A review of computer-aided design of paints and coatings

Jhamb, Spardha; Enekvist, Markus; Zhang, Xiangping; Dam‐Johansen, Kim; Kontogeorgis, Georgios M.

doi:10.1016/j.coche.2019.12.005

Cited by 22 publications

(12 citation statements)

References 54 publications

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“…Furthermore, we created an efficient and user-friendly MI web tool as a solubility calculator based on our prediction models for users of several fields including industry dealing with solubility-related studies (Figure ). Commonly, Log P is used as a hydrophobicity index for determining the solvent selection, bioaccumulation, and biodegradability, − while HSPs and SP are used for applications based on the solubility of two substances, such as solvent selection/combination, coating techniques, polymer research, and drug development. ,, Notably, although HSPs are convenient indices for establishing the degree of solubility between two components, the components can be used even in mixtures. For example, a study revealed that the solubility between an insecticide’s solvent as a single component and a cockroach’s body surface as a mixture could be evaluated according to the HSPs .…”

Section: Results and Discussionmentioning

confidence: 99%

Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)

Kurotani

Kakiuchi²,

Kikuchi

2021

ACS Omega

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Materials informatics is an emerging field that allows us to predict the properties of materials and has been applied in various research and development fields, such as materials science. In particular, solubility factors such as the Hansen and Hildebrand solubility parameters (HSPs and SP, respectively) and Log P are important values for understanding the physical properties of various substances. In this study, we succeeded at establishing a solubility prediction tool using a unique machine learning method called the in-phase deep neural network (ip-DNN), which starts exclusively from the analytical input data (e.g., NMR information, refractive index, and density) to predict solubility by predicting intermediate elements, such as molecular components and molecular descriptors, in the multiple-step method. For improving the level of accuracy of the prediction, intermediate regression models were employed when performing in-phase machine learning. In addition, we developed a website dedicated to the established solubility prediction method, which is freely available at “”.

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Section: Results and Discussionmentioning

confidence: 99%

Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)

Kurotani

Kakiuchi²,

Kikuchi

2021

ACS Omega

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“…Traditionally, and to a large extent also today, the compatibility between different solvents and binders has been handled through general classifications, and knowledge of which types of solvent works with different types of binders [6]. A thermodynamic approach using liquid-liquid-equilibria (LLE) for multi-solvent multi-polymer solutions is not realistic for complex coating systems, nor is it practical for fast qualitative estimations from a computer-aided design view [32]. An alternative thermodynamic approach is to use solubility parameters, δ, which can be regarded as a more systematic approach to the "like dissolves like" concept often referenced for formulating solvent mixtures.…”

Section: Solubility Parametersmentioning

confidence: 99%

“…While there is a significant effort to minimalize the use of solvents in modern paint formulations due to environmental considerations and regulatory requirements [1], paint formulations may still use substantial amounts of solvents, in some cases use up to 50% by volume [32]. Systematic solvent selection therefore allows for a reduction of the overall cost of the product without necessarily changing other components.…”

Section: Costmentioning

confidence: 99%

Computer-aided design and solvent selection for organic paint and coating formulations

Enekvist

Zhang

Dam‐Johansen

et al. 2022

Progress in Organic Coatings

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A methodology for product design of organic coatings is developed to help a formulator screen, modify, or design a paint formulation according to the desired functionality. The computer-aided product design (CAPD) method aims to improve and expand the current state of computer-aided coating formulation through a combination of databases and models capable of estimating various physicochemical properties. Additional paint-specific component interactions are considered, and new properties and estimation methods are included that better suit modern environmental, health, and safety-related considerations in the product design of paints. This work aims to provide a clear description of all property models, estimation methods, tools, and software used throughout the systematic framework. The solvent selection methodology is tested for several commercial paint formulations, using the original solvent mixtures to verify the results, while providing formulation alternatives to guide further experimental testing. For investigated case studies, the methodology provides both viable formulations and solvent selection alternatives. The suggested options can be used to retain key functionalities while lowering cost, substituting unwanted ingredients, or improving factors including health and safety, as indicated by lethal concentration, flash points, and biodegradability.Abbreviations: BOD 5 /COD, 5-day biological oxygen demand/chemical oxygen demand; C, cost; CAMD, computer-aided molecular design; CAPD, computer-aided product design; CI, connectivity index; EH&S, Environment, Health and Safety; GC, group contribution; GC + , group contribution and connectivity index; ∆G mix , Gibb's energy of mixing; HAP, hazardous air pollutant; HSP, Hansen solubility parameter; LC 50 , 50% lethal concentration; MG, Marrero and Gani; MixD, mixture design algorithm; R a , Hansen solubility parameter distance; R o , radius of interaction; RED, relative energy distance; t 90 , time to 90% evaporation; T f , flash temperature; TPD, tangent plane distance; VOC, volatile organic compound; x i , molar fraction; δ D , dispersion partial solubility parameter; δ P , polar partial solubility parameter; δ H , hydrogen-bonding partial solubility parameter; V m , molar volume; v, kinematic viscosity; μ, dynamic viscosity; σ, surface tension.

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“…Surface coating with materials should be consistent with the coated surface to avoid the difficulties, such as failures, after application, or after a long time [ 1 ]. Meanwhile, the resistance to exterior weathering, water chemical resistance, abrasion, heat resistance, time resistance, and ease of application, may happen in a more stable coating [ 1 ]. Cement-based materials act as a hydraulic binder, increasing the binding between fractured particles and allowing them to be used in a variety of industries [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Ameliorating the Mechanical Parameters, Thermal Stability, and Wettability of Acrylic Polymer by Cement Filling for High-Efficiency Waterproofing

et al. 2022

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Acrylic polymer/cement nanocomposites in dark and light colors have been developed for coating floors and swimming pools. This work aims to emphasize the effect of cement filling on the mechanical parameters, thermal stability, and wettability of acrylic polymer. The preparation was carried out using the casting method from acrylic polymer coating solution, which was added to cement nanoparticles (65 nm) with weight concentrations of (0, 1, 2, 4, and 8 wt%) to achieve high-quality specifications and good adhesion. Maximum impact strength and Hardness shore A were observed at cement ratios of 2 wt% and 4 wt%, respectively. Changing the filling ratio has a significant effect on the strain of the nanocomposites. The contact angle was increased as the concentration of additives and cement increased, indicating that the synthesized coating is not hydrophilic and does not allow water permeability through it. The results show that the acrylic polymer/cement with a cement ratio of 8 wt% is the best nanocomposite for high-efficiency waterproofing.

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A review of computer-aided design of paints and coatings

Cited by 22 publications

References 54 publications

Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)

Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)

Computer-aided design and solvent selection for organic paint and coating formulations

Ameliorating the Mechanical Parameters, Thermal Stability, and Wettability of Acrylic Polymer by Cement Filling for High-Efficiency Waterproofing

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