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The combination of ethyl (hydroxyimino)cyanoacetate (Oxyma) and diisopropylcarbodiimide (DIC) has demonstrated superior performance in amino acid activation for peptide synthesis. However, it was recently reported that Oxyma and DIC could react to generate undesired hydrogen cyanide (HCN) at 20 °C, raising safety concerns for the practical use of this activation strategy. To help minimize the risks, there is a need for a comprehensive investigation of the mechanism and kinetics of the generation of HCN. Here we show the results of the first systematic computational study of the underpinning mechanism, including comparisons with experimental data. Two pathways for the decomposition of the Oxyma/DIC adduct are derived to account for the generation of HCN and its accompanying cyclic product. These two mechanisms differ in the electrophilic carbon atom attacked by the nucleophilic sp2-nitrogen in the cyclization step and in the cyclic product generated. On the basis of computed “observed” activation energies, ΔGobs⧧, the mechanism that proceeds via the attack of the sp2-nitrogen at the oxime carbon is identified as the most kinetically favorable one, a conclusion that is supported by closer agreement between predicted and experimental 13C NMR data. These results can provide a theoretical basis to develop a design strategy for suppressing HCN generation when using Oxyma/DIC for amino acid activation.

The molecular structures of the first and second generation sulflowers, sulflower and persulfurated coronene (PSC), are remarkably similar: carbon ring structures decorated with sulfur atoms, without any additional moiety.

Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know

We present a systematic process-wide solvent selection tool based on computer-aided mixture/blend design (CAMbD) (Gani, 2004) for the integrated synthesis, crystallisation and isolation of pharmaceutical compounds. The method proposed simultaneously identifies the solvent and/or antisolvent mixture, mixture composition and process temperatures that optimise one or more key performance indicators. Additionally, the method entails comprehensive design specifications for the integrated process, such as the miscibility of the synthesis, crystallisation and wash solvents. The design approach is illustrated by identifying optimal solvent mixtures for the synthesis, crystallisation and isolation of mefenamic acid. Furthermore, a multi-objective CAMbD problem is formulated and shows that a mefenamic acid with purity of 98.8% can be achieved without significant loss of process performance in terms of the solvent E-factor.

This paper aims to provide an overview of the current state of the art in molecule and material design and a perspective on open questions. First, we discuss the interplay between process design and the design of molecules and materials and then present the basic trade-offs and interdependencies that need to be considered in this integrated design problem. Second, we introduce methods and open questions in the area of processing materials, with special emphasis placed on heterogeneous catalysts due to their importance in the chemical industry. Finally, we discuss materials for photovoltaic cells as one example of a chemical product that is likely to help supply sustainable energy and address climate change concerns. What is presented here is based on presentations and discussions during the FIPSE-4 meeting in June 2018 (https://www.fi-in-pse.org/fipse-4-2018).

The best route to uncover the mechanism of chemical reactions remains a topic of intense debate. In this work, we deploy a three-faceted approach that combines experimental probing, detailed kinetic modelling and quantum-mechanical calculations for the study of the mechanism and regioselectivity of a Williamson ether synthesis, which is of interest because of its simplicity and its broad scope in laboratory and industrial synthesis. The choice of solvent is found to have a large impact on the experimental regioselectivity, with ratios of O-alkylated to C-alkylated product at 298 K of 97 : 3 in acetonitrile and of 72 : 28 in methanol. Through experiments and kinetic modelling, we identify reaction networks that differ significantly from solvent to solvent, providing insights into the factors (proton-exchange, solvolysis and product degradation) that impact on regioselectivity and the relative rates of O-alkylation and C-alkylation. The kinetic models yield detailed information on reaction rates and energy barriers and on the existence of an additional double alkylation pathway. We carry out quantum mechanical calculations and elucidate the transition states for the two main alkylation pathways. The quantum-mechanical calculations highlight structural differences between the transition states found for the two alkylation pathways and provide information on the effect of the solvent on the stabilisation/destabilisation of various structures and hence on reaction selectivity. The three-faceted approach provides complementary information into the elementary steps of the reaction mechanism.

We present the latest developments in thermodynamic modeling using the SAFT-γ Mie group-contribution equation of state. The group database is updated, featuring now 58 groups; this expanded databas...

Industrial activity produces an enormous amount of waste heat energy in the form of low-temperature sources, the utilisation of which remains challenging due to low efficiencies and poor economic viability. Organic Rankine cycles (ORCs) offer an appealing solution to this challenge because the ORC efficiency can be drastically increased by tailoring the organic working fluid to the heat source. Due to the large number of potential fluids available, however, it is difficult to identify those fluids that give the highest ORC performance. Here we present a robust computer-aided molecular and process design technique for the determination of pure-component working fluids based on ORC performance metrics. In particular, factors that lead to process infeasibility are recognised, thereby enabling a diverse exploration of the working fluid design space while reducing the computational cost of the method. These algorithmic developments, in combination with the use of the predictive SAFT-γ Mie group-contribution methodology, provide a reliable platform for designing a wide range of ORCs. Excellent capability for determining optimal ORC designs from a vast number (58 960) of prospective fluids is demonstrated through three case studies. The optimal fluids identified include low molecular weight alkanes, alkenes, and ethers, such as propane, pent-1,4-diene and methyl ethyl ether. Importantly, we find that the performance of these fluids is not always transferable across applications. An analysis of fluid properties further highlights the limitations of using a heuristic approach for fluid selection. Instead, a holistic consideration of the working fluid in the cycle can lead to increased ORC performance.

The first-order thermodynamic perturbation theory of Wertheim (TPT1) is extended to treat ring aggregates, formed by inter- and intramolecular association. The expression for the residual associati...

Choosing a solvent and an antisolvent for a new crystallization process is challenging due to the sheer number of possible solvent mixtures and the impact of solvent composition and crystallization temperature on process performance. To facilitate this choice, we present a general computer aided mixture/blend design (CAMbD) formulation for the design of optimal solvent mixtures for the crystallization of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent, antisolvent, and composition of solvent mixture. The SAFT-γ Mie group-contribution approach is used in the design of crystallization solvents; based on an equilibrium model, both the crystal yield and solvent consumption are considered. The design formulation is implemented in gPROMS and applied to the crystallization of lovastatin and ibuprofen, where a hybrid approach combining cooling and antisolvent crystallization is compared to each method alone. For lovastatin, the use of a hybrid approach leads to an increase in crystal yield compared to antisolvent crystallization or cooling crystallization. Furthermore, it is seen that using less volatile but powerful crystallization solvents at lower temperatures can lead to better performance. When considering ibuprofen, the hybrid and antisolvent crystallization techniques provide a similar performance, but the use of solvent mixtures throughout the crystallization is critical in maximizing crystal yields and minimizing solvent consumption. We show that our more general approach to rational design of solvent blends brings significant benefits for the design of crystallization processes in pharmaceutical and chemical manufacturing.

The application of crystal structure prediction (CSP) to industrially relevant molecules requires the handling of increasingly large and flexible compounds. A revised model for the effect of molecular flexibility on the lattice energy that removes the discontinuities and non-differentiabilities present in earlier models (Sugden et al., 2016), with a view to improving the performance of CSP is presented. The approach is based on the concept of computing a weighted average of local models, and has been implemented within the CrystalPredictor code. Through the comparative investigation of several compounds studied in earlier literature, it is shown that this new model results in large reductions in computational effort (of up to 65%) and in significant increases in reliability. The approach is further applied to investigate, for the first time, the computational polymorphic landscape of flufenamic acid for Z′ = 1 structures, resulting in the successful identification of all three experimentally resolved polymorphs within reasonable computational time.

Of central importance to the $$\alpha $$ BB algorithm is the calculation of the $$\alpha $$ values that guarantee the convexity of the underestimator. Improvement (reduction) of these values can result in tighter underestimators and thus increase the performance of the algorithm. For instance, it was shown by Wechsung et al. (J Glob Optim 58(3):429–438, 2014) that the emergence of the cluster effect can depend on the magnitude of the $$\alpha $$ values. Motivated by this, we present a refinement method that can improve (reduce) the magnitude of $$\alpha $$ values given by the scaled Gerschgorin method and thus create tighter convex underestimators for the $$\alpha $$ BB algorithm. We apply the new method and compare it with the scaled Gerschgorin on randomly generated interval symmetric matrices as well as interval Hessians taken from test functions. As a measure of comparison, we use the maximal separation distance between the original function and the underestimator. Based on the results obtained, we conclude that the proposed refinement method can significantly reduce the maximal separation distance when compared to the scaled Gerschgorin method. This approach therefore has the potential to improve the performance of the $$\alpha $$ BB algorithm.

The prediction of the crystal structures that a given organic molecule is likely to form is an important theoretical problem of significant interest for the pharmaceutical and agrochemical industries, among others. As evidenced by a series of six blind tests organized over the past 2 decades, methodologies for crystal structure prediction (CSP) have witnessed substantial progress and have now reached a stage of development where they can begin to be applied to systems of practical significance. This article reviews the state of the art in general-purpose methodologies for CSP, placing them within a common framework that highlights both their similarities and their differences. The review discusses specific areas that constitute the main focus of current research efforts toward improving the reliability and widening applicability of these methodologies, and offers some perspectives for the evolution of this technology over the next decade.

The SAFT‐ɣ Mie group‐contribution equation of state is used to represent the fluid‐phase behaviour of aqueous solutions of a variety of linear, branched, and cyclic amines. New group interactions are developed in order to model the mixtures of interest, including the like and unlike interactions between alkyl primary, secondary, and tertiary amine groups (NH2, NH, N), cyclic secondary and tertiary amine groups (cNH, cN), and cyclohexylamine groups (cCHNH, cCHN) with water (H2O). The group‐interaction parameters are estimated from appropriate experimental thermodynamic data for pure amines and selected mixtures. By taking advantage of the group‐contribution nature of the method, one can describe the fluid‐phase behaviour of mixtures of molecules comprising those groups over broad ranges of temperature, pressure, and composition. A number of aqueous solutions of amines are studied including linear, branched aliphatic, and cyclic amines. Liquid‐liquid equilibria (LLE) bounded by lower critical solution temperatures (LCSTs) have been reported experimentally and are reproduced here with SAFT‐ɣ Mie approach. The main feature of the approach is the ability not only to represent accurately the experimental data employed in the parameter estimation, but also to predict the vapour‐liquid, liquid‐liquid, and vapor‐liquid‐liquid equilibria, and LCSTs with the same set of parameters. Pure compound and binary phase diagrams of diverse types of amines and their aqueous solutions are assessed in order to demonstrate the main features of the thermodynamic and fluid‐phase behaviour.

In the field of Computer-Aided Molecular and Process Design (CAMPD), a variety of solution methods have been developed to handle the complexities associated with the non-convexity and non-linearity of molecular structure-property and process models. However, mostalgorithms are prone to failing to generate feasible solutions when the integrated solvent-process model renders a significant portion of the search space infeasible. In this work, we propose a solution approach for the integrated design of an optimal chemical absorption process in which tailored feasibility tests are incorporated into a process optimisation problem. The solution approach allows the exploration of a design space without unnecessary difficulties by recognising infeasibilities. The effectiveness of the approach is demonstrated on an aqueous amine solvent-based CO2 capture process.

Mixed-integer polynomial programs (MIPOPs) frequently arise in chemical engineering applications such as pooling, blending and operations planning. Many global optimization solvers rely on mixed-integer linear (MIP) relaxations of MIPOPs and solve them repeatedly as part of a branch-and-bound algorithm using commercial MIP solvers. GUROBI, one of the prominent MIP solvers, recently added the capability to solve mixed-integer quadratically-constrained quadratic programs (MIQCQPs). This paper investigates global optimization of MIPOPs via their reformulation as MIQCQPs followed by their solution to global optimality using GUROBI. The effectiveness of this approach is tested on 60 instances of MIPOPs selected from the library MINLPLib. The performance of the MIQCQP reformulation approach is compared to the state-of-the-art global solvers BARON, ANTIGONE and SCIP in GAMS. For the case of single threading, a reduction of 28% and 42% compared to SCIP and ANTIGONE respectively is observed. This approach, therefore, holds promise for integration into existing global solvers to handle MIPOPs.

The distribution of ionic species in electrolyte systems is important in many fields of science and engineering, ranging from the study of degradation mechanisms to the design of systems for electrochemical energy storage. Often, other phenomena closely related to ionic speciation, such as ion pairing, clustering and hydrogen bonding, which are difficult to investigate experimentally, are also of interest. Here, we develop an accurate molecular approach, accounting for reactions as well as association and ion pairing, to deliver a predictive framework that helps validate experiment and guides future modelling of speciation phenomena of weak electrolytes. We extend the SAFT-VRE Mie equation of state [D. K. Eriksen et al., Mol. Phys., 2016, 114, 2724–2749] to study aqueous solutions of nitric, sulphuric, and carbonic acids, considering complete and partially dissociated models. In order to incorporate the dissociation equilibria, correlations to experimental data for the relevant thermodynamic equilibrium constants of the dissociation reactions are taken from the literature and are imposed as a boundary condition in the calculations. The models for water, the hydronium ion, and carbon dioxide are treated as transferable and are taken from our previous work. We present new molecular models for nitric acid, and the nitrate, bisulfate, sulfate, and bicarbonate anions. The resulting framework is used to predict a range of phase behaviour and solution properties of the aqueous acids over wide ranges of concentration and temperature, including the degree of dissociation, as well as the activity coefficients of the ionic species, and the activity of water and osmotic coefficient, density, and vapour pressure of the solutions. The SAFT-VRE Mie models obtained in this manner provide a means of elucidating the mechanisms of association and ion pairing in the systems studied, complementing the experimental observations reported in the literature.