Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling
Nemanič, V. Hydrogen permeation barriers: Basic requirements, materials selection, deposition methods, and quality evaluation. Nucl. Mater. Energy 19, 451–457 (2019).
Google Scholar
Khosravanian, A. et al. Grand canonical Monte Carlo and molecular dynamics simulations of the structural properties, diffusion and adsorption of hydrogen molecules through poly(benzimidazoles)/nanoparticle oxides composites. Int. J. Hydrogen Energy 43, 2803–2816 (2018).
Google Scholar
Hankoy, M. et al. Enhancing the hydrogen permeation of alumina composite porous membranes via graphene oxide addition. Int. J. Hydrogen Energy 48, 1380–1390 (2023).
Google Scholar
Wei, Y., Yang, W. & Yang, Z. An excellent universal catalyst support-mesoporous silica: Preparation, modification and applications in energy-related reactions. Int. J. Hydrogen Energy 47, 9537–9565 (2022).
Google Scholar
He, G. et al. Photocatalytic hydrogen evolution of nanoporous CoFe2O4 and NiFe2O4 for water splitting. Int. J. Hydrogen Energy 46, 5369–5377 (2021).
Google Scholar
Wang, Y. et al. Crystalline-to-amorphous transformation of tantalum-containing oxides for a superior performance in unassisted photocatalytic water splitting. Int. J. Hydrogen Energy 42, 21006–21015 (2017).
Google Scholar
Qin, M., Hu, Q. & Cheng, Y. F. Passivation of X80 pipeline steel in a carbonate/bicarbonate solution and the effect of oxide film on hydrogen atom permeation into the steel. Int. J. Hydrogen Energy 70, 1–9 (2024).
Google Scholar
Huang, F. et al. Effect of sulfide films formed on X65 steel surface on hydrogen permeation in H2S environments. Int. J. Hydrogen Energy 42, 4561–4570 (2017).
Google Scholar
Ohaeri, E., Eduok, U. & Szpunar, J. Hydrogen related degradation in pipeline steel: a review. Int. J. Hydrogen Energy 43, 14584–14617 (2018).
Google Scholar
Li, Y. et al. Mechanism and evaluation of hydrogen permeation barriers: a critical review. Ind. Eng. Chem. Res. 62, 15752–15773 (2023).
Google Scholar
Plutnar, J. & Pumera, M. Applications of atomic layer deposition in design of systems for energy conversion. Small 17, 2102088 (2021).
Google Scholar
Karimzadeh, S., Safaei, B., Yuan, C. & Jen, T.-C. Emerging atomic layer deposition for the development of high-performance lithium-ion batteries. Electrochem. Energy Rev. 6, 24 (2023).
Google Scholar
Meng, X. Atomic and molecular layer deposition in pursuing better batteries. J. Mater. Res. 36, 2–25 (2021).
Google Scholar
Nilsen, O., Gandrud, K. B., Amund, R. & Helmer, F. Atomic layer deposition for thin-film lithium-ion batteries. In: Atomic layer deposition in energy conversion applications, 183–207, (Wiley, 2017).
Guerra-Nuñez, C., Park, H. G. & Utke, I. Atomic Layer Deposition for Surface and Interface Engineering in Nanostructured Photovoltaic Devices. In: Atomic Layer Deposition in Energy Conversion Applications, 119–148, (Wiley, 2017).
Gupta, B. et al. Recent advances in materials design using atomic layer deposition for energy applications. Adv. Funct. Mater. 32, 2109105 (2022).
Google Scholar
Xing, Z. et al. Atomic layer deposition of metal oxides in perovskite solar cells: present and future. Small Methods 4, 2000588 (2020).
Google Scholar
Shen, C., Yin, Z., Collins, F. & Pinna, N. Atomic layer deposition of metal oxides and chalcogenides for high performance transistors. Adv. Sci. 9, 2104599 (2022).
Google Scholar
Choi, A. R. et al. Review of material properties of oxide semiconductor thin films grown by atomic layer deposition for next-generation 3D dynamic random-access memory devices. Chem. Mater. (2024).
O’Neill, B. J. et al. Catalyst design with atomic layer deposition. ACS Catal. 5, 1804–1825 (2015).
Google Scholar
Singh, J. A., Yang, N. & Bent, S. F. Nanoengineering heterogeneous catalysts by atomic layer deposition. Annu. Rev. Chem. Biomol. Eng. 8, 41–62 (2017).
Google Scholar
Marichy, C. & Pinna, N. Atomic layer deposition to materials for gas sensing applications. Adv. Mater. Interfaces 3, 1600335 (2016).
Google Scholar
Graniel, O., Weber, M., Balme, S., Miele, P. & Bechelany, M. Atomic layer deposition for biosensing applications. Biosens. Bioelectron. 122, 147–159 (2018).
Google Scholar
Weber, M., Julbe, A., Ayral, A., Miele, P. & Bechelany, M. Atomic layer deposition for membranes: basics, challenges, and opportunities. Chem. Mater. 30, 7368–7390 (2018).
Google Scholar
Behroozi, A. H., Vatanpour, V., Meunier, L., Mehrabi, M. & Koupaie, E. H. Membrane fabrication and modification by atomic layer deposition: processes and applications in water treatment and gas separation. ACS Appl. Mater. Interfaces 15, 13825–13843 (2023).
Google Scholar
Gong, N. et al. Atomic layer deposition of Al2O3 thin films for corrosion protections of additive manufactured and wrought stainless steels 316L. Mater. Lett. 331, 133434 (2023).
Google Scholar
Santinacci, L. Atomic layer deposition: an efficient tool for corrosion protection. Curr. Opin. Colloid Interface Sci. 63, 101674 (2023).
Google Scholar
Johnson, R. W., Hultqvist, A. & Bent, S. F. A brief review of atomic layer deposition: from fundamentals to applications. Mater. Today 17, 236–246 (2014).
Google Scholar
George, S., Pandit, P. & Gupta, A. B. Residual aluminium in water defluoridated using activated alumina adsorption – modeling and simulation studies. Water Res. 44, 3055–3064 (2010).
Google Scholar
Detavernier, C., Dendooven, J., Pulinthanathu Sree, S., Ludwig, K. F. & Martens, J. A. Tailoring nanoporous materials by atomic layer deposition. Chem. Soc. Rev. 40, 5242–5253 (2011).
Google Scholar
Keuter, T. et al. Modeling precursor diffusion and reaction of atomic layer deposition in porous structures. J. Vac. Sci. Technol. A Vacuum Surfaces Film. 33, 01A104 (2015).
Zhang, J., Li, Y., Cao, K. & Chen, R. Advances in atomic layer deposition. Nanomanufacturing Metrol. 5, 191–208 (2022).
Google Scholar
Lim, B. S., Rahtu, A. & Gordon, R. G. Atomic layer deposition of transition metals. Nat. Mater. 2, 749–754 (2003).
Google Scholar
Kim, K. et al. Selective metal deposition at graphene line defects by atomic layer deposition. Nat. Commun. 5, 4781 (2014).
Google Scholar
Ritala, M. et al. Atomic layer deposition of oxide thin films with metal alkoxides as oxygen sources. Science 288, 319–321 (2000).
Google Scholar
Ponraj, J. S., Attolini, G. & Bosi, M. Review on atomic layer deposition and applications of oxide thin films. Crit. Rev. Solid State Mater. Sci. 38, 203–233 (2013).
Google Scholar
Macco, B. & Kessels, W. M. M. E. Atomic layer deposition of conductive and semiconductive oxides. Appl. Phys. Rev. 9, 41313 (2022).
Google Scholar
Sønsteby, H. H., Fjellvåg, H. & Nilsen, O. Functional perovskites by atomic layer deposition – an overview. Adv. Mater. Interfaces 4, 1600903 (2017).
Google Scholar
Mackus, A. J. M., Schneider, J. R., MacIsaac, C., Baker, J. G. & Bent, S. F. Synthesis of doped, ternary, and quaternary materials by atomic layer deposition: a review. Chem. Mater. 31, 1142–1183 (2019).
Google Scholar
Hao, W., Marichy, C. & Journet, C. Atomic layer deposition of stable 2D materials. 2D Mater. 6, 12001 (2018).
Google Scholar
Guerra-Nuñez, C., Döbeli, M., Michler, J. & Utke, I. Reaction and growth mechanisms in Al2O3 deposited via atomic layer deposition: elucidating the hydrogen source. Chem. Mater. 29, 8690–8703 (2017).
Google Scholar
Cancellieri, C. et al. Effect of hydrogen on the chemical state, stoichiometry and density of amorphous Al2O3 films grown by thermal atomic layer deposition. Surf. Interface Anal. 56, 293–304 (2024).
Google Scholar
Ruoho, M. et al. Thin-film engineering of mechanical fragmentation properties of atomic-layer-deposited metal oxide. Nanomaterials 10, 558 (2020).
Google Scholar
Schneider, J. M., Larsson, K., Lu, J., Olsson, E. & Hjörvarsson, B. Role of hydrogen for the elastic properties of alumina thin films. Appl. Phys. Lett. 80, 1144–1146 (2002).
Google Scholar
Kim, S. et al. Influence of growth temperature on dielectric strength of Al2O3 thin films prepared via atomic layer deposition at low temperature. Sci. Rep. 12, 5124 (2022).
Google Scholar
Gorham, C. S., Gaskins, J. T., Parsons, G. N., Losego, M. D. & Hopkins, P. E. Density dependence of the room temperature thermal conductivity of atomic layer deposition-grown amorphous alumina (Al2O3). Appl. Phys. Lett. 104, 253107 (2014).
Google Scholar
Moretti, G. Auger parameter shifts in the case of the non-local screening mechanism: applications of the electrostatic model to molecules, solids and adsorbed species. Surf. Interface Anal. 17, 352–356 (1991).
Google Scholar
Jeurgens, L., Sloof, W., Tichelaar, F. & Mittemeijer, E. Thermodynamic stability of amorphous oxide films on metals: Application to aluminum oxide films on aluminum substrates. Phys. Rev. B Condens. Matter Mater. Phys. 62, 4707–4719 (2000).
Google Scholar
Snijders, P. C., Jeurgens, L. P. H. & Sloof, W. G. Structural ordering of ultra-thin, amorphous aluminium-oxide films. Surf. Sci. 589, 98–105 (2005).
Google Scholar
Reichel, F., Jeurgens, L. P. H. H., Richter, G. & Mittemeijer, E. J. Amorphous versus crystalline state for ultrathin Al2O3 overgrowths on Al substrates. J. Appl. Phys. 103, 093515 (2008).
Wang, J., Yu, Y. H., Lee, S. C. & Chung, Y. W. Tribological and optical properties of crystalline and amorphous alumina thin films grown by low-temperature reactive magnetron sputter-deposition. Surf. Coatings Technol. 146-147, 189–194 (2001).
Google Scholar
Angarita, G., Palacio, C., Trujillo, M. & Arroyave, M. Synthesis of Alumina Thin Films Using Reactive Magnetron Sputtering Method. J. Phys. Conf. Ser. 850, 1–4 (2017).
Google Scholar
Hu, Z., Shi, J. & Heath Turner, C. Molecular dynamics simulation of the Al2O3 film structure during atomic layer deposition. Mol. Simul. 35, 270–279 (2009).
Google Scholar
Pugliese, A. et al. Atomic-layer-deposited aluminum oxide thin films probed with X-ray scattering and compared to molecular dynamics and density functional theory models. ACS Omega 7, 41033–41043 (2022).
Google Scholar
Christie, J. K. Review: understanding the properties of amorphous materials with high-performance computing methods. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 381, 20220251 (2023).
Google Scholar
Gramatte, S., Turlo, V. & Politano, O. Do we really need machine learning interatomic potentials for modeling amorphous metal oxides? Case study on amorphous alumina by recycling an existing ab initio database. Model. Simul. Mater. Sci. Eng. 32, 45010 (2024).
Google Scholar
Strand, J. & Shluger, A. L. On the structure of oxygen deficient amorphous oxide films. Adv. Sci. 2306243, 1–14 (2023).
Li, W., Ando, Y. & Watanabe, S. Effects of density and composition on the properties of amorphous alumina: A high-dimensional neural network potential study. J. Chem. Phys.153, 164119 (2020).
Takamoto, S. et al. Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements. Nat. Commun. 13, 2991 (2022).
Google Scholar
Wefers, K. & Misra, C. Oxides and hydroxides of aluminum. Alcoa Tech. Pap. 19, 1–100 (1987).
Dupree, R., Farnan, I., Forty, A. J., El-Mashri, S. & Bottyan, L. A mass NMR study of the structure of amorphous alumina films. Le J. Phys. Colloq. 46, C8–113–C8–117 (1985).
Poe, B. T., McMillan, P. F., Cote, B., Massiot, D. & Coutures, J. P. Silica-alumina liquids: in-situ study by high-temperature aluminum-27 NMR spectroscopy and molecular dynamics simulation. J. Phys. Chem. 96, 8220–8224 (1992).
Google Scholar
Ansell, S. et al. Structure of liquid aluminum oxide. Phys. Rev. Lett. 78, 464–466 (1997).
Google Scholar
Cui, J. et al. Aluminum oxide thin films from aqueous solutions: insights from solid-state NMR and dielectric response. Chem. Mater. 30, 7456–7463 (2018).
Google Scholar
Gutiérrez, G., Belonoshko, A. B., Ahuja, R. & Johansson, B. Structural properties of liquid Al2O3 A molecular dynamics study. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 61, 2723–2729 (2000).
Google Scholar
Landron, C. et al. Liquid alumina: detailed atomic coordination determined from neutron diffraction data using empirical potential structure refinement. Phys. Rev. Lett. 86, 4839–4842 (2001).
Google Scholar
Gramatte, S. et al. Atomistic simulations of the crystalline-to-amorphous transformation of gamma-Al2O3 nanoparticles: delicate interplay between lattice distortions, stresses, and space charges. Langmuir 39, 6301–6315 (2022).
Google Scholar
Snijders, P. C., Jeurgens, L. P. H. & Sloof, W. G. Structure of thin aluminium-oxide films determined from valence band spectra measured using XPS. Surf. Sci. 496, 97–109 (2002).
Google Scholar
Yamaguchi, G., Yasui, I. & Chiu, W.-C. A new method of preparing theta-alumina and the interpretation of its X-Ray-powder diffraction pattern and electron diffraction pattern. Bull. Chem. Soc. Jpn. 43, 2487–2491 (1970).
Google Scholar
Young, M. J. et al. Probing the atomic-scale structure of amorphous aluminum oxide grown by atomic layer deposition. ACS Appl. Mater. Interfaces 12, 22804–22814 (2020).
Google Scholar
Guo, Z., Ambrosio, F. & Pasquarello, A. Extrinsic defects in amorphous oxides: hydrogen, carbon, and nitrogen impurities in alumina. Phys. Rev. Appl. 11, 1 (2019).
Google Scholar
Sundar, A., Yu, J., Qi, L. & Nedim Cinbiz, M. High temperature stability and transport characteristics of hydrogen in alumina via multiscale computation. Int. J. Hydrogen Energy 47, 32345–32357 (2022).
Google Scholar
Tessman, J. R., Kahn, A. H. & Shockley, W. Electronic polarizabilities of ions in crystals. Phys. Rev. 92, 890–895 (1953).
Google Scholar
Jeurgens, L. P. H., Reichel, F., Frank, S., Richter, G. & Mittemeijer, E. J. On the development of long-range order in ultra-thin amorphous Al2O3 films upon their transformation into crystalline gamma-Al2O3. Surf. Interface Anal. 40, 259–263 (2008).
Google Scholar
Shannon, R. D. & Fischer, R. X. Empirical electronic polarizabilities in oxides, hydroxides, oxyfluorides, and oxychlorides. Phys. Rev. B Condens. Matter Mater. Phys. 73, 1–28 (2006).
Google Scholar
van Gog, H. First-principles study of dehydration interfaces between diaspore and corundum, gibbsite and boehmite, and boehmite and γ-Al2O3: Energetic stability, interface charge effects, and dehydration defects. Appl. Surf. Sci. 541, 148501 (2021).
Belonoshko, A. B., Rosengren, A., Dong, Q., Hultquist, G. & Leygraf, C. First-principles study of hydrogen diffusion in alpha – Al2O3 and liquid alumina. Phys. Rev. B Condens. Matter Mater. Phys. 69, 1–6 (2004).
Google Scholar
Niemelä, J.-P. et al. Mechanical properties of atomic-layer-deposited Al2O3/Y2O3 nanolaminate films on aluminum toward protective coatings. ACS Appl. Nano Mater. 5, 6285–6296 (2022).
Google Scholar
Edwards, T. E. J. et al. On the thinnest Al2O3 interlayers in Al-based nanolaminates to enhance strength, and the role of constraint. Acta Mater. 240, 118345 (2022).
Google Scholar
Frankberg, E. J. et al. Highly ductile amorphous oxide at room temperature and high strain rate. Sci. (80-.). 366, 864–869 (2019).
Google Scholar
Lee, J.-S., Ji, J., Jeong, U. & Lee, B.-J. An atomistic simulation study on ductility of amorphous aluminum oxide. Acta Mater. 274, 119985 (2024).
Google Scholar
Koo, J. et al. Evaluating mechanical properties of 100 nm-thick atomic layer deposited Al2O3 as a free-standing film. Scr. Mater. 187, 256–261 (2020).
Google Scholar
Li, X. et al. Review of hydrogen embrittlement in metals: hydrogen diffusion, hydrogen characterization, hydrogen embrittlement mechanism and prevention. Acta Metall. Sin. 33, 759–773 (2020).
Yu, H. et al. Hydrogen embrittlement as a conspicuous material challenge –comprehensive review and future directions. Chem. Rev. 124, 6271–6392 (2024).
Google Scholar
Roberts, R. M., Elleman, T. S., Hayne, P. & Verghese, K. Hydrogen permeability of sintered aluminum oxide. J. Am. Ceram. Soc. 62, 495–499 (1979).
Google Scholar
Somjit, V. & Yildiz, B. Doping α-Al2O3 to reduce its hydrogen permeability: thermodynamic assessment of hydrogen defects and solubility from first principles. Acta Mater. 169, 172–183 (2019).
Google Scholar
Kronenberg, A., Castaing, J., Mitchell, T. & Kirby, S. Hydrogen defects in α-Al2O3 and water weakening of sapphire and alumina ceramics between 600 and 1000 °C I. Infrared characterization of defects. Acta Mater. 48, 1481–1494 (2000).
Google Scholar
Zhao, H. et al. How solute atoms control aqueous corrosion of Al-alloys. Nat. Commun. 15, 561 (2024).
Google Scholar
Gopalan, H. et al. Influence of electrochemical hydrogen charging on the mechanical, diffusional, and interfacial properties of an amorphous alumina coating on Fe-8 wt% Cr alloy. J. Mater. Res. 39, 1812–1821 (2024).
Google Scholar
Ylivaara, O. M. et al. Aluminum oxide from trimethylaluminum and water by atomic layer deposition: The temperature dependence of residual stress, elastic modulus, hardness and adhesion. Thin Solid Films 552, 124–135 (2014).
Google Scholar
Liu, X. & He, D. Atomic layer deposited aluminium oxide membranes for selective hydrogen separation through molecular sieving. J. Memb. Sci. 662, 121011 (2022).
Google Scholar
Aireddy, D. R., Yu, H., Cullen, D. A. & Ding, K. Elucidating the Roles of Amorphous Alumina Overcoat in Palladium-Catalyzed Selective Hydrogenation. ACS Appl. Mater. Interfaces 14, 24290–24298 (2022).
Google Scholar
Thompson, A. P. et al. LAMMPS – a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun. 271, 108171 (2022).
Google Scholar
Asthagiri, D. N. & Beck, T. L. MD simulation of water using a rigid body description requires a small time step to ensure equipartition. J. Chem. Theory Comput. 20, 368–374 (2024).
Google Scholar
Jain, A. et al. A high-throughput infrastructure for density functional theory calculations. Comput. Mater. Sci. 50, 2295–2310 (2011).
Google Scholar
Larsen, A. H. The atomic simulation environment – a python library for working with atoms. J. Phys. Condens. Matter 29, 273002 (2012).
Google Scholar
Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO-the Open Visualization Tool. Model. Simul. Mater. Sci. Eng. 18, 1–7 (2010).
Google Scholar
Takamoto, S. et al. Matlantis, software as a service style material discovery tool. https://matlantis.com.
Gramatte, S. et al. Effect of hydrogen on the local chemical bonding states and structure of amorphous alumina by atomistic and electrostatic modeling of auger parameter shifts, (2024).
Seeger, M. Gaussian processes for machine learning University of California at Berkeley. Int. J. Neural Syst. 14, 69–109 (2004).
Google Scholar
Brochu, E., Cora, V. M. & de Freitas, N. A Tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv, (2010).
Rasmussen, C. E. & Williams, C. K. I. Gaussian processes for machine learning, (The MIT Press, 2005).
Moretti, G. Auger parameter and Wagner plot in the characterization of chemical states by X-ray photoelectron spectroscopy: a review. J. Electron Spectros. Relat. Phenomena 95, 95–144 (1998).
Google Scholar
Nogueira, F. et al. Valence-band and chemical-state analyses of Zr and O in thermally grown thin zirconium-oxide films: an XPS study. Surf. Interface Anal. 496, 175–178 (1985).
Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. Rev. B 47, 558–561 (1993).
Google Scholar
Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metalamorphous- semiconductor transition in germanium. Phys. Rev. B 49, 14251–14269 (1994).
Google Scholar
Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).
Google Scholar
Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6, 15–50 (1996).
Google Scholar
Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994).
Google Scholar
Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B Condens. Matter Mater. Phys. 59, 1758–1775 (1999).
Google Scholar
Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).
Google Scholar
Perdew, J. P. & Zunger, A. Self-interaction correction to density-functional approximations for many-electron systems. Phys. Rev. B 23, 5048–5079 (1981).
Google Scholar
Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys.132, 154104 (2010).
Henkelman, G., Arnaldsson, A. & Jónsson, H. A fast and robust algorithm for Bader decomposition of charge density. Comput. Mater. Sci. 36, 354–360 (2006).
Google Scholar
Yu, M. & Trinkle, D. R. Accurate and efficient algorithm for Bader charge integration. J. Chem. Phys. 134, 064111 (2011).
Sanville, E., Kenny, S. D., Smith, R. & Henkelman, G. Improved grid-based algorithm for Bader charge allocation. J. Comput. Chem. 28, 899–908 (2007).
Google Scholar
Tang, W., Sanville, E. & Henkelman, G. A grid-based Bader analysis algorithm without lattice bias. J. Phys. Condens. Matter. 21, 084204 (2009).
Moretti, G. & Beck, H. P. Relationship between the Auger parameter and the ground state valence charge at the core-ionized site. Surf. Interface Anal. 52, 864–868 (2020).
Google Scholar
Beck, H. P. & Moretti, G. Physical genetics: Cross-breeding density functional theory and X-ray photoelectron spectroscopy to rationalize chemical shifts of binding energies in solid compounds. Solid State Sci. 110, 106359 (2020).
Google Scholar
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