An experimental verification of morphology of ibuprofen crystals from CAMD designed solvent

In our previous work [Karunanithi et al., 2006. A computer-aided molecular design framework for crystallization solvent design. Chemical Engineering Science 61, 1247-1260] we proposed a computer-aided molecular design (CAMD) framework to design solvents for crystallization processes. One of the important aspects of that work was the consideration of a qualitative property, namely crystal morphology, along with other physico-chemical properties (quantitative) of the solvents within the modeling framework. However, it is our view that consideration of any qualitative property, such as morphology of crystals formed from solvents, necessitates additional experimental verification steps. In this work we report the experimental verification of crystal morphology for the case study, solvent design for ibuprofen crystallization, presented in Karunanithi et al. [2006. A computer-aided molecular design framework for crystallization solvent design. Chemical Engineering Science 61, 1247-1260]. This we believe is an important step for the validation of the proposed solvent design model.     

A computer-aided molecular design framework for crystallization solvent design

One of the key decisions in designing solution crystallization processes is the selection of solvents. In this paper, we present a computer-aided molecular design (CAMD) framework for the design and selection of solvents and/or anti-solvents for solution crystallization. The CAMD problem is formulated as a mixed integer nonlinear programming (MINLP) model. Although, the model allows any combination of performance objectives and property constraints, in the case studies, potential recovery was considered as the performance objective. The latter, needs to be maximized, while other solvent property requirements such as solubility, crystal morphology, flashpoint, toxicity, viscosity, normal boiling and melting point are posed as constraints. All the properties are estimated using group contribution methods. The MINLP model is then solved using a decomposition approach to obtain optimal solvent molecules. Solvent design and selection for two types of solution crystallization processes namely cooling crystallization and drowning out crystallization are presented. In the first case study, the design of single compound solvent for crystallization of ibuprofen, which is an important pharmaceutical compound, is addressed. One of the important issues namely, the effect of solvent on the shape of ibuprofen crystals is also considered in the MINLP model. The second case study is a mixture design problem where an optimal solvent/anti-solvent mixture is designed for crystallization of ibuprofen by the drowning out technique. For both case studies the performance of the solvents are verified qualitatively through SLE diagrams.     

通过模型为基础的溶液选择来调控药品化合物的形貌

In this paper we present a strategy for tuning the crystal morphology of pharmaceutical compounds by the appropriate choice of solvent via an optimization model. A three-stage approach involving a pre-design stage, a product design stage and a post-design experimental verification stage is presented. The pre-design stage addresses the tormulation of the property constraint tor crystal morphology. This involves crystallization experiments aria development of property models and constraints for morphology. In the design stage various property requirements for the solvent along with crystal morphology are considered and the product design problem is formulated as a mixed integer nonlinear programming model.The design stage provides an optimal solvent/list of candidate solvents. Similar to the pre-design stage, in the post design experimental verification stage, the morphology of the crystals （precipitated from the designed solvent） is verified through crystallization experiments followed by product characterization via scanni＇ng electron microscopy, powder X-ray diffraction imaging and Fourier transform spectra analysis.     

通过模型为基础的溶液选择来调控药品化合物的形貌

In this paper we present a strategy for tuning the crystal morphology of pharmaceutical compounds by the appropriate choice of solvent via an optimization model. A three-stage approach involving a pre-design stage, a product design stage and a post-design experimental verification stage is presented. The pre-design stage addresses the formulation of the property constraint for crystal morphology. This involves crystallization experiments and development of property models and constraints for morphology. In the design stage various property requirements for the solvent along with crystal morphology are considered and the product design problem is formulated as a mixed integer nonlinear programming model. The design stage provides an optimal solvent/list of candidate solvents. Similar to the pre-design stage, in the post design experimental verification stage, the morphology of the crystals (precipitated from the designed solvent) is verified through crystallization experiments followed by productcharacterization via scanning electron microscopy, powder X-ray diffraction imaging and Fourier transform spectra analysis.     

Machine learning-based atom contribution method for the prediction of surface charge density profiles and solvent design

Solvents are widely used in chemical processes. The use of efficient model-based solvent selection techniques is an option worth considering for rapid identification of candidates with better economic, environment and human health properties. In this paper, an optimization-based MLAC-CAMD framework is established for solvent design, where a novel machine learning-based atom contribution method is developed to predict molecular surface charge density profiles (σ-profiles). In this method, weighted atom-centered symmetry functions are associated with atomic σ-profiles using a high-dimensional neural network model, successfully leading to a higher prediction accuracy in molecular σ-profiles and better isomer identifications compared with group contribution methods. The new method is integrated with the computer-aided molecular design technique by formulating and solving a mixed-integer nonlinear programming model, where model complexities are managed with a decomposition-based strategy. Finally, two case studies involving crystallization and reaction are presented to highlight the wide applicability and effectiveness of the MLAC-CAMD framework.     

ε-CL-20在二元混合溶剂中晶体形貌的分子动力学模拟

利用分子动力学模拟的方法探究了乙酸乙酯与二溴甲烷组成的二元溶剂在298.15 K,1 atm下对ε-CL-20晶体形貌的影响。通过修正附着能模型(MAE)模型探究了溶剂-晶体相互作用,用分子动力学模拟预测了不同组成的乙酸乙酯/二溴甲烷混合溶剂中ε-CL-20的晶体形貌并与实验获得ε-CL-20的晶体形貌进行了对比。结果表明,实验获得的晶体形貌与模拟结果一致,且晶面粗糙度越大,溶剂-晶体相互作用越强。此外,还通过均方位移(MSD)分析了溶剂分子在不同晶面的扩散系数,探究了溶剂扩散速率对不同晶面的影响,并利用径向分布函数(RDF)分析了溶剂分子与晶体分子间相互作用的组成。     

The role of solute conformation,solvent–solute and solute–solute interactions in crystal nucleation

The induction time for nucleation can differ based on the solutions used to conduct a crystallization, which can in turn impact the efficiency and economics of a crystallization process, the crystal size distribution, the morphology and ultimately functionality of the final product. Establishing a link between the nucleation pathway/solution structure and nucleation induction time is essential to achieve improved comprehension of the process of crystal nucleation from solution. In this study, the role of solute conformation, solvent–solute interaction, and solute–solute interaction in nucleation was examined using tolbutamide as a model compound in toluene and toluene–alcohol solutions. Through a combination of induction time experiments, attenuated total reflection Fourier transformed infrared spectroscopy, nuclear magnetic resonance spectroscopy, molecular dynamics simulations, and quantum chemical calculations, it was found that not only solvent–solute interactions but also solute–solute interactions and structural similarities between molecular self-assemblies in the solution and synthons in the crystal structure, can significantly influence the nucleation induction time.     

CL-20结晶形貌的预测

为研究六硝基六氮杂异伍兹烷(CL-20)在真空和溶剂中的晶体形貌和生成机理,基于CL-20的晶胞参数和主要生长面,分别构建了CL-20晶体模型以及晶面和水、甲醇分子的六种吸附模型,用分子动力学模拟计算出溶剂分子与各晶面的吸附能,并据此修正各晶面的附着能,用附着能法对CL-20在真空、水和甲醇中的结晶形貌进行了预测,并与实验结果进行了对比。结果表明,真空中生长基元在晶面的极化程度越高,该面生长越快,且外形为长四棱柱;水和甲醇均会抑制各面的生长,且但程度不同,致使CL-20在水中结晶成扁平的六面体,在甲醇中为四棱柱。     

溶剂对对苯二酚晶体晶习的影响

采用冷却结晶方法,考察了水、甲醇、乙醇、正丙醇、异丙醇和正丁醇等溶剂对对苯二酚晶习的影响,并通过扫描电镜、差热扫描分析、X射线衍射等手段对对苯二酚晶体进行了分析表征.结果表明,溶剂性质显著影响对苯二酚晶体的晶习,随着溶剂介电常数的减小,晶体的长径比变小.在利用分子模拟软件Cerius²预测对苯二酚晶体晶习的基础上,初步揭示了溶剂影响对苯二酚晶体晶习的机理.     

定量结构-性质关系在化合物溶解度预测中的研究进展

溶解度是物质十分重要的一种理化性质,其在化工过程、药物和环境等领域的重要性不可忽视.定量结构-性质关系(quantitative structure-property relationship,QSPR)在化合物溶解度预测中得到广泛的应用.本文介绍了QSPR方法建立溶解度预测模型的研究进展,在总结各类分子描述符和构建溶解度预测模型方法的基础上,分别归纳出三类分子描述符(组成描述符、试验参数及理论计算描述符)和建模方法(线性、非线性及两者联合法),并从不同角度分析它们各自所拥有的特点,比较三类建模的优缺点.最后论述了当前溶解度QSPR研究中存在的不足及未来溶解度预测模型的发展趋势,指出溶解度的预测模型精度有待进一步提高,今后应更关注对化合物在不同pH值、温度、溶剂等更复杂情况下的溶解度预测.     

Computer-Aided Design of Crosslinked Polymer Membrane Using Machine Learning and Molecular Dynamics

The formation of crosslinking network between polymer chains has significant influence on polymer properties. In particular, the crosslinked structure of ionic networks like proton exchange membrane affects the conductivity performance. To further develop in this area, a framework for polymer membrane design based on the developed quantitative prediction model of the properties of crosslinked polymer is proposed. First, polymers with different crosslinking degrees are constructed by a crosslinking algorithm. Next, molecular dynamics is used to calculate the properties of crosslinked polymers. Then, the quantitative relationship between crosslinked polymer structures and macroscopical properties is developed. Subsequently, computer-aided polymer design method is integrated with the developed quantitative predict model. The crosslinked polymer design problem is expressed as an optimization problem to obtain the optimal crosslinking degree. Bayesian optimization strategy is used to solve the established optimization model. Finally, two case studies of perfluoro sulfonic acid and perfluoro imide acid design are given to illustrate the application of the proposed polymer design framework.     

Crystal shape modification through cycles of dissolution and growth: Attainable regions and experimental validation

The impact of particle shape on end‐use efficacy and downstream processing efficiency has driven industrial and academic efforts to control/manipulate the shapes obtained by crystallization. Strategies for controlling crystal shape have focused primarily on chemical routes; with shapes optimized through either solvent selection or the use of growth inhibiting additives. However, the chemical design space for crystallization may be limited, and/or the additional separation and purification steps required to remove additives or engineered solvents may be uneconomical. The application of cycles of dissolution and growth as a means for attaining desired crystal shapes is examined. A dynamic model for determining the shapes that can be attained by cycling is developed, and the results of proof of concept experiments performed using adipic acid and paracetamol are presented. The predictions obtained using the dynamic model were shown to be in good agreement with the results from the paracetamol experiments. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Determination of crystalline thermodynamics and behavior of anthracene in different solvents

To screen suitable solvents for anthracene crystallization, the solubilities of anthracene and metastable zone width were determined in four different solvents, N,N‐dimethyl formamide (DMF), xylene, tetrachloroethylene, and diethylene glycol dimethyl ether from 30 to 80 °C at atmospheric pressure using a self‐made crystallizer. The cooling modes, solvents, and the effects of carbazole on the solvent crystallization process of anthracene were also investigated. The composition of mother liquors and solid products were measured by gas chromatography; the solids were analyzed by scanning electron microscope, X‐ray diffractometer, differential scanning calorimetry, granulometer, and fluorescence spectrometer. The results showed that a uniform anthracene crystal was obtained when using DMF under the forced circulation cooling mode. Solid solution of anthracene and carbazole was initially detected in solvent crystallization. The existence of carbazole in solution has an obvious effect on the crystal morphology of anthracene, to some extent, is beneficial to the crystal growth of anthracene. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2160–2167, 2018     

基于高阶基团贡献法与COSMO-SAC模型的溶剂设计方法

提出了一种基于高阶基团贡献法与类导体屏蔽片段活度系数模型（conductor like screening model-segment activity coefficient, COSMO-SAC）的计算机辅助溶剂设计方法（computer-aided molecular design, CAMD）。首先,基于高阶基团贡献法（higher-order group contribution, GC⁺）与COSMO-SAC模型构建GC⁺-COSMO方法,关联分子基团组合与表面屏蔽电荷密度分布[σ-profiles, p(σ)]、分子空腔体积V_c,实现对二者的高通量预测;然后结合基于简化分子线性输入系统（simplified molecular input line entry system, SMILES）的异构体生成算法与GC⁺-COSMO方法实现CAMD技术对异构体的识别及性质区分;最后,通过目标函数与约束方程组成的混合整数非线性规划模型（mixed integer nonlinear programming, MINLP）来建立溶剂设计问题,进一步采用分解式算法优化求解,实现溶剂优化设计目标。基于以上模型和方法开展了狄尔斯-阿尔德（Diels-Alder, DA）竞争性反应溶剂设计,验证了提出的方法的可行性与有效性。     

萃取精馏溶剂的选择(Ⅰ)溶剂分子QSPR的人工神经网络模型

在分子模拟的基础上选择萃取精馏溶剂首先要建立描述溶剂分子结构与其性质之间定量关系(QSPR)的数学模型。本文采用分子连接性指数(MCI)表示溶剂分子的二维拓扑结构,它不仅可以表示构成分子的原子或基团的种类与数目，而且还可以反映原子或基团之间相互连接的特征;而溶剂主要的性质指标，如选择性与溶解性，都是无限稀释活度因子γ∞的函数。由于分子结构与γ∞之间的关系十分复杂，所以利用人工神经网络(ANN)模型描述溶剂的QSPR。组建了具有广泛代表性的数据库，并采用BP算法对ANN模型进行训练，证明训练成熟的ANN模型可以更准确地计算γ∞，优于当前普遍使用的UNIFAC方法。     

一种简化的萃取溶剂分子设计方法

本研究提出一种新的萃取溶剂分子设计策略，首先以溶剂选择性为标准预选官能团，缩小分子设计的范围；再应用遗传算法对预选出的官能团进行组合，设计出符合要求的萃取溶剂分子。采用本设计的方法模拟计算了两个萃取溶剂设计实例，获得了令人满意的效果。