The use of computational crystal-structure prediction has enabled the targeted assembly of frameworks of porous organic ammonium halide salts that have many of the qualities of metal-organic frameworks despite containing no metal.

Crystalline porous organic salts (CPOS) are a subclass of molecular crystals. The low solubility of CPOS and their building blocks limits the choice of crystallisation solvents to water or polar alcohols, hindering the isolation, scale-up, and scope of the porous material. In this work, high throughput screening was used to expand the solvent scope, resulting in the identification of a new porous salt, CPOS-7, formed from tetrakis(4-sulfophenyl)methane (TSPM) and tetrakis(4-aminophenyl)methane (TAPM). CPOS-7 does not form with standard solvents for CPOS, rather a hydrated phase (Hydrate2920) previously reported is isolated. Initial attempts to translate the crystallisation to batch led to challenges with loss of crystallinity and Hydrate2920 forming favorably in the presence of excess water. Using acetic acid as a dehydrating agent hindered formation of Hydrate2920 and furthermore allowed for direct conversion to CPOS-7. To allow for direct formation of CPOS-7 in high crystallinity flow chemistry was used for the first time to circumvent the issues found in batch. CPOS-7 and Hydrate2920 were shown to have promise for water and CO2 capture, with CPOS-7 having a CO2 uptake of 4.3 mmol/g at 195 K, making it one of the most porous CPOS reported to date.

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.

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 bipyridine complexes as the photosensitizer combined with the same cobalt bipyridine complexes.