High-throughput screening of multimetallic catalysts for three-way catalysis

References

• Vedyagin AA, Gavrilov MS, Volodin AM, et al. Catalytic purification of exhaust gases over Pd-Rh alloy catalysts. Topics Catal. 2013;56(11):1008–10. doi: 10.1007/s11244-013-0064-8
   Web of Science ®Google Scholar
• Dimick PS, Kross JL, Roberts EG, et al. Examining the surface of a synergistic Pt-Rh/γ-Al2O3 catalyst using NO as a probe molecule. Appl Catal B Environ. 2009;89(1–2):1–11. doi: 10.1016/j.apcatb.2008.11.025
   Web of Science ®Google Scholar
• Haneda M, Kaneko T, Kamiuchi N, et al. Improved three-way catalytic activity of bimetallic Ir-Rh catalysts supported on CeO2-ZrO2. Catal Sci Technol. 2015;5(3):1792–1800. doi: 10.1039/C4CY01502A
   Web of Science ®Google Scholar
• Asakura H, Kirihara M, Fujita K, et al. Fe-modified CuNi alloy catalyst as a nonprecious metal catalyst for three-way catalysis. Ind Eng Chem Res. 2020;59(45):19907–19917. doi: 10.1021/acs.iecr.0c03389
   Web of Science ®Google Scholar
• Hirakawa T, Shimokawa Y, Miyahara Y, et al. Activity–composition relationships of Fe-Ni-Cu ternary nanoparticles supported on Al2O3 as three-way catalysts for NO reduction. ACS Appl Nano Mater. 2021;4(10):10613–10622. doi: 10.1021/acsanm.1c02087
   Web of Science ®Google Scholar
• Pollice R, dos Passos Gomes G, Aldeghi M, et al. Data-driven strategies for accelerated materials design. Acc Chem Res. 2021;54(4):849–860. doi: 10.1021/acs.accounts.0c00785
   PubMed Web of Science ®Google Scholar
• Takahashi K, Ohyama J, Nishimura S, et al. Catalysts informatics: paradigm shift towards data-driven catalyst design. Chem Comm. 2023;59(16):2222–2238. doi: 10.1039/D2CC05938J
   PubMed Web of Science ®Google Scholar
• Nishimura S, Ohyama J, Kinoshita T, et al. Revisiting machine learning predictions for oxidative coupling of methane (OCM) based on literature data. ChemCatchem. 2020;12(23):5888–5892. doi: 10.1002/cctc.202001032
   Web of Science ®Google Scholar
• Shevlin M. Practical high-throughput experimentation for chemists. ACS Med Chem Lett. 2017;8(6):601–607. doi: 10.1021/acsmedchemlett.7b00165
   PubMed Web of Science ®Google Scholar
• Hendershot RJ, Vijay R, Snively CM, et al. High-throughput study of the performance of NOx storage and reduction catalysts as a function of cycling conditions and catalyst composition. Chem Eng Sci. 2006;61(12):3907–3916. doi: 10.1016/j.ces.2006.01.025
   Web of Science ®Google Scholar
• Iojoiu EE, Bassou B, Guilhaume N, et al. High-throughput approach to the catalytic combustion of diesel soot. Catal Today. 2008;137(1):103–109. doi: 10.1016/j.cattod.2008.02.016
   Web of Science ®Google Scholar
• Kern P, Klimczak M, Heinzelmann T, et al. High-throughput study of the effects of inorganic additives and poisons on NH3-SCR catalysts–part II: Fe-zeolite catalysts. Appl Catal B Environ. 2010;95(1–2):48–56. doi: 10.1016/j.apcatb.2009.12.008
   Web of Science ®Google Scholar
• Sundermann A, Gerlach O. High-throughput screening as a supplemental tool for the development of advanced emission control catalysts: methodological approaches and data processing. Catalysts. 2016;6(2):23. doi: 10.3390/catal6020023
   Web of Science ®Google Scholar
• Tran TPN, Thakur A, Nguyen TN, et al. Understanding chemiluminescence in catalytic oxidation of CO and hydrocarbons. CatalToday. 2021;375:56–63. doi: 10.1016/j.cattod.2020.02.034
   Google Scholar
• Chammingkwan P, Terano M, Taniike T. High-throughput synthesis of support materials for olefin polymerization catalyst. ACS Comb Sci. 2017;19(5):331–342. doi: 10.1021/acscombsci.7b00010
   PubMed Web of Science ®Google Scholar
• Nakanowatari S, Nguyen TN, Chikuma H, et al. Extraction of catalyst design heuristics from random catalyst dataset and their utilization in catalyst development for oxidative coupling of methane. ChemCatchem. 2021;13(14):3262–3269. doi: 10.1002/cctc.202100460
   Web of Science ®Google Scholar
• Torres JAG, Lau SH, Anchuri P, et al. A multi-objective active learning platform and web app for reaction optimization. J Am Chem Soc. 2022;144(43):19999–20007. doi: 10.1021/jacs.2c08592
   PubMedGoogle Scholar
• Schleinitz J, Langevin M, Smail Y, et al. Machine learning yield prediction from NiCOlit, a small-size literature data set of nickel catalyzed C–O couplings. J Am Chem Soc. 2022;144(32):14722–14730. doi: 10.1021/jacs.2c05302
   PubMed Web of Science ®Google Scholar
• Vittoria A, Urciuoli G, Costanzo S, et al. Extending the high-throughput experimentation (HTE) approach to catalytic olefin polymerizations: from catalysts to materials. Macromolecules. 2022;55(12):5017–5026. doi: 10.1021/acs.macromol.2c00813
   Web of Science ®Google Scholar
• Foppa L, Sutton C, Ghiringhelli LM, et al. Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence. ACS Catal. 2022;12(4):2223–2232. doi: 10.1021/acscatal.1c04793
   PubMed Web of Science ®Google Scholar
• Takimoto K, Takeuchi K, Ton NNT, et al. Exploring stabilizer formulations for light-induced yellowing of polystyrene by high-throughput experimentation and machine learning. Polym Degrad Stab. 2022;201:109967. doi: 10.1016/j.polymdegradstab.2022.109967
   Web of Science ®Google Scholar
• Allen CL, Leitch DC, Anson MS, et al. The power and accessibility of high-throughput methods for catalysis research. Nat Catal. 2019;2(1):2–4. doi: 10.1038/s41929-018-0220-4
   Web of Science ®Google Scholar
• Afonso RV, Gouveia JD, Gomes JR. Catalytic reactions for H2 production on multimetallic surfaces: a review. J Phys Energy. 2021;3(3):032016. doi: 10.1088/2515-7655/ac0d9f
   Google Scholar
• Löffler T, Ludwig A, Rossmeisl J, et al. What makes high-entropy alloys exceptional electrocatalysts? Angewandte Chemie. 2021;60(52):26894–26903. doi: 10.1002/anie.202109212
   Google Scholar
• Crawley JW, Gow IE, Lawes N, et al. Heterogeneous trimetallic nanoparticles as catalysts. Chem Rev. 2022;122(6):6795–6849. doi: 10.1021/acs.chemrev.1c00493
   PubMed Web of Science ®Google Scholar
• Yu L, Zeng K, Li C, et al. High-entropy alloy catalysts: from bulk to nano toward highly efficient carbon and nitrogen catalysis. Carbon Energy. 2022;4(5):731–761. doi: 10.1002/cey2.228
   Google Scholar
• Rossi LM, Fiorio JL, Garcia MA, et al. The role and fate of capping ligands in colloidally prepared metal nanoparticle catalysts. Dalton Trans. 2018;47(17):5889–5915. doi: 10.1039/C7DT04728B
   PubMed Web of Science ®Google Scholar
• Kusada K, Wu D, Kitagawa H. New aspects of platinum group metal-based solid-solution alloy nanoparticles: binary to high-entropy alloys. Chem A Eur J. 2020;26(23):5105–5130. doi: 10.1002/chem.201903928
   PubMed Web of Science ®Google Scholar
• Munnik P, de Jongh PE, de Jong KP. Recent developments in the synthesis of supported catalysts. Chem Rev. 2015;115(14):6687–6718. doi: 10.1021/cr500486u
   PubMed Web of Science ®Google Scholar
• Costa NJ, Rossi LM. Synthesis of supported metal nanoparticle catalysts using ligand assisted methods. Nanoscale. 2012;4(19):5826–5834. doi: 10.1039/c2nr31165h
   PubMed Web of Science ®Google Scholar
• Liu X, Khinast JG, Glasser BJ. A parametric investigation of impregnation and drying of supported catalysts. Chem Eng Sci. 2008;63(18):4517–4530. doi: 10.1016/j.ces.2008.06.013
   Web of Science ®Google Scholar
• Noorithaya AV, Bishop C, Sarkar P, et al. Drying of supported catalysts for high metal concentrations: a reduced parameter model. Chem Eng Sci. 2019;206:361–374. doi: 10.1016/j.ces.2019.05.014
   Web of Science ®Google Scholar
• Hunter JD. Matplotlib: a 2d graphics environment. Comput Sci Eng. 2007;9(3):90–95. doi: 10.1109/MCSE.2007.55
   Web of Science ®Google Scholar
• OriginLab Corporation. Northampton, MA, USA: OriginLab(Pro); 2023. Version 2023. https://www.originlab.com
   Google Scholar
• Nguyen TN, Nhat TTP, Takimoto K, et al. High-throughput experimentation and catalyst informatics for oxidative coupling of methane. ACS Catal. 2019;10(2):921–932. doi: 10.1021/acscatal.9b04293
   Web of Science ®Google Scholar
• Nguyen TN, Nakanowatari S, Nhat Tran TP, et al. Learning catalyst design based on bias-free data set for oxidative coupling of methane. ACS Catal. 2021;11(3):1797–1809. doi: 10.1021/acscatal.0c04629
   Web of Science ®Google Scholar
• Nguyen TN, Seenivasan K, Nakanowatari S, et al. Factors to influence low-temperature performance of supported Mn-Na2WO4 in oxidative coupling of methane. Mol Catal. 2021;516:111976. doi: 10.1016/j.mcat.2021.111976
   Google Scholar
• Vedyagin AA, Kenzhin RM, Tashlanov MY, et al. Synthesis and study of bimetallic Pd–Rh system supported on zirconia-doped alumina as a component of three-way catalysts. Emiss Control Sci Technol. 2019;5(4):363–377. doi: 10.1007/s40825-019-00133-2
   Google Scholar
• Tan Z, Haneda M, Kitagawa H, et al. Slow synthesis methodology-directed immiscible octahedral Pd_xRh_{1-x} dual-atom-site catalysts for superior three-way catalytic activities over Rh. Angew Chem. 2022;134(23):e202202588. doi: 10.1002/ange.202202588
   Google Scholar
• Haynes WM, Lide DR, Bruno T. Abundance of elements in the earth’s crust and in the sea. CRC Handb Chem Phys. 2016;97(2402):14–17.
   Google Scholar