The design and optimization of heterogeneous catalysts are critical for advancing green chemistry processes, aiming to minimize environmental impact while maximizing efficiency. This study explores innovative strategies for the development of heterogeneous catalysts that facilitate environmentally benign reactions. By integrating principles of catalyst design, surface science, and reaction engineering, we aim to enhance catalytic performance, selectivity, and stability. Catalyst composition and structure, investigating novel materials, including metal-organic frameworks (MOFs), zeolites, and supported metal nanoparticles, to identify optimal compositions and structures that promote desired reactions. Developing sustainable synthesis methods such as sol-gel processes, hydrothermal synthesis, and green solvent-based approaches to produce catalysts with high surface areas and active sites. Employing advanced characterization techniques like X-ray diffraction (XRD), scanning electron microscopy (SEM), and surface area analysis to understand catalyst morphology and active site distribution. Catalytic performance is evaluated through rigorous testing in various green chemistry reactions, including hydrogenation, oxidation, and carbon dioxide fixation. Utilizing computational tools and machine learning algorithms to predict catalyst behavior, optimize reaction conditions, and guide experimental efforts. Density functional theory (DFT) and molecular dynamics (MD) simulations provide insights into reaction mechanisms and energy profiles. Assessing the environmental footprint of catalyst production and application, ensuring the processes align with principles of sustainability and resource efficiency.

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1. Védrine JC. Heterogeneous catalysis on metal oxides. Catalysts. 2017;7(11):341.

2. Suvarna M, Araujo TP, Pérez-Ramírez J. A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO₂ hydrogenation. Appl Catal B Environ. 2022;315:121530.

3. Shambhawi S, Csányi G, Lapkin AA. Active learning training strategy for predicting O adsorption free energy on perovskite catalysts using inexpensive catalyst features. Chem Methods. 2021;1(10):444–450.

4. Gu GH, Noh J, Kim S, Back S, Ulissi Z, Jung Y. Practical deep-learning representation for fast heterogeneous catalyst screening. J Phys Chem Lett. 2020;11(9):3185–3191.

5. Chowdhury AJ, Yang W, Walker E, Mamun O, Heyden A, Terejanu GA. Prediction of adsorption energies for chemical species on metal catalyst surfaces using machine learning. J Phys Chem C. 2018;122(49):28142–28150.

6. Xu W, Yang B. Microkinetic modeling with machine learning predicted binding energies of reaction intermediates of ethanol steam reforming: The limitations. Mol Catal. 2023;537:112940.

7. Back S, Yoon J, Tian N, Zhong W, Tran K, Ulissi ZW. Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts. J Phys Chem Lett. 2019;10(15):4401–4408.

8. Studt F. Grand challenges in computational catalysis. Front Catal. 2021;1:658965.

9. Eisenstein O, Shaik S. Computational catalysis: A land of opportunities. Top Catal. 2022;65(1):1–5.

10. Vogiatzis KD, Polynski MV, Kirkland JK, Townsend J, Hashemi A, Liu C, Pidko EA. Computational approach to molecular catalysis by 3d transition metals: Challenges and opportunities. Chem Rev. 2018;119(4):2453–2523.

11. Tameh MS, Dearden AK, Huang C. Accuracy of density functional theory for predicting kinetics of methanol synthesis from CO and CO₂ hydrogenation on copper. J Phys Chem C. 2018;122(31):17942–17953.

12. Trinh QT, Bhola K, Amaniampong PN, Jerome F, Mushrif SH. Synergistic application of XPS and DFT to investigate metal oxide surface catalysis. J Phys Chem C. 2018;122(39):22397–22406.

13. Kapil J, Shukla P, Pathak A. Review article on density functional theory. In: Recent Trends in Materials and Devices: Proceedings of ICRTMD 2019. Singapore: Springer; 2020. p. 211–220.

14. Makkar P, Ghosh NN. A review on the use of DFT for the prediction of the properties of nanomaterials. RSC Adv. 2021;11(45):27897–27924.

15. Zhou Y, Tao X, Chen G, Lu R, Wang D, Chen MX, Jin E, Yang J, Liang HW, Zhao Y, Feng X. Multilayer stabilization for fabricating high-loading single-atom catalysts. Nat Commun. 2020;11(1):5892.

16. Spiegelman F, Tarrat N, Cuny J, Dontot L, Posenitskiy E, Martí C, Simon A, Rapacioli M. Density-functional tight-binding: Basic concepts and applications to molecules and clusters. Adv Phys X. 2020;5(1):1710252.

17. Wu L, Hu S, Yu W, Shen S, Li T. Stabilizing mechanism of single-atom catalysts on a defective carbon surface. npj Comput Mater. 2020;6(1):23.

The design and optimization of heterogeneous catalysts are critical for advancing green chemistry processes, aiming to minimize environmental impact while maximizing efficiency. This study explores innovative strategies for the development of heterogeneous catalysts that facilitate environmentally benign reactions. By integrating principles of catalyst design, surface science, and reaction engineering, we aim to enhance catalytic performance, selectivity, and stability. Catalyst composition and structure, investigating novel materials, including metal-organic frameworks (MOFs), zeolites, and supported metal nanoparticles, to identify optimal compositions and structures that promote desired reactions. Developing sustainable synthesis methods such as sol-gel processes, hydrothermal synthesis, and green solvent-based approaches to produce catalysts with high surface areas and active sites. Employing advanced characterization techniques like X-ray diffraction (XRD), scanning electron microscopy (SEM), and surface area analysis to understand catalyst morphology and active site distribution. Catalytic performance is evaluated through rigorous testing in various green chemistry reactions, including hydrogenation, oxidation, and carbon dioxide fixation. Utilizing computational tools and machine learning algorithms to predict catalyst behavior, optimize reaction conditions, and guide experimental efforts. Density functional theory (DFT) and molecular dynamics (MD) simulations provide insights into reaction mechanisms and energy profiles. Assessing the environmental footprint of catalyst production and application, ensuring the processes align with principles of sustainability and resource efficiency.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Védrine JC. Heterogeneous catalysis on metal oxides. Catalysts. 2017;7(11):341.

2. Suvarna M, Araujo TP, Pérez-Ramírez J. A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO₂ hydrogenation. Appl Catal B Environ. 2022;315:121530.

3. Shambhawi S, Csányi G, Lapkin AA. Active learning training strategy for predicting O adsorption free energy on perovskite catalysts using inexpensive catalyst features. Chem Methods. 2021;1(10):444–450.

4. Gu GH, Noh J, Kim S, Back S, Ulissi Z, Jung Y. Practical deep-learning representation for fast heterogeneous catalyst screening. J Phys Chem Lett. 2020;11(9):3185–3191.

5. Chowdhury AJ, Yang W, Walker E, Mamun O, Heyden A, Terejanu GA. Prediction of adsorption energies for chemical species on metal catalyst surfaces using machine learning. J Phys Chem C. 2018;122(49):28142–28150.

6. Xu W, Yang B. Microkinetic modeling with machine learning predicted binding energies of reaction intermediates of ethanol steam reforming: The limitations. Mol Catal. 2023;537:112940.

7. Back S, Yoon J, Tian N, Zhong W, Tran K, Ulissi ZW. Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts. J Phys Chem Lett. 2019;10(15):4401–4408.

8. Studt F. Grand challenges in computational catalysis. Front Catal. 2021;1:658965.

9. Eisenstein O, Shaik S. Computational catalysis: A land of opportunities. Top Catal. 2022;65(1):1–5.

10. Vogiatzis KD, Polynski MV, Kirkland JK, Townsend J, Hashemi A, Liu C, Pidko EA. Computational approach to molecular catalysis by 3d transition metals: Challenges and opportunities. Chem Rev. 2018;119(4):2453–2523.

11. Tameh MS, Dearden AK, Huang C. Accuracy of density functional theory for predicting kinetics of methanol synthesis from CO and CO₂ hydrogenation on copper. J Phys Chem C. 2018;122(31):17942–17953.

12. Trinh QT, Bhola K, Amaniampong PN, Jerome F, Mushrif SH. Synergistic application of XPS and DFT to investigate metal oxide surface catalysis. J Phys Chem C. 2018;122(39):22397–22406.

13. Kapil J, Shukla P, Pathak A. Review article on density functional theory. In: Recent Trends in Materials and Devices: Proceedings of ICRTMD 2019. Singapore: Springer; 2020. p. 211–220.

14. Makkar P, Ghosh NN. A review on the use of DFT for the prediction of the properties of nanomaterials. RSC Adv. 2021;11(45):27897–27924.

15. Zhou Y, Tao X, Chen G, Lu R, Wang D, Chen MX, Jin E, Yang J, Liang HW, Zhao Y, Feng X. Multilayer stabilization for fabricating high-loading single-atom catalysts. Nat Commun. 2020;11(1):5892.

16. Spiegelman F, Tarrat N, Cuny J, Dontot L, Posenitskiy E, Martí C, Simon A, Rapacioli M. Density-functional tight-binding: Basic concepts and applications to molecules and clusters. Adv Phys X. 2020;5(1):1710252.

17. Wu L, Hu S, Yu W, Shen S, Li T. Stabilizing mechanism of single-atom catalysts on a defective carbon surface. npj Comput Mater. 2020;6(1):23.