Predictive Modeling in Polymer Science: Properties and Performance introduces the latest methods for predicting polymer properties and addressing cost-effectiveness and desirable properties for product design and production. The book discusses a range of prediction methods and models, providing a comprehensive overview of polymer behaviors and properties essential for material performance, such as flammability properties, solubility, intrinsic viscosity, glass transition temperatures, and refractive index. Readers will gain a deep understanding of predictive modeling techniques that enable the design of polymers with desired properties, saving time and resources.
This book provides a comprehensive guide for scientists, engineers, and industry professionals interested on predicting and optimizing polymer properties for material design.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Preface - p. xv 7. Chapter 1. Different approaches for predicting the properties of polymers - p. 1 8. Chapter 2. Solubility of polymers - p. 39 9. 2.1 Polymer solubility: Understanding the basics - p. 39 10. 2.2 Different methods for prediction solubility parameters - p. 44 11. 2.3 Solubility of active substances in polymeric carriers and Flory–Huggins theory - p. 61 12. 2.4 Real-world applications of polymer solubility in research and industry - p. 65 13. 2.5 Summary - p. 67 14. Questions - p. 68 15. Problems - p. 69 16. Chapter 3. Intrinsic (limiting) viscosity - p. 75 17. 3.1 Intrinsic viscosity: Understanding the basics - p. 75 18. 3.2 Different methods for predicting intrinsic viscosity - p. 80 19. 3.3 Practical or real-world situations in research and industry regarding intrinsic viscosity - p. 90 20. 3.4 Summary - p. 98 21. Questions - p. 99 22. Problems - p. 100 23. Chapter 4. Polymeric material’s glass transition temperature - p. 107 24. 4.1 Implication of the glass transition temperature in polymeric materials - p. 107 25. 4.2 Various approaches for predicting Tg - p. 108 26. 4.2.1 SGC method for prediction Tg - p. 108 27. 4.2.2 Review of QSPR models and the other approaches for estimating Tg - p. 114 28. 4.2.3 Details of several new QSPR models - p. 117 29. 4.2.3.1 Encoding polymer structures of polyacrylates without relying on three-dimensional descriptors - p. 117 30. 4.2.3.2 Assessment of Tg of acrylic and methacrylic polymers from structure of their monomers - p. 118 31. 4.2.3.3 A cheminformatics-based methodology to explore the Tg of various polymers - p. 126 32. 4.2.3.4 QSPR modeling with 2D molecular descriptors - p. 127 33. 4.2.3.5 Applying the Monte Carlo technique to build up models of the Tg of diverse polymers - p. 129 34. 4.2.3.6 Evaluating machine learning models in polymer informatics: a case study on Tg - p. 130 35. 4.2.3.7 Application of cheminformatics and molecular dynamics simulations - p. 132 36. 4.2.3.8 Tg from the chemical structure of conjugated polymers - p. 133 37. 4.2.3.9 Tg of polymeric materials only from various structural factors in their repeating unit structure - p. 137 38. 4.3 Real-world applications of polymer Tg in research and industry - p. 142 39. 4.4 Summary - p. 146 40. Questions - p. 147 41. Problems - p. 148 42. Chapter 5. Dielectric constant of polymers - p. 155 43. Chapter 6. Polymer refractive index - p. 183 44. Chapter 7. Biopolymers - p. 209 45. Chapter 8. Energetic polymers - p. 249 46. Chapter 9. Toxicity of polymers - p. 297 47. Chapter 10. Flammability of polymers - p. 339 48. Chapter 11. Shelf life of energetic compounds containing polymers - p. 379 49. Index - p. 421
Predictive Modeling in Polymer Science: Properties and Performance introduces the latest methods for predicting polymer properties and addressing cost-effectiveness and desirable properties for product design and production. The book discusses a range of prediction methods and models, providing a comprehensive overview of polymer behaviors and properties essential for material performance, such as flammability properties, solubility, intrinsic viscosity, glass transition temperatures, and refractive index. Readers will gain a deep understanding of predictive modeling techniques that enable the design of polymers with desired properties, saving time and resources.
This book provides a comprehensive guide for scientists, engineers, and industry professionals interested on predicting and optimizing polymer properties for material design.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Preface - p. xv 7. Chapter 1. Different approaches for predicting the properties of polymers - p. 1 8. Chapter 2. Solubility of polymers - p. 39 9. 2.1 Polymer solubility: Understanding the basics - p. 39 10. 2.2 Different methods for prediction solubility parameters - p. 44 11. 2.3 Solubility of active substances in polymeric carriers and Flory–Huggins theory - p. 61 12. 2.4 Real-world applications of polymer solubility in research and industry - p. 65 13. 2.5 Summary - p. 67 14. Questions - p. 68 15. Problems - p. 69 16. Chapter 3. Intrinsic (limiting) viscosity - p. 75 17. 3.1 Intrinsic viscosity: Understanding the basics - p. 75 18. 3.2 Different methods for predicting intrinsic viscosity - p. 80 19. 3.3 Practical or real-world situations in research and industry regarding intrinsic viscosity - p. 90 20. 3.4 Summary - p. 98 21. Questions - p. 99 22. Problems - p. 100 23. Chapter 4. Polymeric material’s glass transition temperature - p. 107 24. 4.1 Implication of the glass transition temperature in polymeric materials - p. 107 25. 4.2 Various approaches for predicting Tg - p. 108 26. 4.2.1 SGC method for prediction Tg - p. 108 27. 4.2.2 Review of QSPR models and the other approaches for estimating Tg - p. 114 28. 4.2.3 Details of several new QSPR models - p. 117 29. 4.2.3.1 Encoding polymer structures of polyacrylates without relying on three-dimensional descriptors - p. 117 30. 4.2.3.2 Assessment of Tg of acrylic and methacrylic polymers from structure of their monomers - p. 118 31. 4.2.3.3 A cheminformatics-based methodology to explore the Tg of various polymers - p. 126 32. 4.2.3.4 QSPR modeling with 2D molecular descriptors - p. 127 33. 4.2.3.5 Applying the Monte Carlo technique to build up models of the Tg of diverse polymers - p. 129 34. 4.2.3.6 Evaluating machine learning models in polymer informatics: a case study on Tg - p. 130 35. 4.2.3.7 Application of cheminformatics and molecular dynamics simulations - p. 132 36. 4.2.3.8 Tg from the chemical structure of conjugated polymers - p. 133 37. 4.2.3.9 Tg of polymeric materials only from various structural factors in their repeating unit structure - p. 137 38. 4.3 Real-world applications of polymer Tg in research and industry - p. 142 39. 4.4 Summary - p. 146 40. Questions - p. 147 41. Problems - p. 148 42. Chapter 5. Dielectric constant of polymers - p. 155 43. Chapter 6. Polymer refractive index - p. 183 44. Chapter 7. Biopolymers - p. 209 45. Chapter 8. Energetic polymers - p. 249 46. Chapter 9. Toxicity of polymers - p. 297 47. Chapter 10. Flammability of polymers - p. 339 48. Chapter 11. Shelf life of energetic compounds containing polymers - p. 379 49. Index - p. 421
