Intelligent route to design efficient CO2 reduction electrocatalysts using ANFIS optimized by GA and PSO

Giuseppe Meazza
  • Abbasi, F. & Riaz, K. CO2 emissions and financial development in an emerging economy: An augmented VAR approach. Energy Policy 90, 102–114 (2016).

    Article 

    Google Scholar
     

  • Kayani, G. M., Ashfaq, S. & Siddique, A. Assessment of financial development on environmental effect: Implications for sustainable development. J. Clean. Prod. 261, 120984 (2020).

    Article 

    Google Scholar
     

  • Chen, A., Zhang, X., Chen, L., Yao, S. & Zhou, Z. A machine learning model on simple features for CO2 reduction electrocatalysts. J. Phys. Chem. C 124, 22471–22478 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Laursen, A. B. et al. CO2 electro-reduction on Cu3P: Role of Cu(I) oxidation state and surface facet structure in C1-formate production and H2 selectivity. Electrochim. Acta 391, 138889 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Hori, Y. Electrochemical CO2 reduction on metal electrodes. Mod. Asp. Electrochem. https://doi.org/10.1007/978-0-387-49489-0_3 (2008).

    Article 

    Google Scholar
     

  • Maxwell, I. E. Driving forces for innovation in applied catalysis. Stud. Surf. Sci. Catal. 101A, 1–9 (1996).


    Google Scholar
     

  • Lewis, N. S. & Nocera, D. G. Powering the planet: Chemical challenges in solar energy utilization. Proc. Natl. Acad. Sci. U.S.A. 103, 15729–15735 (2006).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baturina, O. A. et al. CO2 electroreduction to hydrocarbons on carbon-supported Cu nanoparticles. ACS Catal. 4, 3682–3695 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Liu, X., Wang, Z., Tian, Y. & Zhao, J. Graphdiyne-supported single iron atom: A promising electrocatalyst for carbon dioxide electroreduction into methane and ethanol. J. Phys. Chem. C 124, 3722–3730 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Cao, L. et al. Mechanistic insights for low-overpotential electroreduction of CO2 to CO on copper nanowires. ACS Catal. 7, 8578–8587 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Zhang, Q., Xu, W., Xu, J., Liu, Y. & Zhang, J. High performing and cost-effective metal/metal oxide/metal alloy catalysts/electrodes for low temperature CO2 electroreduction. Catal. Today 318, 15–22 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Zhong, M. et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 581, 178–183 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Guo, Y. et al. Machine-learning-guided discovery and optimization of additives in preparing Cu catalysts for CO2 reduction. J. Am. Chem. Soc. 143, 5755–5762 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nilsson, A., Pettersson, L. G. M. & Nørskov, J. K. Chemical bonding at surfaces and interfaces. Chem. Bond. Surf. Interfaces https://doi.org/10.1016/B978-0-444-52837-7.X5001-1 (2008).

    Article 

    Google Scholar
     

  • Nilsson, A. & Pettersson, L. G. M. Chemical bonding on surfaces probed by X-ray emission spectroscopy and density functional theory. Surf. Sci. Rep. 55, 49–167 (2004).

    Article 
    CAS 

    Google Scholar
     

  • Nilsson, A. et al. The electronic structure effect in heterogeneous catalysis. Catal. Lett. 100, 111–114 (2005).

    Article 
    CAS 

    Google Scholar
     

  • Van Santen, R. A. & Neurock, M. Molecular heterogeneous catalysis: A Conceptual and computational approach. Mol. Heterog. Catal. Concept. Comput. Approach https://doi.org/10.1002/9783527610846 (2007).

    Article 

    Google Scholar
     

  • Ertl, G. Reactions at solid surfaces. React. Solid Surf. https://doi.org/10.1002/9780470535295 (2010).

    Article 

    Google Scholar
     

  • Sabatier, P. Hydrogénations et déshydrogénations par catalyse. Ber. Dtsch. Chem. Gesellschaft 44, 1984–2001 (1911).

    Article 
    CAS 

    Google Scholar
     

  • Medford, A. J. et al. From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis. J. Catal. 328, 36–42 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Hammer, B. & Nørskov, J. K. Theoretical surface science and catalysis—calculations and concepts. Adv. Catal. 45, 71–129 (2000).

    CAS 

    Google Scholar
     

  • Nørskov, J. K., Abild-Pedersen, F., Studt, F. & Bligaard, T. Density functional theory in surface chemistry and catalysis. Proc. Natl. Acad. Sci. U.S.A. 108, 937–943 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • He, Y., Cubuk, E. D., Allendorf, M. D. & Reed, E. J. Metallic metal-organic frameworks predicted by the combination of machine learning methods and ab initio calculations. J. Phys. Chem. Lett. 9, 4562–4569 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Simón-Vidal, L. et al. Perturbation-theory and machine learning (PTML) model for high-throughput screening of parham reactions: Experimental and theoretical studies. J. Chem. Inf. Model. 58, 1384–1396 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Nørskov, J. K., Bligaard, T., Rossmeisl, J. & Christensen, C. H. Towards the computational design of solid catalysts. Nat. Chem. 1, 37–46 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Greeley, J. et al. Alloys of platinum and early transition metals as oxygen reduction electrocatalysts. Nat. Chem. 1, 552–556 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Back, S. & Jung, Y. Importance of ligand effects breaking the scaling relation for core-shell oxygen reduction catalysts. ChemCatChem 9, 3173–3179 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Back, S., Kim, H. & Jung, Y. Selective heterogeneous CO2 electroreduction to methanol. ACS Catal. 5, 965–971 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Stamenkovic, V. et al. Changing the activity of electrocatalysts for oxygen reduction by tuning the surface electronic structure. Angew. Chem. – Int. Ed. 45, 2897–2901 (2006).

    Article 
    CAS 

    Google Scholar
     

  • Gajdoš, M., Eichler, A. & Hafner, J. CO adsorption on close-packed transition and noble metal surfaces: Trends from ab initio calculations. J. Phys. Condens. Matter 16, 1141–1164 (2004).

    Article 

    Google Scholar
     

  • Xin, H., Vojvodic, A., Voss, J., Nørskov, J. K. & Abild-Pedersen, F. Effects of d-band shape on the surface reactivity of transition-metal alloys. Phys. Rev. B Condens. Matter Mater. Phys. 89, 115114 (2014).

    Article 

    Google Scholar
     

  • Vojvodic, A., Nørskov, J. K. & Abild-Pedersen, F. Electronic structure effects in transition metal surface chemistry. Top. Catal. 57, 25–32 (2014).

    Article 
    CAS 

    Google Scholar
     

  • Xin, H., Holewinski, A. & Linic, S. Predictive structurereactivity models for rapid screening of pt-based multimetallic electrocatalysts for the oxygen reduction reaction. ACS Catal. 2, 12–16 (2012).

    Article 
    CAS 

    Google Scholar
     

  • Gheytanzadeh, M., Baghban, A., Habibzadeh, S., Mohaddespour, A. & Abida, O. Insights into the estimation of capacitance for carbon-based supercapacitors. RSC Adv. 11, 5479–5486 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gheytanzadeh, M. et al. Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach. Sci. Rep. 11, 1–13 (2021).

    Article 

    Google Scholar
     

  • Ahmadi, M. H. et al. An insight into the prediction of TiO2/water nanofluid viscosity through intelligence schemes. J. Therm. Anal. Calorim. 139, 2381–2394 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Baghban, A., Bahadori, M., Lemraski, A. S. & Bahadori, A. Prediction of solubility of ammonia in liquid electrolytes using least square support vector machines. Ain Shams Eng. J. 9, 1303–1312 (2018).

    Article 

    Google Scholar
     

  • Baghban, A. & Khoshkharam, A. Application of LSSVM strategy to estimate asphaltene precipitation during different production processes. Pet. Sci. Technol. 34, 1855–1860 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Bahadori, A. et al. Computational intelligent strategies to predict energy conservation benefits in excess air controlled gas-fired systems. Appl. Therm. Eng. 102, 432–446 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Baghban, A., Abbasi, P. & Rostami, P. Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm. Pet. Sci. Technol. 34, 1698–1704 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Baghban, A. Application of the ANFIS strategy to estimate vaporization enthalpies of petroleum fractions and pure hydrocarbons. Pet. Sci. Technol. 34, 1359–1366 (2016).

    Article 
    CAS 

    Google Scholar
     

  • Ma, X., Li, Z., Achenie, L. E. K. & Xin, H. Machine-learning-augmented chemisorption model for CO2 electroreduction catalyst screening. J. Phys. Chem. Lett. 6, 3528–3533 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Li, Z., Ma, X. & Xin, H. Feature engineering of machine-learning chemisorption models for catalyst design. Catal. Today 280, 232–238 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Noh, J., Back, S., Kim, J. & Jung, Y. Active learning with non-: Ab initio input features toward efficient CO2 reduction catalysts. Chem. Sci. 9, 5152–5159 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Trivedi, R., Singh, T. N. & Gupta, N. Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS. Geotech. Geol. Eng. 33, 875–891 (2015).

    Article 

    Google Scholar
     

  • Nikafshan Rad, H., Jalali, Z. & Jalalifar, H. Prediction of rock mass rating system based on continuous functions using Chaos-ANFIS model. Int. J. Rock Mech. Min. Sci. 73, 1–9 (2015).

    Article 

    Google Scholar
     

  • Hasanipanah, M., Amnieh, H. B., Arab, H. & Zamzam, M. S. Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput. Appl. 30, 1015–1024 (2018).

    Article 

    Google Scholar
     

  • Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H. & Jianhua, Z. Forecasting copper prices using hybrid adaptive neuro-fuzzy inference system and genetic algorithms. Nat. Resour. Res. 28, 1385–1401 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Yang, H., Hasanipanah, M., Tahir, M. M. & Bui, D. T. Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat. Resour. Res. 29, 739–750 (2020).

    Article 

    Google Scholar
     

  • Moayedi, H., Raftari, M., Sharifi, A., Jusoh, W. A. W. & Rashid, A. S. A. Optimization of ANFIS with GA and PSO estimating α ratio in driven piles. Eng. Comput. 36, 227–238 (2020).

    Article 

    Google Scholar
     

  • Jang, J. S. R. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993).

    Article 

    Google Scholar
     

  • Armaghani, D. J., Momeni, E., Abad, S. V. A. N. K. & Khandelwal, M. Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ. Earth Sci. 74, 2845–2860 (2015).

    Article 

    Google Scholar
     

  • Thomas, S., Pillai, G. N., Pal, K. & Jagtap, P. Prediction of ground motion parameters using randomized ANFIS (RANFIS). Appl. Soft Comput. J. 40, 624–634 (2016).

    Article 

    Google Scholar
     

  • Shahnazar, A. et al. A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ. Earth Sci. 76, 1–17 (2017).

    Article 

    Google Scholar
     

  • Chen, X. & Wang, N. A DNA based genetic algorithm for parameter estimation in the hydrogenation reaction. Chem. Eng. J. 150, 527–535 (2009).

    Article 
    CAS 

    Google Scholar
     

  • Rezakazemi, M., Dashti, A., Asghari, M. & Shirazian, S. H2-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS. Int. J. Hydrog. Energy 42, 15211–15225 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Baghban, A., Mohammadi, A. H. & Taleghani, M. S. Rigorous modeling of CO2 equilibrium absorption in ionic liquids. Int. J. Greenh. Gas Control 58, 19–41 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Next Post

    Identifying the target audience and its information needs

    Image by Shunsuke Kobayashi released via Creative Commons CC BY 2.0 As set out in the first module in this series, “Strengthening a media business – the four essential steps”, the first step in setting up a media business is to identify the audience groups you plan to serve in […]