REFERENCES
1. Chen, P.; Xiao, Y.; Li, S.; et al. The promise and challenges of inverted perovskite solar cells. Chem. Rev. 2024, 124, 10623-700.
2. Pryor, S. C.; Barthelmie, R. J.; Bukovsky, M. S.; Leung, L. R.; Sakaguchi, K. Climate change impacts on wind power generation. Nat. Rev. Earth. Environ. 2020, 1, 627-43.
3. Wang, N.; Tang, C.; Du, L.; et al. Advanced cathode materials for protonic ceramic fuel cells: recent progress and future perspectives. Adv. Energy. Mater. 2022, 12, 2201882.
4. Hu, A.; Yang, C.; Li, Y.; et al. High-entropy driven self-assembled dual-phase composite air electrodes with enhanced performance and stability for reversible protonic ceramic cells. Adv. Energy. Mater. 2025, 2405466.
5. Oni, A. M.; Mohsin, A. S.; Rahman, M. M.; Hossain, Bhuian., M. B. A comprehensive evaluation of solar cell technologies, associated loss mechanisms, and efficiency enhancement strategies for photovoltaic cells. Energy. Rep. 2024, 11, 3345-66.
6. Dai, K.; Bergot, A.; Liang, C.; Xiang, W.; Huang, Z. Environmental issues associated with wind energy - a review. Renew. Energy. 2015, 75, 911-21.
7. Sayed, E. T.; Wilberforce, T.; Elsaid, K.; et al. A critical review on environmental impacts of renewable energy systems and mitigation strategies: wind, hydro, biomass and geothermal. Sci. Total. Environ. 2021, 766, 144505.
8. Kuriqi, A.; Pinheiro, A. N.; Sordo-ward, A.; Garrote, L. Water-energy-ecosystem nexus: Balancing competing interests at a run-of-river hydropower plant coupling a hydrologic–ecohydraulic approach. Energy. Convers. Manag. 2020, 223, 113267.
9. Soltani, M.; Moradi, Kashkooli., F.; Souri, M.; et al. Environmental, economic, and social impacts of geothermal energy systems. Renew. Sustain. Energy. Rev. 2021, 140, 110750.
10. Mekhilef, S.; Saidur, R.; Safari, A. Comparative study of different fuel cell technologies. Renew. Sustain. Energy. Rev. 2012, 16, 981-9.
11. Tellez-Cruz, M. M.; Escorihuela, J.; Solorza-Feria, O.; Compañ, V. Proton exchange membrane fuel cells (PEMFCs): advances and challenges. Polymers 2021, 13, 3064.
12. Das, G.; Choi, J. H.; Nguyen, P. K. T.; Kim, D. J.; Yoon, Y. S. Anion exchange membranes for fuel cell application: a review. Polymers 2022, 14, 1197.
13. Petrovic, S.; Hossain, E. Development of a novel technological readiness assessment tool for fuel cell technology. IEEE. Access. 2020, 8, 132237-52.
14. Yu, G.; Dai, C.; Liu, N.; Xu, R.; Wang, N.; Chen, B. Hydrocarbon extraction with ionic liquids. Chem. Rev. 2024, 124, 3331-91.
15. Alaswad, A.; Omran, A.; Sodre, J. R.; et al. Technical and commercial challenges of proton-exchange membrane (PEM) fuel cells. Energies 2021, 14, 144.
16. Xiao, F.; Wang, Y. C.; Wu, Z. P.; et al. Recent advances in electrocatalysts for proton exchange membrane fuel cells and alkaline membrane fuel cells. Adv. Mater. 2021, 33, 2006292.
17. Mclean, G. An assessment of alkaline fuel cell technology. Int. J. Hydrogen. Energy. 2002, 27, 507-26.
18. Kirubakaran, A.; Jain, S.; Nema, R. A review on fuel cell technologies and power electronic interface. Renew. Sustain. Energy. Rev. 2009, 13, 2430-40.
19. Tang, C.; Yao, Y.; Wang, N.; et al. Green hydrogen production by intermediate-temperature protonic solid oxide electrolysis cells: advances, challenges, and perspectives. InfoMat 2024, 6, e12515.
20. Weng, B.; Song, Z.; Zhu, R.; et al. Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts. Nat. Commun. 2020, 11, 3513.
21. Li, Z.; Mao, X.; Feng, D.; et al. Prediction of perovskite oxygen vacancies for oxygen electrocatalysis at different temperatures. Nat. Commun. 2024, 15, 9318.
22. Divilov, S.; Eckert, H.; Hicks, D.; et al. Disordered enthalpy-entropy descriptor for high-entropy ceramics discovery. Nature 2024, 625, 66-73.
23. Hyodo, J.; Tsujikawa, K.; Shiga, M.; Okuyama, Y.; Yamazaki, Y. Accelerated discovery of proton-conducting perovskite oxide by capturing physicochemical fundamentals of hydration. ACS. Energy. Lett. 2021, 6, 2985-92.
24. Priya, P.; Aluru, N. R. Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning. npj. Comput. Mater. 2021, 7, 90.
25. Cao, J.; Ji, Y.; Shao, Z. Perovskites for protonic ceramic fuel cells: a review. Energy. Environ. Sci. 2022, 15, 2200-32.
26. Duan, C.; Huang, J.; Sullivan, N.; O’hayre, R. Proton-conducting oxides for energy conversion and storage. Appl. Phys. Rev. 2020, 7, 011314.
27. Liu, F.; Deng, H.; Diercks, D.; et al. Lowering the operating temperature of protonic ceramic electrochemical cells to < 450 °C. Nat. Energy. 2023, 8, 1145-57.
28. Bello, I. T.; Zhai, S.; Zhao, S.; Li, Z.; Yu, N.; Ni, M. Scientometric review of proton-conducting solid oxide fuel cells. Int. J. Hydrogen. Energy. 2021, 46, 37406-28.
29. Sajid, M.; Hassan, I.; Rahman, A. An overview of cooling of thermoelectric devices. Renew. Sustain. Energy. Rev. 2017, 78, 15-22.
30. Kreuer, K.; Rabenau, A.; Weppner, W. Vehicle Mechanism, A new model for the interpretation of the conductivity of fast proton conductors. Angew. Chem. Int. Ed. 1982, 21, 208-9.
31. Su, H.; Hu, Y. H. Degradation issues and stabilization strategies of protonic ceramic electrolysis cells for steam electrolysis. Energy. Sci. Eng. 2022, 10, 1706-25.
32. Kim, J.; Sengodan, S.; Kim, S.; Kwon, O.; Bu, Y.; Kim, G. Proton conducting oxides: a review of materials and applications for renewable energy conversion and storage. Renew. Sustain. Energy. Rev. 2019, 109, 606-18.
33. Jing, J.; Pang, J.; Chen, L.; Zhang, H.; Lei, Z.; Yang, Z. Structure, synthesis, properties and solid oxide electrolysis cells application of Ba(Ce, Zr)O3 based proton conducting materials. Chem. Eng. J. 2022, 429, 132314.
34. Geneste, G. Proton transfer in barium zirconate: lattice reorganization, landau-zener curve-crossing approach. Solid. State. Ion. 2018, 323, 172-202.
35. Seong, A.; Kim, J.; Jeong, D.; et al. Electrokinetic proton transport in triple (H+/O2-/e-) conducting oxides as a key descriptor for highly efficient protonic ceramic fuel cells. Adv. Sci. 2021, 8, 2004099.
36. Iwahara, H.; Esaka, T.; Uchida, H.; Maeda, N. Proton conduction in sintered oxides and its application to steam electrolysis for hydrogen production. Solid. State. Ion. 1981, 3-4, 359-63.
37. Iwahara, H.; Uchida, H.; Maeda, N. High temperature fuel and steam electrolysis cells using proton conductive solid electrolytes. J. Power. Sources. 1982, 7, 293-301.
38. Iwahara, H.; Uchida, H.; Tanaka, S. High temperature type proton conductor based on SrCeO3 and its application to solid electrolyte fuel cells. Solid. State. Ion. 1983, 9-10, 1021-5.
39. Iwahara, H.; Yajima, T.; Ushida, H. Effect of ionic radii of dopants on mixed ionic conduction (H++O2-) in BaCeO3-based electrolytes. Solid. State. Ion. 1994, 70-71, 267-71.
40. Iwahara, H.; Yajima, T.; Hibino, T.; Ozaki, K.; Suzuki, H. Protonic conduction in calcium, strontium and barium zirconates. Solid. State. Ion. 1993, 61, 65-9.
41. Iwahara, H. Proton conducting ceramics and their applications. Solid. State. Ion. 1996, 86-88, 9-15.
42. Tarasova, N.; Hanif, M. B.; Janjua, N. K.; Anwar, S.; Motola, M.; Medvedev, D. Fluorine-insertion in solid oxide materials for improving their ionic transport and stability. A brief review. Int. J. Hydrogen. Energy. 2024, 50, 104-23.
43. Matkin, D. E.; Starostina, I. A.; Hanif, M. B.; Medvedev, D. A. Revisiting the ionic conductivity of solid oxide electrolytes: a technical review. J. Mater. Chem. A. 2024, 12, 25696-714.
44. Tian, Y.; Abhishek, N.; Yang, C.; et al. Progress and potential for symmetrical solid oxide electrolysis cells. Matter 2022, 5, 482-514.
45. Yang, L.; Wang, S.; Blinn, K.; et al. Enhanced sulfur and coking tolerance of a mixed ion conductor for SOFCs: BaZr0.1Ce0.7Y0.2-xYbxO3-δ. Science 2009, 326, 126-9.
46. Duan, C.; Tong, J.; Shang, M.; et al. Readily processed protonic ceramic fuel cells with high performance at low temperatures. Science 2015, 349, 1321-6.
47. Choi, S.; Kucharczyk, C. J.; Liang, Y.; et al. Exceptional power density and stability at intermediate temperatures in protonic ceramic fuel cells. Nat. Energy. 2018, 3, 202-10.
48. Saito, K.; Yashima, M. High proton conductivity within the ‘Norby gap’ by stabilizing a perovskite with disordered intrinsic oxygen vacancies. Nat. Commun. 2023, 14, 7466.
50. Tarasova, N.; Animitsa, I.; Galisheva, A.; Korona, D. Incorporation and conduction of protons in Ca, Sr, Ba-doped BaLaInO4 with ruddlesden-popper structure. Materials 2019, 12, 1668.
51. Murakami, T.; Hester, J. R.; Yashima, M. High proton conductivity in Ba5Er2Al2ZrO13, a hexagonal perovskite-related oxide with intrinsically oxygen-deficient layers. J. Am. Chem. Soc. 2020, 142, 11653-7.
52. Haugsrud, R.; Norby, T. High-temperature proton conductivity in acceptor-substituted rare-earth ortho-tantalates, LnTaO4. J. Am. Ceram. Soc. 2007, 90, 1116-21.
53. Kim, D.; Bae, K. T.; Kim, K. J.; et al. High-performance protonic ceramic electrochemical cells. ACS. Energy. Lett. 2022, 7, 2393-400.
54. Fop, S.; McCombie, K. S.; Wildman, E. J.; et al. High oxide ion and proton conductivity in a disordered hexagonal perovskite. Nat. Mater. 2020, 19, 752-7.
55. Ling, C. D.; Avdeev, M.; Kharton, V. V.; Yaremchenko, A. A.; Macquart, R. B.; Hoelzel, M. Structures, phase transitions, hydration, and ionic conductivity of Ba4Ta2O9. Chem. Mater. 2010, 22, 532-40.
56. Haugsrud, R.; Norby, T. Proton conduction in rare-earth ortho-niobates and ortho-tantalates. Nature. Mater. 2006, 5, 193-6.
57. Wang, J.; Xie, Y.; Zhang, Z.; Liu, R.; Li, Z. Protonic conduction in Ca2+-doped La2M2O7 (M=Ce, Zr) with its application to ammonia synthesis electrochemically. Mater. Res. Bull. 2005, 40, 1294-302.
58. Wang, N.; Yuan, B.; Zheng, F.; et al. Machine-learning assisted screening proton conducting Co/Fe based oxide for the air electrode of protonic solid oxide cell. Adv. Funct. Mater. 2024, 34, 2309855.
59. Nomura, K.; Shimada, H.; Yamaguchi, Y.; et al. Machine learning based prediction of space group for Ba(Ce0.8-xZrx)Y0.2O3 perovskite-type protonic conductors. Ceram. Int. 2023, 49, 5058-65.
60. Bello, I. T.; Guan, D.; Yu, N.; et al. Revolutionizing material design for protonic ceramic fuel cells: bridging the limitations of conventional experimental screening and machine learning methods. Chem. Eng. J. 2023, 477, 147098.
61. Zhai, S.; Xie, H.; Cui, P.; et al. A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells. Nat. Energy. 2022, 7, 866-75.
62. Luo, Z.; Hu, X.; Zhou, Y.; et al. Harnessing high-throughput computational methods to accelerate the discovery of optimal proton conductors for high-performance and durable protonic ceramic electrochemical cells. Adv. Mater. 2024, 36, 2311159.
63. Fujii, S.; Shimizu, Y.; Hyodo, J.; Kuwabara, A.; Yamazaki, Y. Discovery of unconventional proton-conducting inorganic solids via defect-chemistry-trained, interpretable machine learning. Adv. Energy. Mater. 2023, 13, 2301892.
64. Islam, M. S.; Wang, S.; Hall, A. T.; Mo, Y. First-principles computational design and discovery of solid-oxide proton conductors. Chem. Mater. 2022, 34, 5938-48.
65. Szaro, N. A.; Ammal, S. C.; Chen, F.; Heyden, A. First principles material screening and trend discovery for the development of perovskite electrolytes for proton-conducting solid oxide fuel cells. J. Power. Sources. 2024, 603, 234411.
66. Freysoldt, C.; Grabowski, B.; Hickel, T.; et al. First-principles calculations for point defects in solids. Rev. Mod. Phys. 2014, 86, 253-305.
67. Peng, J.; Zhao, Y.; Wang, X.; Zeng, X.; Wang, J.; Hou, S. Metal-organic frameworks: advances in first-principles computational studies on catalysis, adsorption, and energy storage. Mater. Today. Commun. 2024, 40, 109780.
68. Tawfik, S. A.; Russo, S. P. Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors. J. Cheminform. 2022, 14, 78.
69. Nazemzadeh, N.; Malanca, A. A.; Nielsen, R. F.; Gernaey, K. V.; Andersson, M. P.; Mansouri, S. S. Integration of first-principle models and machine learning in a modeling framework: an application to flocculation. Chem. Eng. Sci. 2021, 245, 116864.
70. Bello, I. T.; Taiwo, R.; Esan, O. C.; et al. AI-enabled materials discovery for advanced ceramic electrochemical cells. Energy. AI. 2024, 15, 100317.
71. Li, Z.; Wang, C.; Chen, X.; et al. A deep-learning-boosted surrogate model of a metal foam based protonic ceramic electrolysis cell stack for uncertainty quantification. Energy. Convers. Manag. 2024, 318, 118886.
72. Liu, X.; Yan, Z.; Wu, J.; et al. Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times. J. Energy. Chem. 2023, 78, 582-8.
73. Chowdhury, A.; Kautz, E.; Yener, B.; Lewis, D. Image driven machine learning methods for microstructure recognition. Comput. Mater. Sci. 2016, 123, 176-87.
74. Ge, M.; Su, F.; Zhao, Z.; Su, D. Deep learning analysis on microscopic imaging in materials science. Mater. Today. Nano. 2020, 11, 100087.
75. Lee, B.; Yoon, S.; Lee, J. W.; et al. Statistical characterization of the morphologies of nanoparticles through machine learning based electron microscopy image analysis. ACS. Nano. 2020, 14, 17125-33.
76. Nandy, A.; Duan, C.; Kulik, H. J. Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery. Curr. Opin. Chem. Eng. 2022, 36, 100778.
77. Jablonka, K. M.; Ongari, D.; Moosavi, S. M.; Smit, B. Big-data science in porous materials: materials genomics and machine learning. Chem. Rev. 2020, 120, 8066-129.
78. Anand, D. V.; Xu, Q.; Wee, J.; Xia, K.; Sum, T. C. Topological feature engineering for machine learning based halide perovskite materials design. npj. Comput. Mater. 2022, 8, 203.
79. Himanen, L.; Jäger, M. O.; Morooka, E. V.; et al. DScribe: library of descriptors for machine learning in materials science. Comput. Phys. Commun. 2020, 247, 106949.
80. Li, Z.; Ma, X.; Xin, H. Feature engineering of machine-learning chemisorption models for catalyst design. Catal. Today. 2017, 280, 232-8.
81. Liu, Y.; Zhao, T.; Ju, W.; Shi, S. Materials discovery and design using machine learning. J. Materiomics. 2017, 3, 159-77.