[1] S.S. Das, J. Kumar, S. Dawn, F. Salata, Existing stature and possible outlook of renewable power in comprehensive electricity market, Processes 11 (6) (2023) 1849. [2] N. Liu, X.P. Liu, F.M. Wang, F. Xin, M.S. Sun, Y. Zhai, X.B. Zhang, CFD simulation study of the effect of baffles on the fluidized bed for hydrogenation of silicon tetrachloride, Chin. J. Chem. Eng. 45 (2022) 219-228. [3] F. Chigondo, From metallurgical-grade to solar-grade silicon: an overview, Silicon 10 (3) (2018) 789-798. [4] J. Zheng, Y.G. Huang, G.Q. Huang, Research progress of plasma hydrogenation of silicon tetrachloride, Chem. Ind. Eng. Prog. 34 (2015) 1532-1538. [5] J.S. Liu, Z. Ding, H.H. Zhang; J.Y. Chen, C. Hua, P Lu, Research progress on process and mechanism for cold hydrogenation of silicon tetrachloride, Mod. Chem. Ind. 43 (2023) 30-34. [6] Y.F. Liu, Study on the catalyst in the hydrogenation process of silicon tetrachloride, Master Thesis Tianjin Univ. China, 2016. [7] W.J. Ding, J.M. Yan, W.D. Xiao, Hydrogenation of silicon tetrachloride in the presence of silicon: thermodynamic and experimental investigation, Ind. Eng. Chem. Res. 53 (27) (2014) 10943-10953. [8] Q. Li, P. Li, T. Wang, Experimental study on hydrogenation of SiCl4 to SiHCl3 in a stirred bed reactor, Chin. J. Process Eng. 16 (2016) 767-773. [9] J. Xu, S.J. Song, J. Li, Y.J. Ji, Z.X. Li, D.X. Fu, Z.Y. Zhong, G.W. Xu, F.B. Su, Forming multiple heterojunctions in ZnO/Cu/Cu2O boosts dimethyldichlorosilane production in Rochow-Muller reaction, J. Catal. 419 (2023) 99-111. [10] H. Zhang, B.F. Jin, Y.X. Zhu, L.Q. Ban, K.J. Wang, J. Xu, J.J. Gao, Z.Y. Zhong, G.W. Xu, F.B. Su, Deciphering the promoting mechanism of SnO2 to Cu2O in the Rochow-Muller reaction, J. Catal. 425 (2023) 143-154. [11] J.B. Geng, Y.X. Zhu, B.F. Jin, J.J. Gao, Z.G. Zhang, Z.Y. Zhong, G.W. Xu, F.B. Su, Structural evolution of the CuO catalyst modified with ZnO, Sn, and P promoters in the Rochow-Muller reaction, J. Catal. 429 (2024) 115262. [12] N. Kunioshi, K. Anzai, H. Ushijima, A. Fuwa, Effects of cluster size on calculation of activation energies of silicon surface reactions with H2 and HCl, J. Cryst. Growth 418 (2015) 115-119. [13] K. Anzai, N. Kunioshi, A. Fuwa, Analysis of the dynamics of reactions of SiCl2 at Si(100) surfaces, Appl. Surf. Sci. 392 (2017) 410-417. [14] S. Yadav, C.V. Singh, Molecular adsorption and surface formation reactions of HCl, H2 and chlorosilanes on Si(100)-c(4 ×2) with applications for high purity silicon production, Appl. Surf. Sci. 475 (2019) 124-134. [15] C. Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell. 1 (5) (2019) 206-215. [16] R.H. Ouyang, S. Curtarolo, E. Ahmetcik, M. Scheffler, L.M. Ghiringhelli, SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates, Phys. Rev. Materials 2 (8) (2018) 083802. [17] C.J. Bartel, S.L. Millican, A.M. Deml, J.R. Rumptz, W. Tumas, A.W. Weimer, S. Lany, V. Stevanovic, C.B. Musgrave, A.M. Holder, Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry, Nat. Commun. 9 (2018) 4168. [18] C.J. Bartel, C. Sutton, B.R. Goldsmith, R.H. Ouyang, C.B. Musgrave, L.M. Ghiringhelli, M. Scheffler, New tolerance factor to predict the stability of perovskite oxides and halides, Sci. Adv. 5 (2) (2019) eaav0693. [19] R.H. Ouyang, E. Ahmetcik, C. Carbogno, M. Scheffler, L.M. Ghiringhelli, Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO, J. Phys. Mater. 2 (2) (2019) 024002. [20] G. Kresse, J. Furthmuller, Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set, Comput. Mater. Sci. 6 (1) (1996) 15-50. [21] F.R. Qian, L.S. Peng, Y.J. Zhuang, L. Liu, Q.J. Chen, Direct atomic-level insight into oxygen reduction reaction on size-dependent Pt-based electrocatalysts from density functional theory calculations, Chin. J. Chem. Eng. 61 (2023) 140-146. [22] S. Grimme, S. Ehrlich, L. Goerigk, Effect of the damping function in dispersion corrected density functional theory, J. Comput. Chem. 32 (7) (2011) 1456-1465. [23] E.L. Kolsbjerg, M.N. Groves, B. Hammer, An automated nudged elastic band method, J. Chem. Phys. 145 (9) (2016) 094107. [24] R.G. Tanguturi, J.C. Tsai, Y.S. Li, J.S. Tsay, Impact of a rubrene buffer layer on the dynamic magnetic behavior of nickel layers on Si(100), Phys. Chem. Chem. Phys. 25 (46) (2023) 32029-32039. [25] V.G. Antunes, M.J.M. Jimenez, F. Cemin, C.A. Figueroa, F. Alvarez, Comparative passivation of Si(100) by H2 and D2 atmospheres under simultaneous Xe+ bombardment: an X-ray photoelectron spectroscopy analysis, Langmuir 40 (9) (2024) 4824-4830. [26] C.A. Correa, O. Perez, J. Kopecek, P. Brazda, M. Klementova, L. Palatinus, Crystal structures of η"-Cu3+xSi and η'''-Cu3+xSi, ACTA Crystallogr. B 73 (4) (2017) 767-774. [27] H.H. Wang, Q. Zheng, Y. Wang, Y.W. Ji, W.C. Peng, J.L. Zhang, J.S. Zhang, J.C. Liu, Effect of Cu vacancy on Cu3Si(001) surface for the synthesis of SiHCl3 by hydrogenation of SiCl4: a DFT study, ChemistrySelect 7 (43) (2022) e202202818. [28] X. Liu, W. An, C.H. Turner, D.E. Resasco, Hydrodeoxygenation of m-cresol over bimetallic NiFe alloys: Kinetics and thermodynamics insight into reaction mechanism, J. Catal. 359 (2018) 272-286. [29] W. Tang, E. Sanville, G. Henkelman, A grid-based Bader analysis algorithm without lattice bias, J. Phys. Condens. Mat. 21 (8) (2009) 084204. [30] S.P. Ong, W.D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V.L. Chevrier, K.A. Persson, G. Ceder, Python materials genomics (pymatgen): a robust, open-source python library for materials analysis, Comput. Mater. Sci. 68 (2013) 314-319. [31] Y.L. Han, H.Y. Zhang, Z.N. Zeng, Z.Y. Liu, D.N. Lu, Z. Liu, Descriptor-augmented machine learning for enzyme-chemical interaction predictions, Synth. Syst. Biotechnol. 9 (2) (2024) 259-268. [32] Z. Ding, L. Guo, C. Hua, J.Y. Chen, P. Lu, Investigation of the thermodynamic characteristics of low-temperature hydrogenation of silicon tetrachloride, Silicon 16 (13) (2024) 5417-5429. [33] H.H. Wang, W.C. Peng, J.S. Peng, J.L. Zhang, The research of key reaction of SiCl4 hydrogenation with density functional theory simulation, J. Shihezi University (Natural Science) 39 (2021) 537-540. [34] Z. Lian, C.W. Si, F. Jan, M. Yang, B. Li, Resolving the mechanism complexity of oxidative dehydrogenation of hydrocarbons on nanocarbon by microkinetic modeling, ACS Catal. 10 (23) (2020) 14006-14014. [35] N. Acerbi, S.C. Tsang, G. Jones, S. Golunski, P. Collier, Rationalization of interactions in precious metal/ceria catalysts using the d-band center model, Angew. Chem. Int. Ed 52 (30) (2013) 7737-7741. [36] I. Takigawa, K.I. Shimizu, K. Tsuda, S. Takakusagi, Machine-learning prediction of the d-band center for metals and bimetals, RSC Adv. 6 (58) (2016) 52587-52595. [37] C. Meng, T. Ling, T.Y. Ma, H. Wang, Z.P. Hu, Y. Zhou, J. Mao, X.W. Du, M. Jaroniec, S.Z. Qiao, Atomically and electronically coupled Pt and CoO hybrid nanocatalysts for enhanced electrocatalytic performance, Adv. Mater. 29 (9) (2017) 1604607. [38] S.L. Jiao, X.W. Fu, H.W. Huang, Descriptors for the evaluation of electrocatalytic reactions: d-band theory and beyond, Adv. Funct. Mater. 32 (4) (2022) 2107651. [39] Q.S. Zhu, W.W. Huang, C. Huang, L. Gao, Y.J. Su, L.J. Qiao, The d band center as an indicator for the hydrogen solution and diffusion behaviors in transition metals, Int. J. Hydrog. Energy 47 (90) (2022) 38445-38454. |