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Donald P. Visco - One of the best experts on this subject based on the ideXlab platform.
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An Application of Computer‐Aided Molecular Design (CAMD) Using the Signature Molecular Descriptor–Part 2. Evaluating Newly Identified Surface Tension‐Reducing Substances for Potential Use as Shrinkage‐Reducing Admixtures
Journal of the American Ceramic Society, 2013Co-Authors: Natalia Shlonimskaya, Joseph J. Biernacki, Hamed M. Kayello, Donald P. ViscoAbstract:In this study, the use of Computer-Aided Molecular Design (CAMD) is validated as a tool for enabling the discovery of new shrinkage-reducing compounds for possible use in portland cement composites and is framed as one of many multiscale modeling tools in a broad hierarchy of possibilities. Twelve additives were tested for their ability to inhibit shrinkage in Type I ordinary portland cement under both autogenous and drying conditions. The 12 additives included two commercial shrinkage-reducing admixtures (SRAs), two active ingredients of a commercial admixture [one of which was used to establish the quantitative structure–property relationships (QSPR)], two additional classified as potential SRA compounds based on the patent literature, four newly identified compounds predicted by using CAMD and an inverse quantitative structure–property relationship (I-QSPR), and two other compounds use to establish the QSPR relationship. The newly identified I-QSPR compounds were targeted for their ability to reduce the surface tension of water, a primary consideration for shrinkage-reducing activity. Results for both drying shrinkage and autogenous shrinkage indicate that the Designed compounds perform similar to commercial admixtures, yet have different chemical functionalities. Hydration data and set measurements were also considered since selection of new SRAs is a multiparameter problem with many constraints. Thus, these newly identified shrinkage-reducing compounds can potentially provide additional options for use in portland cement concrete applications.
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An Application of Computer‐Aided Molecular Design (CAMD) Using the Signature Molecular Descriptor—Part 1. Identification of Surface Tension Reducing Agents and the Search for Shrinkage Reducing Admixtures
Journal of the American Ceramic Society, 2013Co-Authors: Hamed M. Kayello, Joseph J. Biernacki, Natalia Shlonimskaya, Naresh Kumar Reddy Tadisina, Donald P. ViscoAbstract:The development of new admixtures for concrete is normally an experimental endeavor in that the Molecular scaffolds of existing admixtures are modified and tested. This approach is time consuming, incremental and typically expensive. Alternatively, a Computer-Aided Molecular Design (CAMD) approach is proposed that uses the Signature Molecular descriptor. CAMD is the application of computer-implemented algorithms that are utilized to Design molecules with optimally predicted properties such that they can be tested and evaluated for efficacy. The property of interest here is the surface tension of compounds in aqueous solutions as this property is related to shrinkage in concrete. In particular, we have chosen two classes of compounds, amines and glycol ethers, as they present opportunities for use as shrinkage reducing admixtures (SRAs). By evaluating the initial surface tension reduction in these compounds in solution with water, a number of structure–property conjectures associated with the effect of these compounds were developed. From these conjectures, 14 compounds were identified and utilized as a training set for the CAMD of new compounds. After creating and refining a quantitative structure–property relationship (QSPR) model for surface tension reduction, a structure enumeration algorithm was employed to generate structures outside of the original training set that have optimally predicted properties. In work, the CAMD approach is introduced as well as the identification of new compounds with the greatest predicted impact on the surface tension reduction in water. Furthermore, the surface tension reduction for the newly identified compounds was experimentally evaluated.
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Computer-Aided Molecular Design using the Signature Molecular descriptor: Application to solvent selection
Computers & Chemical Engineering, 2010Co-Authors: Derick C. Weis, Donald P. ViscoAbstract:Abstract There is a growing demand to develop more environmentally friendly solvents to reduce costs and comply with regulation. Researchers at GlaxoSmithKline (GSK) have developed a solvent selection guide that ranks 47 frequently used solvents from 1 to 10 in five areas related to environmental compatibility. In this work, we apply a Computer-Aided Molecular Design method known as inverse Design with the Signature Molecular descriptor to identify additional potentially green solvents outside of GSK's list. Applying this approach is much quicker, less expensive and allows for a more comprehensive search for the most suitable candidates than working with experimental data alone. We present results for solvents with optimal predicted properties that span the classes from the 47 compounds in the GSK solvent selection guide and include several which are hybrids that cross-cut amongst classes. Additionally, our technique “rediscovers” the known green solvent ethyl lactate through this method by combining different solvent classes.
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Computer-Aided Molecular Design: Approaches and Applications
2009Co-Authors: Donald P. ViscoAbstract:Presented on November 4, 2009, from 4-5 pm in room G011 of the Molecular Science and Engineering Building on the Georgia Tech Campus.
Rafiqul Gani - One of the best experts on this subject based on the ideXlab platform.
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Deep learning and knowledge-based methods for Computer-Aided Molecular Design—toward a unified approach: State-of-the-art and future directions
Computers & Chemical Engineering, 2020Co-Authors: Abdulelah S. Alshehri, Rafiqul Gani, Fengqi YouAbstract:Abstract The optimal Design of compounds through manipulating properties at the Molecular level is often the key to considerable scientific advances and improved process systems performance. This paper highlights key trends, challenges, and opportunities underpinning the Computer-Aided Molecular Design (CAMD) problems. A brief review of knowledge-driven property estimation methods and solution techniques, as well as corresponding CAMD tools and applications, are first presented. In view of the computational challenges plaguing knowledge-based methods and techniques, we survey the current state-of-the-art applications of deep learning to Molecular Design as a fertile approach towards overcoming computational limitations and navigating uncharted territories of the chemical space. The main focus of the survey is given to deep generative modeling of molecules under various deep learning architectures and different Molecular representations. Further, the importance of benchmarking and empirical rigor in building deep learning models is spotlighted. The review article also presents a detailed discussion of the current perspectives and challenges of knowledge-based and data-driven CAMD and identifies key areas for future research directions. Special emphasis is on the fertile avenue of hybrid modeling paradigm, in which deep learning approaches are exploited while leveraging the accumulated wealth of knowledge-driven CAMD methods and tools.
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A machine learning based Computer-Aided Molecular Design/screening methodology for fragrance molecules
Computers & Chemical Engineering, 2018Co-Authors: Lei Zhang, Haitao Mao, Linlin Liu, Rafiqul GaniAbstract:Abstract Although the business of flavors and fragrances has become a multibillion dollar market, the Design/screening of fragrances still relies on the experience of specialists as well as available odor databases. Potentially better products, however, could be missed when employing this approach. Therefore, a Computer-Aided Molecular Design/screening method is developed in this work for the Design and screening of fragrance molecules as an important first step. In this method, the odor of the molecules are predicted using a data driven machine learning approach, while a group contribution based method is employed for prediction of important physical properties, such as, vapor pressure, solubility parameter and viscosity. A MILP/MINLP model is established for the Design and screening of fragrance molecules. Decomposition-based solution approach is used to obtain the optimal result. Finally, case studies are presented to highlight the application of the proposed fragrance Design/screening method.
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Chapter 6 – Computer-Aided Molecular Design and Property Prediction
Computer Aided Chemical Engineering, 2016Co-Authors: Rafiqul Gani, L. Zhang, Sawitree Kalakul, Stefano CignittiAbstract:Abstract Today's society needs many chemical-based products for its survival, nutrition, health, transportation, agriculture, and the functioning of processes. Chemical-based products have to be Designed/developed in order to meet these needs, while at the same time, they must be innovative and sustainable to meet the global challenges of resources, competition, and demand. Design/development of these products mostly follows experiment-based trial and error approaches. With the availability of reliable property prediction models, however, Computer-Aided techniques have become popular, at least for the initial stages of the Design/development process. Therefore, Computer-Aided Molecular Design and property prediction techniques are two topics that play important roles in chemical product Design, analysis, and application. In this chapter, an overview of the concepts, methods, and tools related to these two topics are given. In addition, a generic Computer-Aided framework for the Design of molecules, mixtures, and blends is presented. The application of the framework is highlighted for Molecular products through two case studies involving the Design of refrigerants and surfactants.
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Generic mathematical programming formulation and solution for Computer-Aided Molecular Design
Computers & Chemical Engineering, 2015Co-Authors: Lei Zhang, Stefano Cignitti, Rafiqul GaniAbstract:Abstract This short communication presents a generic mathematical programming formulation for Computer-Aided Molecular Design (CAMD). A given CAMD problem, based on target properties, is formulated as a mixed integer linear/non-linear program (MILP/MINLP). The mathematical programming model presented here, which is formulated as an MILP/MINLP problem, considers first-order and second-order Molecular groups for Molecular structure representation and property estimation. It is shown that various CAMD problems can be formulated and solved through this model.
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Biodiesel Process Design through a Computer-Aided Molecular Design Approach
2009Co-Authors: Arunprakash T. Karunanithi, Rafiqul Gani, Luke E. K. AchenieAbstract:This paper proposes the application of Computer-Aided Molecular Design methods for the Design of biodiesel processes. Solvents are used in two stages during bio diesel production. In the first stage solvents are used for extraction of oil from seed feedstock and in the second stage solvents are used to facilitate the conversion of oil into biodiesel. This paper introduces the application of state of the art Computer-Aided Molecular Design methodology for the Design/selection of solvents for the conversion of oil seed feedstock to biodiesel. A case study involving Design of co-solvents for the promotion of transesterification reaction during the production of biodiesel from soybean oil is presented. The Designed co-solvent is intended to increase the reaction rate by forming a single phase solution of the reactants.
Nishanth G. Chemmangattuvalappil - One of the best experts on this subject based on the ideXlab platform.
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Design of fragrant molecules through the incorporation of rough sets into Computer-Aided Molecular Design
Molecular Systems Design & Engineering, 2020Co-Authors: Kirridharhapany T. Radhakrishnapany, Raymond R. Tan, Chee Yan Wong, Fang Khai Tan, Jia Wen Chong, Kathleen B. Aviso, Jose Isagani B. Janairo, Nishanth G. ChemmangattuvalappilAbstract:Design and screening of fragrances based on experiments or experiences of specialists can overlook potentially better fragrance products. To overcome this issue, a systematic mathematical programming-based approach is developed for the Design of fragrant molecules. A novel data-driven rough set-based machine learning (RSML) model is utilised as a predictive or diagnostic modelling tool for odour properties. RSML generates deterministic rules based on the relationship between the topology of fragrant molecules and their odour characters elicited from an existing odour database. The rules generated are then integrated as constraints into a Computer-Aided Molecular Design (CAMD) problem. The CAMD framework also involves other relevant properties such as diffusion coefficient, vapour pressure, viscosity, LC50 and solubility parameter which are predicted using a group contribution (GC) method. Since there are different types of models involved in the prediction of various attributes, Molecular signature descriptors are utilised as the common platform that links machine learning and other predictive models in a CAMD problem. The application of the new Design method is demonstrated through a case study to Design fragrant molecules for shampoo additives with desirable physical and environmental properties. The results indicate the ability of the novel method in identifying non-intuitive and promising fragrant molecules that can be used for various applications.
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Development of solvent Design methodologies using Computer-Aided Molecular Design tools
Current Opinion in Chemical Engineering, 2020Co-Authors: Nishanth G. ChemmangattuvalappilAbstract:This paper presents an overview of the recent developments in the area of solvent Design using Computer-Aided Molecular Design (CAMD) tools. Effective CAMD approaches are based on reliable property prediction models. In the recent years, the application range of solvent Design has expanded into the Design of blends and formulated products. In addition, the Design of reactive solvents has been performed accurately because of the ability to predict the reaction rate constants and the Design of ionic liquids because of the development of effective activity coefficient estimation models. Because of the increasing awareness on safety, health and environmental impacts of solvents, different recent contributions focus on reducing the negative attributes of solvents along with satisfying the process Design targets.
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Reactive and non-reactive solvents as bio-oil blends: a Computer-Aided Molecular Design approach
Biomass Conversion and Biorefinery, 2019Co-Authors: Hon Huin Chin, Omar Anas Aboagwa, Jie Qi Neoh, Suchithra Thangalazhy-gopakumar, Nishanth G. ChemmangattuvalappilAbstract:This paper presents a systematic framework of Computer-Aided Molecular Design (CAMD), to Design solvents that can improve the critical properties of bio-oil. CAMD tools have been used in this work to improve the key properties of bio-oil such as heating value, water content, viscosity, acidity and stability in storage. The Design of both non-reactive and reactive solvents has been covered in this work. In both cases, the main objective was to Design solvents that form the bio-oil solvent blend with minimum solvent content. In order to model the bio-oil, five major components in the bio-oil are used and the solvent Design problem focussed on Designing this surrogate solvent. Mixing rules have been employed to estimate the properties of various bio-oil solvent blends. The main objective is to compute the minimum amount of solvent needed by identifying the optimal combination of functional groups for the solvent molecules. The improvement in properties had been tracked using group contribution models. This work also presents a framework to Design solvents that improve bio-oil properties by undergoing chemical reactions with the bio-oil components. In order to ensure the homogeneity after solvent addition, miscibility test between solvent and bio-oil is conducted using appropriate thermodynamic models. Based on the results, the use of mixtures of alkene and alcohol is more recommended as the optimal blend solvents since the mass fraction and viscosity could be balanced to meet the final heating value target and viscosity constraints of the blends.
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Design of bio-oil additives via Computer-Aided Molecular Design tools and phase stability analysis on final blends
Computers & Chemical Engineering, 2019Co-Authors: Angel Xin Yee Mah, Huin Chin, Jie Qi Neoh, Omar Anas Aboagwa, Suchithra Thangalazhy-gopakumar, Nishanth G. ChemmangattuvalappilAbstract:Abstract Direct application of bio-oil as fuel is limited by its undesirable properties such as low heating value, high viscosity, and non-homogenous aqueous and organic phases. Direct addition of solvent to bio-oil is one of the most practical approaches to improve bio-oil properties because of its simplicity and low processing cost. This inspired the Design of solvents to enhance bio-oil properties upon physical blending. One of the major challenges faced in solvent Design is the immiscibility of final blend due to the difference in polarity of solvent and bio-oil molecules. This work presents a Computer-Aided Molecular Design (CAMD) framework to identify potential solvent candidates that allow bio-oil to satisfy targeted properties with minimal solvent addition. Moreover, a model for phase stability analysis is developed based on Gibbs tangent plane distance, which covers miscibility check on solvent-oil blend, generation of phase diagram, and addition of binding agents to homogenise the blend.
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alternative solvent Design for oil extraction from palm pressed fibre via computer aided Molecular Design
2019Co-Authors: Michael Angelo B. Promentilla, Denny K S Ng, Nishanth G. ChemmangattuvalappilAbstract:Palm pressed fibre (PPF) is a by-product from palm oil milling process. There are approximately 5–7% of residual oils retained in PPF after the oil extraction process. Hexane is commonly used as solvent for extraction of the residual oil due to its low cost and high oil solubility. However, the high boiling point of hexane leads to degradation of carotenes during oil recovery. Besides, hexane is highly flammable and causes air pollution through fugitive emissions. Thus, there is interest in identifying alternative solvents to extract residual oil from PPF. In this chapter, a new approach that combines Computer-Aided Molecular Design (CAMD) and Analytic Hierarchy Process (AHP) is presented. The proposed approach can determine the alternative solvents that exert favourable attributes for oil extraction. Both physical and environmental properties are chosen as Design criteria to generate solvents with improved performance and environmental characteristics. Nonetheless, it is difficult to evaluate the relative importance of each property since properties that belong to different categories cannot be compared on a common scale. This issue needs to be addressed seriously as different relative weights will identify different solvents. The main attraction of this AHP–CAMD approach is that the relative importance weight of those identified properties can be systematically defined. AHP structures the CAMD problem in a hierarchical manner that allows physical and environmental properties to be compared under the same analysis. Through this approach, the identified alternative solvents have comparable or better performance as compared to hexane.
Luke E. K. Achenie - One of the best experts on this subject based on the ideXlab platform.
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Biodiesel Process Design through a Computer-Aided Molecular Design Approach
2009Co-Authors: Arunprakash T. Karunanithi, Rafiqul Gani, Luke E. K. AchenieAbstract:This paper proposes the application of Computer-Aided Molecular Design methods for the Design of biodiesel processes. Solvents are used in two stages during bio diesel production. In the first stage solvents are used for extraction of oil from seed feedstock and in the second stage solvents are used to facilitate the conversion of oil into biodiesel. This paper introduces the application of state of the art Computer-Aided Molecular Design methodology for the Design/selection of solvents for the conversion of oil seed feedstock to biodiesel. A case study involving Design of co-solvents for the promotion of transesterification reaction during the production of biodiesel from soybean oil is presented. The Designed co-solvent is intended to increase the reaction rate by forming a single phase solution of the reactants.
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An experimental verification of morphology of ibuprofen crystals from CAMD Designed solvent
Chemical Engineering Science, 2007Co-Authors: Arunprakash T. Karunanithi, Luke E. K. Achenie, Charles Acquah, Shanthakumar Sithambaram, Steven L. Suib, Rafiqul GaniAbstract:Abstract 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.
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Shorter Communication An experimental verification of morphology of ibuprofen crystals from CAMD Designed solvent
2007Co-Authors: Arunprakash T. Karunanithi, Luke E. K. Achenie, Charles Acquah, Shanthakumar Sithambaram, Steven L. Suib, Rafiqul GaniAbstract:Abstract In our previous work [Karunanithi et al., 2006. A Computer-Aided Molecular Design framework for crystallization solvent Design. ChemicalEngineering Science 61, 1247–1260] we proposed a Computer-Aided Molecular Design (CAMD) framework to Design solvents for crystallizationprocesses. One of the important aspects of that work was the consideration of a qualitative property, namely crystal morphology, along withother physico-chemical properties (quantitative) of the solvents within the modeling framework. However, it is our view that consideration ofany qualitative property, such as morphology of crystals formed from solvents, necessitates additional experimental verification steps. In thiswork we report the experimental verification of crystal morphology for the case study, solvent Design for ibuprofen crystallization, presentedin Karunanithi et al. [2006. A Computer-Aided Molecular Design framework for crystallization solvent Design. Chemical Engineering Science61, 1247–1260]. This we believe is an important step for the validation of the proposed solvent Design model. 2007 Elsevier Ltd. All rights reserved.
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a computer aided Molecular Design framework for crystallization solvent Design
Chemical Engineering Science, 2006Co-Authors: Arunprakash T. Karunanithi, Luke E. K. Achenie, Rafiqul GaniAbstract: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.
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On the solution of mixed-integer nonlinear programming models for computer aided Molecular Design.
Computers & Chemistry, 2002Co-Authors: G. M. Ostrovsky, Luke E. K. Achenie, Manish SinhaAbstract:This paper addresses the efficient solution of computer aided Molecular Design (CAMD) problems, which have been posed as mixed-integer nonlinear programming models. The models of interest are those in which the number of linear constraints far exceeds the number of nonlinear constraints, and with most variables participating in the nonconvex terms. As a result global optimization methods are needed. A branch-and-bound algorithm (BB) is proposed that is specifically tailored to solving such problems. In a conventional BB algorithm, branching is performed on all the search variables that appear in the nonlinear terms. This translates to a large number of node traversals. To overcome this problem, we have proposed a new strategy for branching on a set of linear branching functions, which depend linearly on the search variables. This leads to a significant reduction in the dimensionality of the search space. The construction of linear underestimators for a class of functions is also presented. The CAMD problem that is considered is the Design of optimal solvents to be used as cleaning agents in lithographic printing.
Claire S. Adjiman - One of the best experts on this subject based on the ideXlab platform.
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An approach for simultaneous Computer-Aided Molecular Design with holistic sustainability assessment: Application to phase-change CO2 capture solvents
Computers & Chemical Engineering, 2020Co-Authors: Athanasios I. Papadopoulos, Gulnara Shavalieva, Stavros Papadokonstantakis, Panos Seferlis, Felipe A. Perdomo, Amparo Galindo, George Jackson, Claire S. AdjimanAbstract:Abstract We propose an approach for the simultaneous consideration of a holistic sustainability assessment framework in Computer-Aided Molecular Design (CAMD). The framework supports the assessment of life cycle (LCA) and safety, hazard and environmental (EHS) impacts from cradle-to-gate of chemicals Designed through CAMD. It enables the calculation of a total of 11 sustainability-related indicators, aggregating several impact categories. A lack of models and data gaps in property prediction are addressed through a data mining approach which deploys on-line similarity assessment against existing molecules. The LCA and EHS assessment are conducted simultaneously with CAMD or after CAMD to assess the Designed solvents. A case study is presented on the Design of phase-change solvents for chemisorption-based post-combustion CO2 capture. The proposed approach identifies verifiably useful phase-change solvents that exhibit favourable performance trade-offs compared to a reference CO2 capture solvent. The on-line use in CAMD of sustainability criteria favours the Design of hydroxyl-containing solvents.
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Computer-Aided Molecular Design and selection of CO2 capture solvents based on thermodynamics, reactivity and sustainability
Molecular Systems Design & Engineering, 2016Co-Authors: Athanasios I. Papadopoulos, Stavros Papadokonstantakis, Panos Seferlis, Amparo Galindo, George Jackson, Sara Badr, Alexandros Chremos, Esther Forte, Theodoros Zarogiannis, Claire S. AdjimanAbstract:The identification of improved carbon dioxide (CO2) capture solvents remains a challenge due to the vast number of potentially-suitable molecules. We propose an optimization-based Computer-Aided Molecular Design (CAMD) method to identify and select, from hundreds of thousands of possibilities, a few solvents of optimum performance for CO2 chemisorption processes, as measured by a comprehensive set of criteria. The first stage of the approach involves a fast screening stage where solvent structures are evaluated based on the simultaneous consideration of important pure component properties reflecting thermodynamic, kinetic, and sustainability behaviour. The impact of model uncertainty is considered through a systematic method that employs multiple models for the prediction of performance indices. In the second stage, high-performance solvents are further selected and evaluated using a more detailed thermodynamic model, i.e. the group-contribution statistical associating fluid theory for square well potentials (SAFT-γ SW), to predict accurately the highly non-ideal chemical and phase equilibrium of the solvent–water–CO2 mixtures. The proposed CAMD method is applied to the Design of novel Molecular structures and to the screening of a data set of commercially available amines. New Molecular structures and commercially-available compounds that have received little attention as CO2 capture solvents are successfully identified and assessed using the proposed approach. We recommend that these solvents should be given priority in experimental studies to identify new compounds.
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computer aided Molecular Design of solvents for accelerated reaction kinetics
Nature Chemistry, 2013Co-Authors: Heiko Struebing, Amparo Galindo, Zara Ganase, Panagiotis G Karamertzanis, Eirini Siougkrou, Peter R Haycock, Patrick M Piccione, Alan Armstrong, Claire S. AdjimanAbstract:Finding the right solvent can radically transform the rate of a reaction. Here, a systematic computational method for the identification of solvents that accelerate kinetics is described. Starting with a quantum mechanical computation of the reaction rate constant in a set of six solvents, a Computer-Aided approach identifies the best solvent among 1,341, with a 40% increase in reaction rate.