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Dissipative Particle Dynamics
Fundamentals and Applications in Colloid and Interface Science
- 1st Edition - April 1, 2025
- Authors: Alexander V. Neimark, Kolattukudy P. Santo
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 6 6 5 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 6 6 6 - 5
Dissipative particle dynamics (DPD): Fundamentals and Applications in Colloid and Interface Science is one of the most efficient mesoscale simulation methodologies for studying… Read more
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Request a sales quoteDissipative particle dynamics (DPD): Fundamentals and Applications in Colloid and Interface Science is one of the most efficient mesoscale simulation methodologies for studying soft matter systems. Applied to nanostructured materials of supramolecular architecture, DPD bridges the gap between the microscopic atomistic and macroscopic continuum length and time scales. The book comprehensively presents the fundamentals of DPD theoretical formulations and computational strategies and provides a practical guidance for applications of DPD models to various colloidal and interfacial phenomena involving phase separations, self-assembly and transport in complex fluids, polymeric, surfactant, nanoparticle, and biological systems. In addition, the book contains instructive advice on efficient implementation of the DPD models in open-source computational packages.
Since introduction of the principles of DPD methodology thirty years ago, multiple efforts have been performed to improve the computational basis of DPD and to devise advanced versions and modifications of the original DPD framework. The progress in parametrization techniques that can reproduce engineering properties of experimental systems attracted a lot of interest from the industrial community longing to use DPD models to characterize, help design and optimize the practical products. While there are numerous research papers, there is no book dedicated to the dissipative particle dynamics that provides a critical review of various DPD formulations, serves as a comprehensive reference source of multifaceted interdisciplinary applications, and provides a practical guidance for efficient computational implementation.
- Comprehensive discussion on the foundations and latest advances in the DPD methodology, showcasing multiple interdisciplinary applications with appropriate examples, whilst providing a practical guidance to the choice and computational implementation of DPD models
- Balanced synergistic presentation of the theoretical foundations, modern computational methodologies, and practical applications of dissipative particle dynamics suitable for interdisciplinary readership, including academics, industrial researchers, and students
- Critical analysis of various DPD formulations with pragmatic recommendations how to choose, parameterize, and implement DPD models for computational simulations of specific systems
- Demonstration of the multidisciplinary nature of DPD models on diverse practically relevant examples in materials, chemical, and biological science, and engineering
The book is written for a broad and interdisciplinary audience of researchers and advanced level students interested in theoretical and computational modelling of soft matter systems in engineering, physics, chemistry, biology, and medicine Dissipative particle dynamics (DPD) has become a computational tool of choice for studies of the mechanisms of phase separations, self-assembly, micellization, and structural/transport properties of soft matter.
PART-I MESOSCALE LANDSCAPE OF COMPLEX MATERIALS
1: Mesoscale Materials Landscape
1. Bridging molecular and continuum scales
2. Mesoscale colloids and interfaces
2: Multiscale simulations
2.1 Ab-initio methods
2.2 Atomistic approaches and force fields
2.3 Mesoscale models and coarse-grained simulations
2.3.1 Dissipative particle dynamics (DPD)
2.3.2 Coarse-grained molecular dynamics
2.3.3 Brownian dynamics
2.3.4 Other models
2.4 Macroscale continuum models
2.5 Advantages of DPD
PART II. DPD FUNDAMENTALS
3: Principles of DPD
3.1 Hoogerbrugge-Koleman Formulation
3.2 Groot -Warren Formalism
3.3 Numerical Implementation
3.4 Thermostats
3.5 Boundary conditions
4: Advanced DPD formulations
4.1 Smoothed dissipative particle dynamics (SDPD)
4.2. Fluid particle model (FPM)
4.3 Many-body DPD
4.4. DPD with energy conservation
4.5 Reactive DPD
4.6. Bottom-up DPD
4.7 Other DPD Approaches
5. Phase boundaries in DPD systems
5.1 Liquid-liquid interfaces
5.2 Liquid-gas interfaces
5.3 Fluid-solid interfaces
6: Parametrization of DPD models
6. 1 Groot-Warren Approach
6.1.1 Equation of State
6.1.2 Flory-Huggins parameterization
6.2 Parametrizing bonds and angles in chain molecules
6.3 Matching solubilities
6.4 Matching infinite dilution activity coefficients
6.5 Extensions of Groot-Warren Approach
6.6 DPD models with variable bead sizes
6.7 Using ab-initio methods
6.8 Matching DPD and atomistic simulations
6.9. Parametrizing electrostatic interactions
6.9.1 Smoothed charge approximation
6.9.2 Ewald summation
6.9.3 Gaussian and Bessel charge densities
PART III. DPD APPLICATIONS
7: Multiphase Systems
7.1 Immiscible liquids
7.1.1 Oil-water systems
7.2 Interfacial Tension
7.2.1 Planar Interfaces
7.2.2 Curved Interfaces
7.3 Liquid-liquid phase separation
7.4 Gas-liquid interfaces
7.4.1 DPD Gas Model
7.4.2 MDPD Simulations
7.5 Solid-liquid Interfaces
8. Surfactant Solutions
8.1 Industrial surfactants
8.1.1 Surfactant types
8.1.2 Parameterization
8.2 Surfactant self-assembly and mesophases
8.2.1 Modelling self-assembly
8.2.2 Critical micelle concentration
8.2.3 Salt effects
8.2.4 Surfactant mixtures
8.3 Surfactant monolayers at Interfaces
8.3.1 Surfactant adsorption at air-water interfaces
8.3.2 Langmuir model
8.3.3 DPD simulations of surfactant adsorption at air-water interface
8.3.4 MDPD simulations
8.4 Emulsions
8.4.1 Types of emulsions
8.4.2 Enhanced oil recovery
8.4.3 Surfactants as emulsifiers
8.4.4 DPD simulations of surfactant-oil-water systems
9. Polymeric Systems
9. 1 Dilute polymeric solutions
9.1.1 Configurational properties
9.1.2 Effects of solvent quality
9.1.3 Flow and rheology of polymeric solutions
9.1.4 Rodlike polymers
9.2 Concentrated polymeric solutions
9.3 Polymer melts
9.4 Branched and star polymers
9.5 Reactive polymer networks
9.6 Entanglement and crosslinking effects
10. Block copolymers
10.1 Modelling copolymer self-assembly
10.2 Diblock copolymers and mesophases
10.3 Multi-block copolymers
10.4 Block copolymer films and vesicles
10.5 Confinement effects on block copolymer self-assembly
11. Polymer-grafted surfaces
11.1 Smart surfaces
11.2.Polymer mushrooms
11.3 Polymer brushes (PBs) at different solvent quality
11.4 Smart channels
11.4.1 Control of permeability of PB-grafted channels
11.4.2 Solvent flow in PB-grafted channels
11.5 Interaction between PBs
11.5.1 Forces between PBs
11.5.2 PBs under shear and lubrication
11.6 Spherical PBs
11.7 Mixed PBs
12. Polyelectrolyte solutions and polymer-metal complexes
12.1 Solutions of polyions
12.2 Metal-polymer complexes
12.2.1 DPD model for complexation
12.2.2 Rheology of polyelectrolyte solutions
12.3 Polyelectrolytes on surfaces
12.4 Proton and ionic conductivity of polyelectrolyte solutions
12.5 Ionic liquids
13. Polyelectrolyte membranes (PEMs)
13.1 PEMs and their applications
13.1.1 Fuel cells
13.1.2 Gas and liquid separation
13.1.3 Metal -substituted PEMs (MPEMs)
13.1.4 Protective membranes
13.2 Cation Exchange Membranes
13.2.1 Nafion membrane as a case study
13.2.2 Self-assembly and nano-segregated morphology
13.2.3 Water and ion transport
13.2.4 Diffusion of chemicals and permeability
13.3 Anion exchange membranes
14. Nanoparticle (NP) systems
14.1 DPD models of functional NPs
14.2 Self-assembly of NPs in solutions
14.3 Nanocomposites
14.4 Nanoparticle flow
14.5 Nanoparticle interactions with polymer-grafted substrates
14.5.1 Ghost Tweezers Method
14.5.2 Effects of solvent quality
14.5.3 Critical conditions of NP adhesion
14.5.4 Interaction NP chromatography
15. Lipid membranes
15. 1 DPD models of lipids and cholesterol
15. 2. Modelling lipid bilayers and monolayers
15. 3 Vesicles
15. 4. Interactions of NPs with lipid membranes: adhesion, engulfment, and translocation
16: Colloidal Systems
16.1 Asphaltene aggregation
16.2 Food colloids
16.3 Polysaccharides aggregation and flow
17: Biological systems
17.1 Modelling DNA
17.2 DPD models of proteins
17.2.1 Protein folding and conformational transitions
17.2.2 Protein translocation
17.3 Models of viruses
18. Other systems
PART IV: IMPLEMENTATION OF DPD MODELS FOR HIGH- PERFORMANCE COMPUTATIONS
19 : Software for DPD simulation
19.1 Using LAMMPS
19.1.1 Installation
19.1.2 Example systems
19. 2 Using DL_MESO
19.2.1 Obtaining DL_MESO and installation
19. 2. 2 Example systems
19. 3 Other software
PART V: DPD IN A NUTSHELL
20. Concluding Remarks
20.1 Practical recommendation on DPD modeling
20.2 Limitations of DPD models
20.3 Future perspective of DPD modeling
1: Mesoscale Materials Landscape
1. Bridging molecular and continuum scales
2. Mesoscale colloids and interfaces
2: Multiscale simulations
2.1 Ab-initio methods
2.2 Atomistic approaches and force fields
2.3 Mesoscale models and coarse-grained simulations
2.3.1 Dissipative particle dynamics (DPD)
2.3.2 Coarse-grained molecular dynamics
2.3.3 Brownian dynamics
2.3.4 Other models
2.4 Macroscale continuum models
2.5 Advantages of DPD
PART II. DPD FUNDAMENTALS
3: Principles of DPD
3.1 Hoogerbrugge-Koleman Formulation
3.2 Groot -Warren Formalism
3.3 Numerical Implementation
3.4 Thermostats
3.5 Boundary conditions
4: Advanced DPD formulations
4.1 Smoothed dissipative particle dynamics (SDPD)
4.2. Fluid particle model (FPM)
4.3 Many-body DPD
4.4. DPD with energy conservation
4.5 Reactive DPD
4.6. Bottom-up DPD
4.7 Other DPD Approaches
5. Phase boundaries in DPD systems
5.1 Liquid-liquid interfaces
5.2 Liquid-gas interfaces
5.3 Fluid-solid interfaces
6: Parametrization of DPD models
6. 1 Groot-Warren Approach
6.1.1 Equation of State
6.1.2 Flory-Huggins parameterization
6.2 Parametrizing bonds and angles in chain molecules
6.3 Matching solubilities
6.4 Matching infinite dilution activity coefficients
6.5 Extensions of Groot-Warren Approach
6.6 DPD models with variable bead sizes
6.7 Using ab-initio methods
6.8 Matching DPD and atomistic simulations
6.9. Parametrizing electrostatic interactions
6.9.1 Smoothed charge approximation
6.9.2 Ewald summation
6.9.3 Gaussian and Bessel charge densities
PART III. DPD APPLICATIONS
7: Multiphase Systems
7.1 Immiscible liquids
7.1.1 Oil-water systems
7.2 Interfacial Tension
7.2.1 Planar Interfaces
7.2.2 Curved Interfaces
7.3 Liquid-liquid phase separation
7.4 Gas-liquid interfaces
7.4.1 DPD Gas Model
7.4.2 MDPD Simulations
7.5 Solid-liquid Interfaces
8. Surfactant Solutions
8.1 Industrial surfactants
8.1.1 Surfactant types
8.1.2 Parameterization
8.2 Surfactant self-assembly and mesophases
8.2.1 Modelling self-assembly
8.2.2 Critical micelle concentration
8.2.3 Salt effects
8.2.4 Surfactant mixtures
8.3 Surfactant monolayers at Interfaces
8.3.1 Surfactant adsorption at air-water interfaces
8.3.2 Langmuir model
8.3.3 DPD simulations of surfactant adsorption at air-water interface
8.3.4 MDPD simulations
8.4 Emulsions
8.4.1 Types of emulsions
8.4.2 Enhanced oil recovery
8.4.3 Surfactants as emulsifiers
8.4.4 DPD simulations of surfactant-oil-water systems
9. Polymeric Systems
9. 1 Dilute polymeric solutions
9.1.1 Configurational properties
9.1.2 Effects of solvent quality
9.1.3 Flow and rheology of polymeric solutions
9.1.4 Rodlike polymers
9.2 Concentrated polymeric solutions
9.3 Polymer melts
9.4 Branched and star polymers
9.5 Reactive polymer networks
9.6 Entanglement and crosslinking effects
10. Block copolymers
10.1 Modelling copolymer self-assembly
10.2 Diblock copolymers and mesophases
10.3 Multi-block copolymers
10.4 Block copolymer films and vesicles
10.5 Confinement effects on block copolymer self-assembly
11. Polymer-grafted surfaces
11.1 Smart surfaces
11.2.Polymer mushrooms
11.3 Polymer brushes (PBs) at different solvent quality
11.4 Smart channels
11.4.1 Control of permeability of PB-grafted channels
11.4.2 Solvent flow in PB-grafted channels
11.5 Interaction between PBs
11.5.1 Forces between PBs
11.5.2 PBs under shear and lubrication
11.6 Spherical PBs
11.7 Mixed PBs
12. Polyelectrolyte solutions and polymer-metal complexes
12.1 Solutions of polyions
12.2 Metal-polymer complexes
12.2.1 DPD model for complexation
12.2.2 Rheology of polyelectrolyte solutions
12.3 Polyelectrolytes on surfaces
12.4 Proton and ionic conductivity of polyelectrolyte solutions
12.5 Ionic liquids
13. Polyelectrolyte membranes (PEMs)
13.1 PEMs and their applications
13.1.1 Fuel cells
13.1.2 Gas and liquid separation
13.1.3 Metal -substituted PEMs (MPEMs)
13.1.4 Protective membranes
13.2 Cation Exchange Membranes
13.2.1 Nafion membrane as a case study
13.2.2 Self-assembly and nano-segregated morphology
13.2.3 Water and ion transport
13.2.4 Diffusion of chemicals and permeability
13.3 Anion exchange membranes
14. Nanoparticle (NP) systems
14.1 DPD models of functional NPs
14.2 Self-assembly of NPs in solutions
14.3 Nanocomposites
14.4 Nanoparticle flow
14.5 Nanoparticle interactions with polymer-grafted substrates
14.5.1 Ghost Tweezers Method
14.5.2 Effects of solvent quality
14.5.3 Critical conditions of NP adhesion
14.5.4 Interaction NP chromatography
15. Lipid membranes
15. 1 DPD models of lipids and cholesterol
15. 2. Modelling lipid bilayers and monolayers
15. 3 Vesicles
15. 4. Interactions of NPs with lipid membranes: adhesion, engulfment, and translocation
16: Colloidal Systems
16.1 Asphaltene aggregation
16.2 Food colloids
16.3 Polysaccharides aggregation and flow
17: Biological systems
17.1 Modelling DNA
17.2 DPD models of proteins
17.2.1 Protein folding and conformational transitions
17.2.2 Protein translocation
17.3 Models of viruses
18. Other systems
PART IV: IMPLEMENTATION OF DPD MODELS FOR HIGH- PERFORMANCE COMPUTATIONS
19 : Software for DPD simulation
19.1 Using LAMMPS
19.1.1 Installation
19.1.2 Example systems
19. 2 Using DL_MESO
19.2.1 Obtaining DL_MESO and installation
19. 2. 2 Example systems
19. 3 Other software
PART V: DPD IN A NUTSHELL
20. Concluding Remarks
20.1 Practical recommendation on DPD modeling
20.2 Limitations of DPD models
20.3 Future perspective of DPD modeling
- No. of pages: 256
- Language: English
- Edition: 1
- Published: April 1, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443136658
- eBook ISBN: 9780443136665
AN
Alexander V. Neimark
Alexander V. Neimark is a Distinguished Professor of Chemical and Biochemical Engineering at Rutgers University, USA. He received his Doctor of Science degree at the Moscow State University and worked at the Institute of Physical Chemistry of Russian Academy of Sciences. After receiving the Humboldt fellowship in 1992, he worked at Mainz University (Germany) and then held visiting positions at CNRS (France), and Yale University (USA). Prior to joining Rutgers in 2006, he served as Research Director of the Center for Modeling and Characterization of Nanoporous Materials at the Textile Research Institute (TRI/Princeton) in 1996-2006. He is a recipient of many national and international awards and honored appointments, including Guggenheim Fellow, Blaise Pascal International Chair, Humboldt Fellow, Fellow of American Institute of Chemical Engineers, Fellow of International Adsorption Society, Distinguished Visiting Fellow of the Royal Academy of Engineering, and Leverhulme Professorship. He published 280+ research papers with 31,500+ citations and Hirsh index h=74.
Neimark’s research interests include thermodynamics, statistical mechanics, and molecular modelling of adsorption, transport, and interfacial phenomena in nanoporous and nanostructured materials and self-assembly in surfactant and polymeric soft matter systems. For the last 12 years, his group at Rutgers has been actively involved in the development of novel simulation methods within the dissipative particle dynamics framework that resulted in 3 PhD and 3 MS dissertations and 20 papers, including a comprehensive historical perspective review [K.P. Santo and A.V. Neimark, Dissipative Particle Dynamics Simulations in Colloid and Interface Science, Advances in Colloid and Interface Science, 2021, V.298, 102545, DOI: 10.1016/j.cis.2021.102545].
Affiliations and expertise
Distinguished Professor of Chemical and Biochemical Engineering, Rutgers University, New Jersey, USAKS
Kolattukudy P. Santo
Kolattukudy P. Santo is a research assistant professor at the department of chemical and biochemical engineering, Rutgers University, New Jersey, USA. Dr Santo obtained his Ph.D. in theoretical chemistry at the Indian Institute of Science, Bengaluru, India. After receiving his Ph.D., he worked as a post-doctoral fellow and visiting scientist at the National Institute of Nanotechnology (NRC) and University of Alberta in Edmonton, Canada, and later at the department of chemistry, University of North Carolina at Chapel Hill. Before joining Rutgers University in 2015, he worked as a teaching faculty at the department of physics, Central University of Kerala, Kasaragod, India.
Dr Santo has published 23 articles in international journals on theory and computational studies of diverse fields of soft matter, which include various polymeric systems, metal-polymer complexes, lipid membranes, proteins, and polysaccharides, isurfactant and nanoparticle solutions, and colloids. His main area of expertise is atomistic and coarse-grained molecular dynamics and dissipative particle dynamics simulations. He has published 23 papers in leading research journals.
Affiliations and expertise
Research Assistant Professor of Chemical and Biochemical Engineering, Rutgers University, New Jersey, USA