RAS Chemistry & Material ScienceЖурнал физической химии Russian Journal of Physical Chemistry

  • ISSN (Print) 0044-4537
  • ISSN (Online) 3034-5537

Information Entropy of Parallel and Independent Chemical Reactions

PII
10.31857/S0044453723100291-1
DOI
10.31857/S0044453723100291
Publication type
Status
Published
Authors
Volume/ Edition
Volume 97 / Issue number 10
Pages
1393-1397
Abstract
In mathematical chemistry problems, a chemical reaction is represented as a transformation of one molecular ensemble into another, and information entropy and related parameters are often used to quantify changes in the complexity of molecules. The information entropy of a chemical reaction is calculated as the difference between the values corresponding to an ensemble of products and an ensemble of reagents. Previously, we have shown that the information entropy of molecular ensembles depends not only on the information entropy of individual molecules, but also on cooperative entropy—an emergent parameter that arises when molecules are combined into an ensemble. Inclusion of this parameter in calculation determines the peculiarities of calculating the information entropy for interrelated chemical reactions. The article considers systems of independent and parallel chemical reactions and gives an analytical dependence that correlates the information entropy of the total process with the parameters of individual reactions.
Keywords
информационная энтропия кооперативная энтропия молекулярный ансамбль параллельные реакции независимые реакции
Date of publication
12.09.2025
Year of publication
2025
Number of purchasers
0
Views
10

References

  1. 1. Станкевич И.М., Станкевич И.В., Зефиров Н.С. // Успехи химии. 1988. Т. 57. С. 191–208.
  2. 2. Sabirov D.S., Shepelevich I.S. // Entropy. 2021. V. 23. P. 1240.
  3. 3. Barigye S.J., Marrero-Ponce Y., Pérez-Giménez F., Bonchev D. // Mol. Divers. 2014. V. 18. P. 673.
  4. 4. Dehmer M., Mowshowitz A. // Inf. Sci. 2011. V. 181. P. 57.
  5. 5. Basak S., Harriss D., Magnuson V. // J. Pharm. Sci. 1984. V. 73. P. 429.
  6. 6. Basak S.C. // Big Data Analytics in Chemoinformatics and Bioinformatics with Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology. Eds: Basak S.C., Vračko M. Elsevier, 2023. P. 3–35.
  7. 7. Bonchev D. // Bulgar. Chem. Commun. 1995. V. 28. P. 567.
  8. 8. Sabirov D.S. // Comput. Theor. Chem. 2016. V. 1097. P. 83.
  9. 9. Sabirov D.S., Shepelevich I.S. // Comput. Theor. Chem. 2015. V. 1073. P. 61.
  10. 10. Sabirov D.S., Ori O., László I. // Fullerene Nanotube Carbon Nanostruct. 2018. V. 26. P. 100.
  11. 11. Augustine T., Roy S., Sahaya V.J. et al. // Mol. Phys. 2023. V. 121. P. e2179858.
  12. 12. Krivovichev S. // Mineral. Mag. 2013. V. 77. P. 275.
  13. 13. Aksenov S.M., Yamnova N.A., Borovikova E.Y. et al. // J. Struct. Chem. 2020. V. 61. P. 1760.
  14. 14. Bindi L., Nespolo M., Krivovichev S.V. et al. // Rep. Prog. Phys. 2020. V. 83. P. 106501.
  15. 15. Krivovichev S.V., Krivovichev V.G., Hazen R.M. // Eur. J. Miner. 2018. V. 30. P. 231.
  16. 16. Krivovichev S.V., Hawthorne F., Williams P.A. // Struct. Chem. 2016. V. 28. P. 153.
  17. 17. Krivovichev S.V., Krivovichev V.G., Hazen R.M. et al. // Mineral. Mag. 2022. V. 86. P. 183.
  18. 18. Banaru D.A., Hornfeck W., Aksenov S.M., Banaru A.M. // CrystEngComm. 2023. https://doi.org/10.1039/D2CE01542K
  19. 19. Banaru A., Aksenov S., Krivovichev S. // Symmetry. 2021. V. 13. P. 1399.
  20. 20. Jacob K., Clement J., Arockiaraj M. et al. // J. Mol. Struct. 2023. V. 1277. P. 134786.
  21. 21. Plášil J. // Eur. J. Minerol. 2018. V. 30. P. 237.
  22. 22. Hanif M.F., Mahmood H. // Polycyclic Aromatic Compounds. 2022. https://doi.org/10.1080/10406638.2022.2149575
  23. 23. Sabirov D.S., Ori O., Tukhbatullina A.A., Shepelevich I.S. // Symmetry. 2021. V. 13. P. 1899.
  24. 24. Augustine T., Santiago R. // Symmetry. 2023. V. 15. P. 635.
  25. 25. Rahul M.P., Clement J. // Eur. Phys. J. Plus. 2022. V. 137. P. 1365.
  26. 26. Rahul M., Clement J., Singh J.J. et al. // J. Mol. Struct. 2022. V. 1260. P. 132797.
  27. 27. Sabirov D., Tukhbatullina A., Shepelevich I. // Liquids. 2021. V. 1. P. 25.
  28. 28. Baby A., Julietraja K., Xavier D.A. // Polycyclic Aromatic Compounds. 2023. https://doi.org/10.1080/10406638.2023.2179641
  29. 29. Castellano G., Lara A., Torrens F. // Phytochemistry. 2014. V. 97. P. 62.
  30. 30. Castellano G., Torrens F. // Phytochemistry. 2015. V. 116. P. 305.
  31. 31. Sabirov D., Koledina K. // EPJ Web. 2020. V. 244. P. 01016.
  32. 32. Karreman G. // Bull. Math. Biol. 1955. V. 17. P. 279.
  33. 33. Кобозев Н.И. // Журн. физ. химии. 1966. Т. 40. С. 281.
  34. 34. Кобозев Н.И., Страхов Б.В., Рубашов А.М. // Там же. 1971. Т. 45. С. 86.
  35. 35. Кобозев Н.И., Страхов Б.В., Рубашов А.М. // Там же. 1971. Т. 45. С. 375.
  36. 36. Sabirov D.S., Osawa E. // J. Chem. Inf. Model. 2015. V. 55. P. 1576.
  37. 37. Sabirov D.S., Sokolov V.I., Terentyev O.A. // RSC Adv. 2016. V. 6. P. 72230.
  38. 38. Sabirov D.S., Tukhbatullina A.A., Shepelevich I.S. // Symmetry. 2022. V. 14. P. 1800.
  39. 39. Feng B., Zhuang X. // Acta Chimica Sinica. 2020. V. 78. P. 833.
  40. 40. Champion Y., Thurieau N. // Sci. Rep. 2020. V. 10. P. 10801.
  41. 41. Бальмаков М.Д. // Успехи физ. наук. 1999. Т. 169. С. 1273.
  42. 42. Кадомцев Б.Б. // Там же. 1994. Т. 164. С. 449.
  43. 43. Sabirov D.S. // Comput. Theor. Chem. 2018. V. 1123. P. 169.
  44. 44. Sabirov D.S. // Ibid. 2020. V. 1187. P. 112933.
  45. 45. Sabirov D.S., Tukhbatullina A.A., Shepelevich I.S. // J. Mol. Graph. Model. 2022. V. 110. P. 108052.
  46. 46. Бенсон С. Термохимическая кинетика. М.: Мир, 1971. 308 с.
  47. 47. Sabirov D.Sh. // Understanding Information Entropy. Ed.: Kumar V. Nova Publishers, 2023.
  48. 48. Nielsen M.A., Chuang I.L. Quantum Computation and Quantum Information. Cambridge University Press, 2001. P. 822.
  49. 49. Sharma A., Thakur P., Kumar G., Kumar A. // Modern Phys. Lett. A. 2021. V. 36. P. 2150065.
  50. 50. Matsubara S. // Chem. Lett. 2021. V. 50. P. 475.
  51. 51. Grzybowski A.B., Badowski T., Molga K., Szymkuć S. // WIREs Comput. Mol. Sci. 2023. V. 13. P. e1630.
  52. 52. Тухбатуллина А.А., Шепелевич И.С., Сабиров Д.Ш. // Вестн. Башкирск. ун-та. 2022. Т. 27. № 2. С. 349.
  53. 53. Ugi I., Gillespie P. // Angew. Chem. 1971. V. 10. P. 914.
  54. 54. Hunter K.C., East A.L.L. // J. Phys. Chem. A. 2002. V. 106. P. 1346.
  55. 55. Bertz S.H. // New J. Chem. 2003. V. 27. P. 860.
  56. 56. Matsubara S. // Chem. Lett. 2021. V. 50. P. 475.
  57. 57. Жданов Ю.А. Энтропия информации в органической химии. Ростов н/Д: изд-во Ростовского ун-та, 1979. 56 с.
  58. 58. Коледина К.Ф. // Математическое моделирование. 2022. Т. 34. С. 97.
  59. 59. Sabirov D.S., Shepelevich I.S., Tumanskii B.L. // Comput. Theor. Chem. 2018. V. 1138. P. 84.
QR
Translate

Индексирование

Scopus

Scopus

Scopus

Crossref

Scopus

Higher Attestation Commission

At the Ministry of Education and Science of the Russian Federation

Scopus

Scientific Electronic Library