Automatic discovery and optimization of chemical processes

This paper presents the first overview of recent developments in techniques and methods that enable closed-loop optimization, also sometimes called ‘self optimization’, as well as discovery in different areas of molecular sciences. The closed-loop experimental platforms offer tremendous new opportunities by significantly increasing productivity, as well as enabling completely new types of experiments to be performed. Such experiments involve three main enabling technology areas: automated experimental systems, analytical instruments connected to automated chemoinformatics software and optimization or decision-making algorithms. We review the most exciting developments concerning robotic experiments, 3D printed lab-ware, experimental systems with multiple analytical instruments and advanced optimization algorithms based on machine learning approaches. A range of different chemical problems is described, which show the breadth of potential applications of this emerging experimental approach.This work was in part funded by EPSRC project “Closed Loop Optimization for Sustainable Chemical Manufacture” [EP/L003309/1].This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.coche.2015.07.00

Hydrodynamic assembly of two-dimensional layered double hydroxide nanostructures.

Formation mechanisms of two-dimensional nanostructures in wet syntheses are poorly understood. Even more enigmatic is the influence of hydrodynamic forces. Here we use liquid flow cell transmission electron microscopy to show that layered double hydroxide, as a model material, may form via the oriented attachment of hexagonal nanoparticles; under hydrodynamic shear, oriented attachment is accelerated. To hydrodynamically manipulate the kinetics of particle growth and oriented attachment, we develop a microreactor with high and tunable shear rates, enabling control over particle size, crystallinity and aspect ratio. This work offers new insights in the formation of two-dimensional materials, provides a scalable yet precise synthesis method, and proposes new avenues for the rational engineering and scalable production of highly anisotropic nanostructures

Feasibility of the Simultaneous Determination of Monomer Concentrations and Particle Size in Emulsion Polymerization Using in Situ Raman Spectroscopy.

An immersion Raman probe was used in emulsion copolymerization reactions to measure monomer concentrations and particle sizes. Quantitative determination of monomer concentrations is feasible in two-monomer copolymerizations, but only the overall conversion could be measured by Raman spectroscopy in a four-monomer copolymerization. The feasibility of measuring monomer conversion and particle size was established using partial least-squares (PLS) calibration models. A simplified theoretical framework for the measurement of particle sizes based on photon scattering is presented, based on the elastic-sphere-vibration and surface-tension models.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (EC FP7) Grant Agreement n. [NMP2-SL-2012-280827] and Engineering and Physical Sciences Research Council under grant EP/L003309/1.This is the final version of the article. It first appeared from the American Chemical Society via http://dx.doi.org/10.1021/acs.iecr.5b0275

Carbon neutral manufacturing via on-site CO2 recycling.

The chemical industry needs to significantly decrease carbon dioxide (CO2) emissions in order to meet the 2050 carbon neutrality goal. Utilization of CO2 as a chemical feedstock for bulk products is a promising way to mitigate industrial emissions; however, CO2-based manufacturing is currently not competitive with the established petrochemical methods and its deployment requires creation of a new value chain. Here, we show that an alternative approach, using CO2 conversion as an add-on to existing manufactures, can disrupt the global carbon cycle while minimally perturbing the operation of chemical plants. Proposed closed-loop on-site CO2 recycling processes are economically viable in the current market and have the potential for rapid introduction in the industries. Retrofit-based CO2 recycling can reduce annually between 4 and 10 Gt CO2 by 2050 and contribute to achieving up to 50% of the industrial carbon neutrality goal

Feasibility of Using 2,3,3,3-Tetrafluoropropene (R1234yf) as a Solvent for Solid–Liquid Extraction of Biopharmaceuticals

Tetrafluoropropene (R1234yf) is the most likely replacement for tetrafluoroethane (R134a), a widely used refrigerant, propellant and solvent, characterised by very high global warming potential. In this study solvation properties of R1234yf were studied experimentally and computationally for solubility of artemisinin, a precursor to the important bio-pharmaceutical API, and extraction of artemisinin from biomass. R1234yf was shown to be a poorer solvent than R134a for artemisinin. COSMO-RS calculations of solvation in R1234yf suggest that the decrease in performance is likely to be due to entropic effects. However, R1234yf was effectively used in solid liquid extraction of Artemisia annua. The new solvent has shown an increased selectivity to the target metabolite artemisinin. This should allow for design of more selective separation processes based on the new solvent molecule with a low global warming potential of 4 relative to CO2.This work was funded under iCON feasibility projects scheme of EPSRC “Centre for Continuous Manufacturing (CMAC)”, EP/IO33459/1. We acknowledge past funding from Medicine for Malaria Ventures for HPLC instrument and funding by University of Cambridge for the LCMS system

In silico rationalisation of selectivity and reactivity in Pd-catalysed C-H activation reactions.

A computational approach has been developed to automatically generate and analyse the structures of the intermediates of palladium-catalysed carbon-hydrogen (C-H) activation reactions as well as to predict the final products. Implemented as a high-performance computing cluster tool, it has been shown to correctly choose the mechanism and rationalise regioselectivity of chosen examples from open literature reports. The developed methodology is capable of predicting reactivity of various substrates by differentiation between two major mechanisms - proton abstraction and electrophilic aromatic substitution. An attempt has been made to predict new C-H activation reactions. This methodology can also be used for the automated reaction planning, as well as a starting point for microkinetic modelling.EPSRC grant EP/K009494/1 (MK and SL) and the National Research Foundation, Prime Minister’s Office, Singapore under its CREATE programme, project “Cambridge Centre for Carbon Reduction in Chemical Technology” (LC and AL)

Efficient Syntheses of Biobased Terephthalic Acid, p-Toluic Acid, and p-Methylacetophenone via One-Pot Catalytic Aerobic Oxidation of Monoterpene Derived Bio-p-cymene.

An efficient elevated-pressure catalytic oxidative process (2.5 mol % Co(NO3)2, 2.5 mol % MnBr2, air (30 bar), 125 °C, acetic acid, 6 h) has been developed to oxidize p-cymene into crystalline white terephthalic acid (TA) in ∼70% yield. Use of this mixed Co2+/Mn2+ catalytic system is key to obtaining high 70% yields of TA at relatively low reaction temperatures (125 °C) in short reaction times (6 h), which is likely to be due to the synergistic action of bromine and nitrate radicals in the oxidative process. Recycling studies have demonstrated that the mixed metal catalysts present in recovered mother liquors could be recycled three times in successive p-cymene oxidation reactions with no loss in catalytic activity or TA yield. Partial oxidation of p-cymene to give p-methylacetophenone (p-MA) in 55-60% yield can be achieved using a mixed CoBr2/Mn(OAc)2 catalytic system under 1 atm air for 24 h, while use of Co(NO3)2/MnBr2 under 1 atm O2 for 24 h gave p-toluic acid in 55-60% yield. Therefore, access to these simple catalytic aerobic conditions enables multiple biorenewable bulk terpene feedstocks (e.g., crude sulfate turpentine, turpentine, cineole, and limonene) to be converted into synthetically useful bio-p-MA, bio-p-toluic acid, and bio-TA (and hence bio-polyethylene terephthalate) as part of a terpene based biorefinery

Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction

We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly in the loss function for training neural networks, which is straightforward in standard machine learning (ML) frameworks enabling automatic differentiation. In contrast to recent hybrid ML approaches, our approach does not rely on embedding a specific thermodynamic model inside the neural network and corresponding prediction limitations. Rather, Gibbs-Duhem consistency serves as regularization, with the flexibility of ML models being preserved. Our results show increased thermodynamic consistency and generalization capabilities for activity coefficient predictions by Gibbs-Duhem-informed graph neural networks and matrix completion methods. We also find that the model architecture, particularly the activation function, can have a strong influence on the prediction quality. The approach can be easily extended to account for other thermodynamic consistency conditions