REFERENCES
1. Jamesh, M.; Tong, H.; Santoso, S. P.; et al. Recent advances in developing nanoscale electro-/photocatalysts for hydrogen production: modification strategies, charge-carrier characterizations, and applications. Nanoscale 2024, 16, 18213-50.
2. Xu, F.; Weng, B. Photocatalytic hydrogen production: an overview of new advances in structural tuning strategies. J. Mater. Chem. A. 2023, 11, 4473-86.
3. Choi, J.; Jung, W.; Gonzalez-Carrero, S.; Durrant, J. R.; Cha, H.; Park, T. Understanding charge carrier dynamics in organic photocatalysts for hydrogen evolution. Energy. Environ. Sci. 2024, 17, 7999-8018.
4. Rahman, M. Z.; Edvinsson, T.; Gascon, J. Hole utilization in solar hydrogen production. Nat. Rev. Chem. 2022, 6, 243-58.
5. Balapure, A.; Ray Dutta, J.; Ganesan, R. Recent advances in semiconductor heterojunctions: a detailed review of the fundamentals of photocatalysis, charge transfer mechanism and materials. RSC. Appl. Interfaces. 2024, 1, 43-69.
6. Molaei, M. J. Recent advances in hydrogen production through photocatalytic water splitting: a review. Fuel 2024, 365, 131159.
7. Li, X.; Wang, Q.; Sun, Y.; Sun, S.; Ge, L. Photogenerated charge carriers’ regulation strategies: structure design, mechanism, and characterization technology. Int. J. Hydrogen. Energy. 2024, 69, 1341-65.
8. Chen, T.; Qin, T.; Sun, H.; Su, C. Oxygen vacancy in lead‐free perovskite Cs2AgBiBr6/WO3-X for enhanced photocatalytic C(sp3)‐H bond oxidation. Rare. Met. 2025, 44, 6354-65.
9. Li, R.; Luan, J.; Zhang, Y.; et al. A review of efficient photocatalytic water splitting for hydrogen production. Renew. Sustain. Energy. Rev. 2024, 206, 114863.
10. Pastor, E.; Sachs, M.; Selim, S.; Durrant, J. R.; Bakulin, A. A.; Walsh, A. Electronic defects in metal oxide photocatalysts. Nat. Rev. Mater. 2022, 7, 503-21.
11. Chen, R.; Ren, Z.; Liang, Y.; et al. Spatiotemporal imaging of charge transfer in photocatalyst particles. Nature 2022, 610, 296-301.
12. Dai, B.; Zhou, C.; Yin, H.; Kong, R.; Wang, H.; Xie, Y. Advanced catalysis for efficient hydrogen production. Microstructures 2025, 5, 2025083.
13. Liu, Q.; Zhan, F.; Luo, H.; et al. Na‐Sm bimetallic regulation and band structure engineering in CaBi2Nb2O9 to enhance piezo‐photo‐catalytic performance. Adv. Funct. Mater. 2023, 33, 2303736.
14. Zhang, S.; Yi, X.; Hu, G.; et al. Configuration regulation of active sites by accurate doping inducing self-adapting defect for enhanced photocatalytic applications: a review. Coord. Chem. Rev. 2023, 478, 214970.
15. Wang, S.; Niu, X.; Wang, L.; Bi, J.; Yang, W.; Hou, H. Recent advances in non-metal doped ZnS nanostructures for photocatalytic hydrogen production. Microstructures 2025, 5, 2025074.
16. Zhu, C. Z.; Tian, Q. H.; Wang, B. H.; et al. Application of modified cerium dioxide for photocatalytic air pollution purification. Rare. Met. 2024, 43, 5473-86.
17. Yang, Y.; Toyoda, M.; Yamaguchi, A.; et al. Bandgap widening through doping for improving the photocatalytic oxidation ability of narrow-bandgap semiconductors. Phys. Chem. Chem. Phys. 2023, 25, 255-61.
18. Ali, S.; Ismail, P. M.; Khan, M.; et al. Charge transfer in TiO2-based photocatalysis: fundamental mechanisms to material strategies. Nanoscale 2024, 16, 4352-77.
19. Lu, J.; Shan, P.; Chen, F.; et al. Construction of a recyclable foam photocatalyst for boosted photothermal-assisted photocatalytic H2 production. J. Alloys. Compd. 2025, 1014, 178819.
20. Sui, Q.; Li, R.; Zhang, Y.; et al. High-efficient C3N4-viologen charge transfer systems for promoting photocatalytic H2 evolution through band engineering. Int. J. Hydrogen. Energy. 2023, 48, 16330-40.
21. Luo, J.; Han, H.; Wu, J.; et al. Excellent photocatalytic activity of MoO3-adorned g-C3N4 systems: construction of S-scheme heterojunction. Appl. Surf. Sci. 2022, 604, 154512.
22. Yang, M.; Zhan, X. Q.; Ou, D. L.; et al. Efficient visible‐light‐driven hydrogen production with Ag‐doped flower‐like ZnIn2S4microspheres. Rare. Met. 2024, 44, 1024-41.
23. Lin, R.; Hsueh, C.; Yu, H.; Heine, T.; Chen, C.; Murayama, T. The challenge and opportunity of organic semiconductors in photocatalysis. Microstructures 2025, 5, 2025071.
24. Wu, B. Y.; Huang, Z. S.; Zhao, D. F.; et al. Computational screening‐aided design of transition metal‐doped CeO2 as NH3‐SCR catalysts. Rare. Met. 2025, 44, 6303-18.
25. Jin, W.; Yang, C.; Pau, R.; et al. Photocatalytic doping of organic semiconductors. Nature 2024, 630, 96-101.
26. Guo, Y.; Wen, H.; Guo, Y.; et al. Surface states modulation via vacancy engineering for creating band bending on semiconductor BiOBr photocatalysts. Colloids. Surf. A. Physicochem. Eng. Asp. 2024, 682, 132915.
27. Zafar, Z.; Yi, S.; Li, J.; et al. Recent development in defects engineered photocatalysts: an overview of the experimental and theoretical strategies. Energy. Environ. Mater. 2021, 5, 68-114.
28. Wang, J.; Liu, X.; Zheng, H.; et al. Spin-selection in L-chiral Ag-doped WS2 nanoflower for photocatalysis. Microstructures 2026, 6, 2026032.
29. Meng, L.; Li, Y.; Zhao, C.; et al. Orbital hybridization-driven singlet oxygen generation in piezo-photocatalytic self-fenton system: synergistic band engineering and spin-state modulation for sustainable water remediation. Appl. Catal. B. Environ. Energy. 2025, 377, 125521.
30. Liu, Y.; Feng, S.; Yu, J. First-principles study on the electronic structure and photocatalytic properties of transition metal(Fe,Co,Ni) and N co-doped graphene-like ZnO monolayer. Solid. State. Commun. 2025, 404, 116037.
31. Zhang, F.; Sa, R.; Kong, K.; et al. Electronic configurations modulated by heterometallic orbital interactions of spinel cocatalysts in covalent organic framework-based dyads for photocatalytic hydrogen evolution. ACS. Catal. 2026, 16, 4868-76.
32. Lv, X.; Ding, X.; Gong, J.; et al. Research progress on rbital hybridization in photocatalysis and electrocatalysis. Acta. Phys. Chim. Sin. 2026, 42, 100151.
33. Liu, X.; Pu, Y.; Li, Y.; Liu, Z.; Wang, J. Enhanced photocatalytic water splitting via chiral cadmium zinc sulfide nanostructures: spin polarization modulation for efficient charge carrier dynamics. Solar. RRL. 2025, 10, e202500805.
34. Miao, Y.; Yuan, J.; Zhou, L.; et al. Enhancing hydrogen production from photocatalytic water splitting over ZnIn2S4/Ni-MOF via magnetically induced spin polarization. Appl. Catal. B. Environ. Energy. 2026, 387, 126513.
35. Li, Y.; Wang, Z.; Wang, Y.; et al. Local magnetic spin mismatch promoting photocatalytic overall water splitting with exceptional solar-to-hydrogen efficiency. Energy. Environ. Sci. 2022, 15, 265-77.
36. Wu, Y.; Cheng, L.; Tang, Y.; Sun, H.; Xiang, Q. Unified perspectives on spin polarization in heterogeneous photo/electrocatalysis. Adv. Funct. Mater. 2025, 36, e16617.
37. Takubo, K.; Ono, A.; Ueno, S.; et al. Photocontrol of ferroelectricity in multiferroic BiFeO3 via structural modification coupled with photocarrier. Commun. Mater. 2024, 5, 261.
38. Zhang, J.; Zhang, Y.; Li, L.; et al. Synergizing the internal electric field and ferroelectric polarization of the BiFeO3/ZnIn2S4 Z-scheme heterojunction for photocatalytic overall water splitting. J. Mater. Chem. A. 2023, 11, 434-46.
39. Wu, J. Y.; Jin, X.; Wang, J.; et al. Enhancement of photocatalytic activity in BiFeO3 nanoparticles through electrical polarization. Adv. Energy. Sustain. Res. 2025, 6, 2400285.
40. Ahmad, T.; Jindal, K.; Tomar, M.; Jha, P. K. Effect of codoping of rare earth elements and Cr on multiferroic, optical and photocatalytic properties of BiFeO3. Mater. Today. Commun. 2023, 37, 107516.
41. Dhawan, A.; Sudhaik, A.; Raizada, P.; et al. BiFeO3-based Z scheme photocatalytic systems: advances, mechanism, and applications. J. Ind. Eng. Chem. 2023, 117, 1-20.
42. He, J.; Liu, Y.; Qu, J.; Zhang, J.; Fan, F.; Li, C. The ferroelectric effects of rhombohedral and tetragonal BiFeO3 in photoelectrochemical water splitting. J. Phys. Chem. Lett. 2024, 15, 6031-7.
43. Kumar, M.; Kumar, A.; Sharma, S.; et al. Investigations on low temperature magnetic and magnetoelectric properties of multiferroic-NiO nanocomposites. J. Alloys. Compd. 2023, 965, 171353.
44. Shah, J. H.; Malik, A. S.; Idris, A. M.; Rasheed, S.; Han, H.; Li, C. Intrinsic photocatalytic water oxidation activity of Mn-doped ferroelectric BiFeO3. Chin. J. Catal. 2021, 42, 945-52.
45. Wang, Y.; Zheng, L.; Liu, P.; Chen, F.; Chen, R. Tunable photocatalytic and magnetic properties in Y and transition metals (TM = Mn, Co, Ni, Cu, Zn) co-doped BiFeO3: DFT + U study*. Ferroelectrics 2023, 615, 212-22.
46. Yang, C.; Zhang, Y.; Yue, F.; et al. Co doping regulating electronic structure of Bi2MoO6 to construct dual active sites for photocatalytic nitrogen fixation. Appl. Catal. B. Environ. 2023, 338, 123057.
47. Akarchaou, E. H.; Touti, R.; El, Mekkaouy. A.; Didi, Y.; Tahiri, A.; Chtita, S. Computational analysis of X2MgTiH6 (X = Li, Na, and K) double perovskite hydride materials for hydrogen storage applications. Int. J. Hydrogen. Energy. 2025, 161, 150644.
48. Mohamed Samy, M.; Mekhemer, I. M.; Mohamed, M. G.; et al. Conjugated microporous polymers incorporating Thiazolo[5,4-d]thiazole moieties for Sunlight-Driven hydrogen production from water. Chem. Eng. J. 2022, 446, 137158.
49. Xia, Y.; Chen, S.; Zhu, X.; Ren, Z.; Feng, W. High‐efficiency S‐scheme Bi4O5IBr/Bi5O7I0.7Br0.3 double solid solution heterojunction photocatalyst for levofloxacin degradation via optimized carrier dynamics. Energy. Environ. Mater. 2026, 9, e70204.
50. He, J.; Yue, T.; Kim, Y. L.; Li, Y. High-throughput screening of piezoelectric hybrid organic–inorganic perovskites via density-functional theory and machine learning. J. Mater. Chem. A. 2026, 14, 24610-23.
51. Hu, W.; Zhang, L. High-throughput calculation and machine learning of two-dimensional halide perovskite materials: formation energy and band gap. Mater. Today. Commun. 2023, 35, 105841.
52. Zhao, G.; Yu, M.; He, C. Theoretical study of nitrate reduction to ammonia on dual-atom doped CoP(101) catalysts. Surf. Interfaces. 2025, 75, 107859.
53. Zhao, G.; Dai, S.; He, C. Dopant-engineered Ir(100) surfaces for efficient N2 activation: a combined multi-objective screening and electronic-structure mechanistic study. Comput. Theor. Chem. 2026, 1259, 115730.
54. Augustine, L. J.; Wang, Y.; Taylor, M. G.; et al. Coupling high-throughput density functional theory, automated experimentation, and adaptive experimental design to achieve selective rare-earth element separations. J. Am. Chem. Soc. 2026, 148, 40676-87.
55. Su, B. R.; Liu, J. Q.; Zhao, D.; et al. Past, present, prospect: AI‐driven evolution of low‐dimensional material design for sustainable environmental solutions. Rare. Met. 2025, 44, 9671-701.
56. Wang, Y.; Zhang, W.; Guan, H.; et al. Interpretable ML-driven high-throughput screening reveals orbital–site–spin synergy for cross-field-enhanced multiferroic perovskite photocatalyst design. ACS. Catal. 2026, 16, 16994-7010.
57. Song, J.; Wang, S.; Zhang, Z.; Niu, K.; Shan, Y.; Li, Y. Integrated PredRNN++ and phase-field for microstructure prediction and simulation acceleration. Microstructures 2026, 6, 2026074.
58. Liu, J.; Liang, L.; Su, B.; et al. Transformative strategies in photocatalyst design: merging computational methods and deep learning. J. Mater. Inf. 2024, 4, 33.
59. Lee, J.; Sun, H.; Liu, Y.; Li, X.; Liu, Y.; Kim, M. State-of-health estimation and anomaly detection in Li-ion batteries based on a novel architecture with machine learning. Batteries 2023, 9, 264.
60. Fan, T.; Lei, H.; Wang, K.; Qu, B. High-throughput screening of Ni-rich layered cathode materials for sodium-ion batteries based on machine learning. J. Alloys. Compd. 2025, 1026, 180282.
61. El Fallah, S.; Kharbach, J.; Vanagas, J.; et al. Advanced state of charge estimation using deep neural network, gated recurrent unit, and long short-term memory models for lithium-ion batteries under aging and temperature conditions. Appl. Sci. 2024, 14, 6648.
62. Islam, S.; Namkoong, G. Interpretable battery SOH prediction: a comparative interpretability framework for multi-architecture ML models. J. Electrochem. Soc. 2026, 173, 110505.
63. Acharyya, S.; Bhowmick, P. K.; Guha, P. K. Selective identification and quantification of VOCs using metal nanoparticles decorated SnO2 hollow-spheres based sensor array and machine learning. J. Alloys. Compd. 2023, 968, 171891.
64. Zafari, M.; Kumar, D.; Umer, M.; Kim, K. S. Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts. J. Mater. Chem. A. 2020, 8, 5209-16.
65. Sheng, Y.; Wu, Y.; Jiang, C.; et al. Interpretable model of dielectric constant for rational design of microwave dielectric materials: a machine learning study. J. Mater. Inf. 2025, 5, 7.
66. Sahu, N.; Azad, C.; Kumar, U. Study and prediction of photocurrent density with external validation using machine learning models. Int. J. Hydrogen. Energy. 2024, 92, 1335-55.
67. Wei, M.; Chen, Y.; Bi, Y.; et al. Degradation-informed closed-loop feature optimization and SHAP-enhanced machine learning for lithium-ion battery SOH estimation. J. Power. Sources. 2026, 689, 240784.
68. Li, X.; Zhang, H.; Wang, C.; et al. Accelerated design of 5xxx-Series Al-Mg alloys with enhanced mechanical properties via interpretable machine learning and microstructural validation. J. Alloys. Compd. 2026, 1050, 185653.
