- Код статьи
 - 10.31857/S0424857023010188-1
 - DOI
 - 10.31857/S0424857023010188
 - Тип публикации
 - Статус публикации
 - Опубликовано
 - Авторы
 - Том/ Выпуск
 - Том 59 / Номер выпуска 1
 - Страницы
 - 4-15
 - Аннотация
 - На основании анализа публикационной активности с использованием единой библиографической и реферативной базы данных рецензируемой научной литературы Scopus установлены тенденции развития основных разделов ионики твердого тела. Указаны перспективные области исследований, связанные с in situ и operando экспериментами, искусственным интеллектом (машинным обучением) и конструированием новых устройств с использованием суперионных материалов.
 - Ключевые слова
 - ионика твердого тела твердые электролиты суперионные проводники Scopus
 - Дата публикации
 - 01.01.2023
 - Год выхода
 - 2023
 - Всего подписок
 - 0
 - Всего просмотров
 - 30
 
Библиография
- 1. Иванов-Шиц, А.К., Мурин, И.В. Ионика твердого тела. Т. 1. СПб.: Изд-во СПбГУ, 2000. 616 с. [Ivanov-Schitz, A.K. and Murin, I.V., Solid State Ionics, V.1 (in Russian), S.-Petersburg: S.-Petersburg Univ. Press, 2000. 616 p.]
 - 2. Takahashi, T., Yamamoto, O., Tsukuba, K., and Baba, A., Electrical Conductivity of Solid Electrolyte (Part VI). Electrical Conductivity in a Ag2S–HgI2 System, Denki kagaku, 1967, vol. 35, p. 32.
 - 3. Knauth, Ph. and Tuller, H.L., Solid-State Ionics: Roots, Status, and Future Prospects, J. Amer. Ceram. Soc., 2002, vol. 85, p. 1654.
 - 4. Kim, S., Yamaguchi, S., and Elliott, J.A., Solid-State Ionics in the 21st Century: Current Status and Future Prospects, MRS Bull., 2009, vol. 34, p. 900.
 - 5. Funke, K., Solid State Ionics: from Michael Faraday to green energy—the European dimension, Sci. and Technol. Adv. Mater., 2013, vol.14, p. 043502. https://doi.org/10.1088/1468-6996/14/4/043502
 - 6. Terabe, K., Tsuchiya, T., Tsuruoka,T., Kim, S.-J., and Aono, M., Current Progress of Solid State Ionics on Information and Communication Device Technology, Ext. Abs. the 17th Int. Workshop on Junction Technology, 2017, p. S4-1.
 - 7. Yamamoto, O., Solid state ionics: a Japan perspective, Sci. and Technol. Adv. Mater., 2017, vol. 18, p. 504. https://doi.org/10.1080/14686996.2017.1328955
 - 8. Иванов-Шиц, А.К., Мурин, И.В. Ионика твердого тела. Т. 2. СПб.: Изд-во СПбГУ, 2010. 1000 с. [Ivanov-Schitz, A.K. and Murin, I.V., Solid State Ionics, V. 2 (in Russian), S.-Petersburg: S.-Petersburg Univ. Press, 2010. 1000 p.]
 - 9. https://www.scopus.com/.
 - 10. https://www.elsevier.com/solutions/scopus/how-scopus-works.
 - 11. Син, В., Ковалев, М. Китай строит экономику знаний. Вестник ассоциации белорусских банков. 2015. № 7. С. 3.
 - 12. Беляков, Г.П., Беляков, С.А., Шпак, А.С. Опыт КНР по реформированию системы стратегического планирования и управления научно-технологическим развитием. Экономические отношения. 2019. Т. 9. С. 1575. DOI
 - 13. Рейтинг ведущих стран мира по затратам на науку. Институт статистических исследований и экономики знаний, Дата выпуска 24.07.2018, https://issek.hse.ru/mirror/pubs/share/221869863.
 - 14. Наука России в 10 цифрах. Институт статистических исследований и экономики знаний, Новости, Февраль 2021, https://issek.hse.ru/news/442044357.html.
 - 15. Уваров, Н.Ф. Композиционные твердые электролиты, Новосибирск, Изд. СО РАН, 2008. 258 с.
 - 16. Сомов, С.И. Частное сообщение.
 - 17. Stangl, A., Muñoz-Rojas, D., and Burriel, M., In situ and operando characterisation techniques for solid oxide electrochemical cells: recent advances, J. Phys. Energy, 2021, vol. 3, p. 012001.
 - 18. Li, X., Wang, H.-Y., Yang, H., Cai, W., Liu, S., and Liu, B., In situ/operando characterization techniques to probe the electrochemical reactions for energy conversion, Small Methods, 2018, vol. 2, p. 1700395.
 - 19. Meyer, Q., Zeng, Y., and Zhao, C., In situ and operando characterization of proton exchange membrane fuel cells, Adv. Mater., 2019, vol. 31, p. 1.
 - 20. Abakumov, A.M., Fedotov, S.S., Antipov, E.V., and Tarascon, J.-M., Solid state chemistry for developing better metal-ion batteries, Nature Commun., 2020, vol. 11, p. 4976. https://doi.org/10.1038/s41467-020-18736-7
 - 21. Yamada, T., Morita, K., Kume, K., Yoshikawa, H., and Awaga, K., The solid-state electrochemical reduction process of magnetite in Li batteries: in situ magnetic measurements toward electrochemical magnets, J. Mater. Chem. C, 2014, vol. 2, p. 5183.
 - 22. Agarkov, D.A., Burmistrov, I.N., Eliseeva, G.M., Ionov, I.V., Rabotkin, S.V., Semenov, V.A., Solovyev, A.A., Tartakovskii, I.I., and Bredikhin, S.I., Comparison of in situ Raman Studies of SOFC with Thick Single-crystal and Thin-film Magnetron Sputtered Membranes, Solid State Ionics, 2020, vol. 344, p. 115091. https://doi.org/10.1016/j.ssi.2019.115091
 - 23. Gershinsky, G., Bar, E., Monconduit, L., & Zitoun, D., Operando electron magnetic measurements of Li-ion batteries, Energy Environ. Sci., 2014, vol. 7, p. 2012.
 - 24. Drozhzhin, O.A., Tereshchenko, I.V., Emerich, H., Antipov, E.V., Abakumov, A.M., and Chernyshov, D., An electrochemical cell with sapphire windows for operando synchrotron X-ray powder diffraction and spectroscopy studies of high-power and high-voltage electrodes for metal-ion batteries, J. Synchrotron Rad., 2018, vol. 25, p. 468. https://doi.org/10.1107/S1600577517017489
 - 25. Guo, H., Wang, Q., Stuke, A., Urban, A., and Artrith, N., Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning, Front. Energy Res., 2021. https://doi.org/10.3389/fenrg.2021.695902
 - 26. Terabe, K., Tsuchiya, T., and Tsuruoka, T., Solid state ionics for the development of artificial intelligence components, Japan J. Appl. Phys., 2022, vol. 61, p. SM0803. https://doi.org/10.35848/1347-4065/ac64e5
 - 27. Liu, Y., Guo, B., Zou, X., Li, Y., and Shi, S., Machine Learning Assisted Materials Design and Discovery for Rechargeable Batteries, Energy Storage Mater., 2020, https://doi.org/10.1016/j.ensm.2020.06.033
 - 28. Lv, C., Zhou, X., Zhong, L., Yan, C., Srinivasan, M., Seh, Z.W., Liu, C., Pan, H., Li, S., Wen, Y., and Yan, Q., Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium-Ion Batteries, Adv. Mater., 2021, N.2101474. https://doi.org/10.1002/adma.202101474
 - 29. Gao, T. and Lu, W., Machine learning toward advanced energy storage devices and systems, iScience, 2021, vol. 24, p. 101936. https://doi.org/10.1016/j.isci.2020.101936
 - 30. Ling, C., A review of the recent progress in battery informatics, npj Computational Materials, 2022, vol. 8, p. 33. https://doi.org/10.1038
 - 31. Miwa, K. and Asahi, R., Molecular dynamics simulations of lithium superionic conductor Li10GeP2S12 using a machine learning potential, Solid State Ionics, 2021, vol. 361, p. 115567. https://doi.org/10.1016/j.ssi.2021.115567
 - 32. Kahle, L., Marcolongo, A., and Marzari, N., High-throughput computational screening for solid-state Li-ion conductors, Energy & Environmental Science, 2020, vol. 13. https://doi.org/10.1039/C9EE02457C
 - 33. Chen, Y.-T., Duquesnoy, M., Tan, D.H.S., Doux, J.-M., Yang, H., Deysher, G., Ridley, P., Franco, A.A., Meng, Y.S., and Chen, Z., Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning, ACS Energy Lett., 2021, vol. 6, p. 1639. https://doi.org/10.1021/acsenergylett.1c00332
 - 34. Sendek, A.D., Cubuk, E.D., Antoniuk, E.R., Cheon, G., Cui, Y., and Reed, E.J., Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials, Chem. Mater., 2019, vol. 31, p. 342. https://doi.org/10.1021/acs.chemmater.8b03272
 - 35. Zhao, Y., Schiffmann, N., Koeppe, A., Brandt, N., Bucharsky, E.C., Schell, K.G., Selzer, M., and Nestler, B., Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate, Front. Mater., 2022, vol. 9, p. 821817. https://doi.org/10.3389/fmats.2022.821817
 - 36. Watanabe, S., Li, W., Jeong, W., Lee, D., Shimizu, K., Mimanitani, E., Ando, Y., and Han, S., High-dimensional neural network atomic potentials for examining energy materials: some recent simulations, J. Phys. Energy, 2021, vol. 3, p. 012003.
 - 37. Zhang, X., Tang, B., and Zhou, Z., Unsupervised machine learning accelerates solid electrolyte discovery, Green Energy & Environment, 2019. https://doi.org/10.1016/j.gee.2019.12.003
 - 38. Zhang, Y., He, X., Chen, Z., Bai, Q., Nolan, A.M., Roberts, C.A., Banerjee, D., Matsunaga, T., Mo, Y., and Ling, C., Unsupervised discovery of solid-state lithium ion conductors, Nature Commun., 2019, vol. 10, Article number: 5260.
 - 39. Louis, S.-Y., Siriwardane, E.M.D., Joshi, R.P., Omee, S.S., Kumar N., and Hu, J., Accurate Prediction of Voltage of Battery Electrode Materials Using Attention-Based Graph Neural Networks, ACS Appl. Mater. Interfaces, 2022, vol. 14, p. 26587. https://doi.org/10.1021/acsami.2c00029
 - 40. Bhowmik, A., Castelli, I.E., Garcia-Lastra, J.M., Jørgensen, P.B., Winther, O., and Vegge, T., A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning, Energy Storage Mater., 2019, vol. 21, p. 446. https://doi.org/10.1016/j.ensm.2019.06.011
 - 41. Lu, J., Xiong, R., Tian, J., Wang, C., Hsu, C.-W., Tsou, N.-T., Sun, F., and Li, J., Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning, Energy Storage Mater., 2022, vol. 50, p. 139. https://doi.org/10.1016/j.ensm.2022.05.007
 - 42. Shao, Z.-Y., Huang, H.-M., and Guo, X., Optimizing linearity of weight updating in TaOx-based memristors by depression pulse scheme for neuromorphic computing, Solid State Ionics, 2021, vol. 370, p. 115746.
 - 43. Manikandan, J., Tsuchiya, T., Takayanagi, M., Kawamura, K., Higuchi, T., Terabe, K., and Jayavel, R., Substrate effect on the neuromorphic function of nanoionics-based transistors fabricated using WO3 thin film, Solid State Ionics, 2021, vol. 364, p. 115638.