Journal Home > Volume 16 , Issue 1
Nano Research 2023, 16(1): 264-280 https://doi.org/10.1007/s12274-022-4763-y
Research Article | Issue | Published: 31 August 2022

Inspecting design rules of metal-nitrogen-carbon catalysts for electrochemical CO2 reduction reaction: From a data science perspective

Show Author's Information Rui Ding1,  Meng Ma1,  Yawen Chen1,  Xuebin Wang1,  Jia Li1( ),  Guoxiong Wang2( ),  Jianguo Liu1( )
National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Nanjing University, 22 Hankou Road, Nanjing 210093, China
State Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
Keywords:
artificial intelligence, machine learning, electrochemical CO2 reduction reaction, data science, metal-nitrogen-carbon catalyst
Topics:
Catalysis
Cite this article:
Ding R, Ma M, Chen Y, et al. Inspecting design rules of metal-nitrogen-carbon catalysts for electrochemical CO2 reduction reaction: From a data science perspective. Nano Research, 2023, 16(1): 264-280. https://doi.org/10.1007/s12274-022-4763-y
Download citation
EndNote(RIS)
BibTeX

761

Views

53

Downloads

Citations

10

Crossref

7

WoS

9

Scopus

0

CSCD

Abstract Full text Electronic supplementary material About this article

The development of inexpensive metal-nitrogen-carbon (M-N-C) catalysts for electrochemical CO2 reduction reaction (CO2RR) on an industrial scale has come to a standstill. Although the number of related studies and reviews has grown fast, the complexity of the M-N-C composite has limited researchers to focus on only a few variables and carry out sluggish trial-and-error optimizations in their studies. As a result, the conclusions are drawn only by artificial analysis based on a few orthogonal experimental results. To obtain more general design strategies, we have innovatively introduced machine learning (ML) into this field to address this bottleneck. A standard workflow that comprehensively utilizes different ML algorithms and black-box interpretation methods is proposed for this purpose. Besides predicting CO2RR performance metrics for M-N-C catalysts, such as maximum faradaic efficiency with great accuracy, the ML models have also indicated simple and clear design strategies that would guide future exploration from a data science perspective. Besides, we have also demonstrated the potential of the models in guiding the development of new material systems. We thereby believe that the new research paradigm proposed may accelerate the development of this field soon.


menu
Abstract
Full text
Outline
Electronic supplementary material
About this article

Inspecting design rules of metal-nitrogen-carbon catalysts for electrochemical CO2 reduction reaction: From a data science perspective

Show Author's information Rui Ding1, Meng Ma1, Yawen Chen1, Xuebin Wang1, Jia Li1( ), Guoxiong Wang2( ), Jianguo Liu1( )
National Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Nanjing University, 22 Hankou Road, Nanjing 210093, China
State Key Laboratory of Catalysis, Dalian National Laboratory for Clean Energy, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China

Abstract

The development of inexpensive metal-nitrogen-carbon (M-N-C) catalysts for electrochemical CO2 reduction reaction (CO2RR) on an industrial scale has come to a standstill. Although the number of related studies and reviews has grown fast, the complexity of the M-N-C composite has limited researchers to focus on only a few variables and carry out sluggish trial-and-error optimizations in their studies. As a result, the conclusions are drawn only by artificial analysis based on a few orthogonal experimental results. To obtain more general design strategies, we have innovatively introduced machine learning (ML) into this field to address this bottleneck. A standard workflow that comprehensively utilizes different ML algorithms and black-box interpretation methods is proposed for this purpose. Besides predicting CO2RR performance metrics for M-N-C catalysts, such as maximum faradaic efficiency with great accuracy, the ML models have also indicated simple and clear design strategies that would guide future exploration from a data science perspective. Besides, we have also demonstrated the potential of the models in guiding the development of new material systems. We thereby believe that the new research paradigm proposed may accelerate the development of this field soon.

Keywords: artificial intelligence, machine learning, electrochemical CO2 reduction reaction, data science, metal-nitrogen-carbon catalyst

References(76)

[1]

Ahmad, T.; Liu, S.; Sajid, M.; Li, K. Ali, M.; Liu, L.; Chen, W. Electrochemical CO2 reduction to C2+ products using Cu-based electrocatalysts: A review. Nano Res. Energy 2022, 1: e9120021.
DOI Google Scholar
[2]
ZhangS. L. SunL. Fan Q. N.Zhang F. L.WangZ. J.Zou J. S.Zhao S. Y.MaoJ. F. GuoZ. P. Challenges and prospects of lithium−CO2 batteriesNano Res. Energy20221: e912000110.26599/NRE.2022.9120001

Zhang, S. L.; Sun, L.; Fan, Q. N.; Zhang, F. L.; Wang, Z. J.; Zou, J. S.; Zhao, S. Y.; Mao, J. F.; Guo, Z. P. Challenges and prospects of lithium−CO2 batteries. Nano Res. Energy 2022, 1: e9120001.
DOI Google Scholar
[3]

Xie, H.; Wang, T. Y.; Liang, J. S.; Li, Q.; Sun, S. H. Cu-based nanocatalysts for electrochemical reduction of CO2. Nano Today 2018, 21, 41–54.
DOI Google Scholar
[4]

Li, M. H.; Wang, H. F.; Luo, W.; Sherrell, P. C.; Chen, J.; Yang, J. P. Heterogeneous single-atom catalysts for electrochemical CO2 reduction reaction. Adv. Mater. 2020, 32, 2001848.
DOI Google Scholar
[5]

Wu, Z. Z.; Gao, F. Y.; Gao, M. R. Regulating the oxidation state of nanomaterials for electrocatalytic CO2 reduction. Energy Environ. Sci. 2021, 14, 1121–1139.
DOI Google Scholar
[6]

Ju, W.; Bagger, A.; Hao, G. P.; Varela, A. S.; Sinev, I.; Bon, V.; Roldan Cuenya, B.; Kaskel, S.; Rossmeisl, J.; Strasser, P. Understanding activity and selectivity of metal-nitrogen-doped carbon catalysts for electrochemical reduction of CO2. Nat. Commun. 2017, 8, 944.
DOI Google Scholar
[7]

Fan, Q.; Hou, P. F.; Choi, C.; Wu, T. S.; Hong, S.; Li, F.; Soo, Y. L.; Kang, P.; Jung, Y.; Sun, Z. Y. Activation of Ni particles into single Ni-N atoms for efficient electrochemical reduction of CO2. Adv. Energy Mater. 2020, 10, 1903068.
DOI Google Scholar
[8]

Zhang, C.; Fu, Z. H.; Zhao, Q.; Du, Z. G.; Zhang, R. F.; Li, S. M. Single-atom-Ni-decorated, nitrogen-doped carbon layers for efficient electrocatalytic CO2 reduction reaction. Electrochem. Commun. 2020, 116, 106758.
DOI Google Scholar
[9]

Zhao, C. M.; Wang, Y.; Li, Z. J.; Chen, W. X.; Xu, Q.; He, D. S.; Xi, D. S.; Zhang, Q. H.; Yuan, T. W.; Qu, Y. T. et al. Solid-diffusion synthesis of single-atom catalysts directly from bulk metal for efficient CO2 reduction. Joule 2019, 3, 584–594.
DOI Google Scholar
[10]

Jiang, K.; Siahrostami, S.; Zheng, T. T.; Hu, Y. F.; Hwang, S.; Stavitski, E.; Peng, Y. D.; Dynes, J.; Gangisetty, M.; Su, D.; Attenkofer, K.; Wang, H. T. Isolated Ni single atoms in graphene nanosheets for high-performance CO2 reduction. Energy Environ. Sci. 2018, 11, 893–903.
DOI Google Scholar
[11]

Yan, C. C.; Li, H. B.; Ye, Y. F.; Wu, H. H.; Cai, F.; Si, R.; Xiao, J. P.; Miao, S.; Xie, S. H.; Yang, F. et al. Coordinatively unsaturated nickel-nitrogen sites towards selective and high-rate CO2 electroreduction. Energy Environ. Sci. 2018, 11, 1204–1210.
DOI Google Scholar
[12]

Lin, L.; Li, H. B.; Yan, C. C.; Li, H. F.; Si, R.; Li, M. R.; Xiao, J. P.; Wang, G. X.; Bao, X. H. Synergistic catalysis over iron-nitrogen sites anchored with cobalt phthalocyanine for efficient CO2 electroreduction. Adv. Mater. 2019, 31, 1903470.
DOI Google Scholar
[13]

Lin, L.; Li, H. B.; Wang, Y.; Li, H. F.; Wei, P. F.; Nan, B.; Si, R.; Wang, G. X.; Bao, X. H. Temperature-dependent CO2 electroreduction over Fe-N-C and Ni-N-C single-atom catalysts. Angew. Chem., Int. Ed. 2021, 60, 26582–26586.
DOI Google Scholar
[14]

Qu, Q. Y.; Ji, S. F.; Chen, Y. J.; Wang, D. S.; Li, Y. D. The atomic-level regulation of single-atom site catalysts for the electrochemical CO2 reduction reaction. Chem. Sci. 2021, 12, 4201–4215.
DOI Google Scholar
[15]

Han, S. G.; Ma, D. D.; Zhu, Q. L. Atomically structural regulations of carbon-based single-atom catalysts for electrochemical CO2 reduction. Small Methods 2021, 5, 2100102.
DOI Google Scholar
[16]

Zhang, J. C.; Cai, W. Z.; Hu, F. X.; Yang, H. B.; Liu, B. Recent advances in single atom catalysts for the electrochemical carbon dioxide reduction reaction. Chem. Sci. 2021, 12, 6800–6819.
DOI Google Scholar
[17]

Gao, D. F.; Liu, T. F.; Wang, G. X.; Bao, X. H. Structure sensitivity in single-atom catalysis toward CO2 electroreduction. ACS Energy Lett. 2021, 6, 713–727.
DOI Google Scholar
[18]

Attia, P. M.; Grover, A.; Jin, N.; Severson, K. A.; Markov, T. M.; Liao, Y. H.; Chen, M. H.; Cheong, B.; Perkins, N.; Yang, Z. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 2020, 578, 397–402.
DOI Google Scholar
[19]

Zhang, Y. W.; Tang, Q. C.; Zhang, Y.; Wang, J. B.; Stimming, U.; Lee, A. A. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning. Nat. Commun. 2020, 11, 1706.
DOI Google Scholar
[20]

Ding, R.; Wang, R.; Ding, Y. Q.; Yin, W. J.; Liu, Y. D.; Li, J.; Liu, J. G. Designing AI-aided analysis and prediction models for nonprecious metal electrocatalyst-based proton-exchange membrane fuel cells. Angew. Chem., Int. Ed. 2020, 59, 19175–19183.
DOI Google Scholar
[21]

Liu, Y.; Wang, X.; Zhao, Y. J.; Wu, Q. Y.; Nie, H. D.; Si, H. L.; Huang, H.; Liu, Y.; Shao, M. W.; Kang, Z. H. Highly efficient metal-free catalyst from cellulose for hydrogen peroxide photoproduction instructed by machine learning and transient photovoltage technology. Nano Res. 2022, 15, 4000–4007.
DOI Google Scholar
[22]

Ding, R.; Yin, W. J.; Cheng, G.; Chen, Y. W.; Wang, J. K.; Wang, R.; Rui, Z. Y.; Li, J.; Liu, J. G. Boosting the optimization of membrane electrode assembly in proton exchange membrane fuel cells guided by explainable artificial intelligence. Energy AI 2021, 5, 100098.
DOI Google Scholar
[23]

Ding, R.; Chen, Y. W.; Chen, P.; Wang, R.; Wang, J. K.; Ding, Y. Q.; Yin, W. J.; Liu, Y. D.; Li, J.; Liu, J. G. Machine learning-guided discovery of underlying decisive factors and new mechanisms for the design of nonprecious metal electrocatalysts. ACS Catal. 2021, 11, 9798–9808.
DOI Google Scholar
[24]

Palkovits, R.; Palkovits, S. Using artificial intelligence to forecast water oxidation catalysts. ACS Catal. 2019, 9, 8383–8387.
DOI Google Scholar
[25]

Malek, A.; Wang, Q. P.; Baumann, S.; Guillon, O.; Eikerling, M.; Malek, K. A data-driven framework for the accelerated discovery of CO2 reduction electrocatalysts. Front. Energy Res. 2021, 9, 609070.
DOI Google Scholar
[26]

Wu, D. H.; Zhang, J. Y.; Cheng, M. J.; Lu, Q.; Zhang, H. C. Machine learning investigation of supplementary adsorbate influence on copper for enhanced electrochemical CO2 reduction performance. J. Phys. Chem. C 2021, 125, 15363–15372.
DOI Google Scholar
[27]

Wan, X. H.; Zhang, Z. F.; Niu, H.; Yin, Y. H.; Kuai, C. G.; Wang, J.; Shao, C.; Guo, Y. Z. Machine-learning-accelerated catalytic activity predictions of transition metal phthalocyanine dual-metal-site catalysts for CO2 reduction. J. Phys. Chem. Lett. 2021, 12, 6111–6118.
DOI Google Scholar
[28]

Guo, Y.; He, X. R.; Su, Y. M.; Dai, Y. H.; Xie, M. C.; Yang, S. L.; Chen, J. W.; Wang, K.; Zhou, D.; Wang, C. Machine-learning-guided discovery and optimization of additives in preparing Cu catalysts for CO2 reduction. J. Am. Chem. Soc. 2021, 143, 5755–5762.
DOI Google Scholar
[29]

Reddy, G. T.; Reddy, M. P. K.; Lakshmanna, K.; Kaluri, R.; Rajput, D. S.; Srivastava, G.; Baker, T. Analysis of dimensionality reduction techniques on big data. IEEE Access 2020, 8, 54776–54788.
DOI Google Scholar
[30]

Friedman, J. H.; Popescu, B. E. Predictive learning via rule ensembles. Ann. Appl. Stat. 2008, 2, 916–954.
DOI Google Scholar
[31]
Greenwell, B. M. pdp: An R package for constructing partial dependence plots. R J 2017, 9, 421–436.
DOI
[32]

Gu, Z. D.; Zhou, S. M.; Liu, J. F.; Zhou, Q. R.; Wang, D. J. Shapley distance and shapley index for some special graphs. Parallel Process. Lett. 2020, 30, 2050012.
DOI Google Scholar
[33]

Biecek, P. DALEX:Explainers for complex predictive models in R. J. Mach. Learn. Res. 2018, 19, 3245–3249.
Google Scholar
[34]

Yu, F. B.; Wei, C. H.; Deng, P.; Peng, T.; Hu, X. G. Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles. Sci. Adv. 2021, 7, eabf4130.
DOI Google Scholar
[35]

Wang, W. J.; Cao, C. S.; Wang, K. W.; Zhou, T. H. Boosting CO2 electroreduction to CO with abundant nickel single atom active sites. Inorg. Chem. Front. 2021, 8, 2542–2548.
DOI Google Scholar
[36]

Wu, Q.; Liang, J.; Xie, Z. L.; Huang, Y. B.; Cao, R. Spatial sites separation strategy to fabricate atomically isolated nickel catalysts for efficient CO2 electroreduction. ACS Materials Lett. 2021, 3, 454–461.
DOI Google Scholar
[37]

Li, Z. D.; He, D.; Yan, X. X.; Dai, S.; Younan, S.; Ke, Z. J.; Pan, X. Q.; Xiao, X. H.; Wu, H. J.; Gu, J. Size-dependent nickel-based electrocatalysts for selective CO2 reduction. Angew. Chem., Int. Ed. 2020, 59, 18572–18577.
DOI Google Scholar
[38]

Ding, R.; Ding, Y. Q.; Zhang, H. Y.; Wang, R.; Xu, Z. H.; Liu, Y. D.; Yin, W. J.; Wang, J. K.; Li, J.; Liu, J. G. Applying machine learning to boost the development of high-performance membrane electrode assembly for proton exchange membrane fuel cells. J. Mater. Chem. A 2021, 9, 6841–6850.
DOI Google Scholar
[39]
Chen, C. F.; Li, O.; Tao, C. F.; Barnett, A. J.; Rudin, C.; Su, J. This looks like that: Deep learning for interpretable image recognition. In 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019.
[40]

Singh, G.; Yow, K. C. These do not look like those: An interpretable deep learning model for image recognition. IEEE Access 2021, 9, 41482–41493.
DOI Google Scholar
[41]

Hu, X. M.; Hval, H. H.; Bjerglund, E. T.; Dalgaard, K. J.; Madsen, M. R.; Pohl, M. M.; Welter, E.; Lamagni, P.; Buhl, K. B.; Bremholm, M. et al. Selective CO2 reduction to CO in water using earth-abundant metal and nitrogen-doped carbon electrocatalysts. ACS Catal. 2018, 8, 6255–6264.
DOI Google Scholar
[42]

Ma, Z. X.; Niu, L. J.; Jiang, W. S.; Dong, C. X.; Liu, G. H.; Qu, D.; An, L.; Sun, Z. C. Recent advances of single-atom electrocatalysts for hydrogen evolution reaction. J. Phys. Mater. 2021, 4, 042002.
DOI Google Scholar
[43]

Menisa, L. T.; Cheng, P.; Long, C.; Qiu, X. Y.; Zheng, Y. L.; Han, J. Y.; Zhang, Y.; Gao, Y.; Tang, Z. Y. Insight into atomically dispersed porous M-N-C single-site catalysts for electrochemical CO2 reduction. Nanoscale 2020, 12, 16617–16626.
DOI Google Scholar
[44]

Liang, S. Y.; Huang, L.; Gao, Y. S.; Wang, Q.; Liu, B. Electrochemical reduction of CO2 to CO over transition metal/N-doped carbon catalysts: The active sites and reaction mechanism. Adv Sci (Weinh) 2021, 8, 2102886.
DOI Google Scholar
[45]

Jiao, L.; Yang, W. J.; Wan, G.; Zhang, R.; Zheng, X. S.; Zhou, H.; Yu, S. H.; Jiang, H. L. Single-atom electrocatalysts from multivariate metal-organic frameworks for highly selective reduction of CO2 at low pressures. Angew. Chem., Int. Ed. 2020, 59, 20589–20595.
DOI Google Scholar
[46]

Li, J. K.; Pršlja, P.; Shinagawa, T.; Martín Fernández, A. J.; Krumeich, F.; Artyushkova, K.; Atanassov, P.; Zitolo, A.; Zhou, Y. C.; García-Muelas, R. et al. Volcano trend in electrocatalytic CO2 reduction activity over atomically dispersed metal sites on nitrogen-doped carbon. ACS Catal. 2019, 9, 10426–10439.
DOI Google Scholar
[47]

Yang, H. P.; Lin, Q.; Zhang, C.; Yu, X. Y.; Cheng, Z.; Li, G. D.; Hu, Q.; Ren, X. Z.; Zhang, Q. L.; Liu, J. et al. Carbon dioxide electroreduction on single-atom nickel decorated carbon membranes with industry compatible current densities. Nat. Commun. 2020, 11, 593.
DOI Google Scholar
[48]

Cheng, H. Y.; Wu, X. M.; Li, X. C.; Nie, X. W.; Fan, S.; Feng, M. M.; Fan, Z. H.; Tan, M. Q.; Chen, Y. G.; He, G. H. Construction of atomically dispersed Cu-N4 sites via engineered coordination environment for high-efficient CO2 electroreduction. Chem. Eng. J. 2021, 407, 126842.
DOI Google Scholar
[49]

Yang, H. P.; Lin, Q.; Wu, Y.; Li, G. D.; Hu, Q.; Chai, X. Y.; Ren, X. Z.; Zhang, Q. L.; Liu, J. H.; He, C. X. Highly efficient utilization of single atoms via constructing 3D and free-standing electrodes for CO2 reduction with ultrahigh current density. Nano Energy 2020, 70, 104454.
DOI Google Scholar
[50]

Zhang, Y.; Jiao, L.; Yang, W. J.; Xie, C. F.; Jiang, H. L. Rational fabrication of low-coordinate single-atom Ni electrocatalysts by MOFs for highly selective CO2 reduction. Angew. Chem., Int. Ed. 2021, 60, 7607–7611.
DOI Google Scholar
[51]

Chen, H. H.; Guo, X.; Kong, X. D.; Xing, Y. L.; Liu, Y.; Yu, B. L.; Li, Q. X.; Geng, Z. G.; Si, R.; Zeng, J. Tuning the coordination number of Fe single atoms for the efficient reduction of CO2. Green Chem. 2020, 22, 7529–7536.
DOI Google Scholar
[52]

Hu, C.; Mu, Y.; Bai, S. L.; Yang, J.; Gao, L. J.; Cheng, S. D.; Mi, S. B.; Qiu, J. S. Polyvinyl pyrrolidone mediated fabrication of Fe, N-codoped porous carbon sheets for efficient electrocatalytic CO2 reduction. Carbon 2019, 153, 609–616.
DOI Google Scholar
[53]

Zhao, S. Y.; Wang, T. S.; Zhou, G. M.; Zhang, L. J.; Lin, C.; Veder, J. P.; Johannessen, B.; Saunders, M.; Yin, L. C.; Liu, C. et al. Controlled one-pot synthesis of nickel single atoms embedded in carbon nanotube and graphene supports with high loading. ChemNanoMat 2020, 6, 1063–1074.
DOI Google Scholar
[54]

Shen, S. J.; Han, C.; Wang, B.; Du, Y. A.; Wang, Y. D. Dual active sites-dependent syngas proportions from aqueous CO2 electroreduction. Appl. Catal. B Environ. 2020, 279, 119380.
DOI Google Scholar
[55]
Miao, Z. C.; Meng, J.; Liang, M. F.; Li, Z. B.; Zhao, Y. Z.; Wang, F. Y.; Xu, L. L.; Mu, J. L.; Zhuo, S. P.; Zhou, J. In-situ CVD synthesis of Ni@N-CNTs/carbon paper electrode for electro-reduction of CO2. Carbon 2021, 172, 324–333.
DOI
[56]

Zhu, W. L.; Fu, J. J.; Liu, J.; Chen, Y.; Li, X.; Huang, K. K.; Cai, Y. M.; He, Y. M.; Zhou, Y.; Su, D. et al. Tuning single atom-nanoparticle ratios of Ni-based catalysts for synthesis gas production from CO2. Appl. Catal. B Environ. 2020, 264, 118502.
DOI Google Scholar
[57]

Zhang, T. Y.; Han, X.; Yang, H. B.; Han, A. J.; Hu, E. Y.; Li, Y. P.; Yang, X. Q.; Wang, L.; Liu, J. F.; Liu, B. Atomically dispersed nickel(I) on an alloy-encapsulated nitrogen-doped carbon nanotube array for high-performance electrochemical CO2 reduction reaction. Angew. Chem., Int. Ed. 2020, 59, 12055–12061.
DOI Google Scholar
[58]

He, Y.; Li, Y. X.; Zhang, J. F.; Wang, S. Y.; Huang, D. K.; Yang, G. L.; Yi, X. L.; Lin, H. W.; Han, X. P.; Hu, W. B. et al. Low-temperature strategy toward Ni-NC@Ni core-shell nanostructure with Single-Ni sites for efficient CO2 electroreduction. Nano Energy 2020, 77, 105010.
DOI Google Scholar
[59]

Gong, Y. N.; Jiao, L.; Qian, Y. Y.; Pan, C. Y.; Zheng, L. R.; Cai, X. C.; Liu, B.; Yu, S. H.; Jiang, H. L. Regulating the coordination environment of MOF-templated single-atom nickel electrocatalysts for boosting CO2 reduction. Angew. Chem., Int. Ed. 2020, 59, 2705–2709.
DOI Google Scholar
[60]

Zhang, H. N.; Li, J.; Xi, S. B.; Du, Y. H.; Hai, X.; Wang, J. Y.; Xu, H. M.; Wu, G.; Zhang, J.; Lu, J. et al. A graphene-supported single-atom FeN5 catalytic site for efficient electrochemical CO2 reduction. Angew. Chem., Int. Ed. 2019, 58, 14871–14876.
DOI Google Scholar
[61]

Jin, S.; Ni, Y. X.; Hao, Z. M.; Zhang, K.; Lu, Y.; Yan, Z. H.; Wei, Y. J.; Lu, Y. R.; Chan, T. S.; Chen, J. A universal graphene quantum dot tethering design strategy to synthesize single-atom catalysts. Angew. Chem., Int. Ed. 2020, 59, 21885–21889.
DOI Google Scholar
[62]

Wang, X. Y.; Wang, Y.; Sang, X. H.; Zheng, W. Z.; Zhang, S.; Shuai, L.; Yang, B.; Li, Z. J.; Chen, J. M.; Lei, L. C. et al. Dynamic activation of adsorbed intermediates via axial traction for the promoted electrochemical CO2 reduction. Angew. Chem., Int. Ed. 2021, 60, 4192–4198.
DOI Google Scholar
[63]

Chen, Z. C.; Huang, A. J.; Yu, K.; Cui, T. T.; Zhuang, Z. W.; Liu, S. J.; Li, J. Z.; Tu, R. Y.; Sun, K. A.; Tan, X. et al. Fe1N4-O1 site with axial Fe-O coordination for highly selective CO2 reduction over a wide potential range. Energy Environ. Sci. 2021, 14, 3430–3437.
DOI Google Scholar
[64]

He, C.; Zhang, Y.; Zhang, Y.; Zhao, L.; Yuan, L. P.; Zhang, J. N.; Ma, J. M.; Hu, J. S. Molecular evidence for metallic cobalt boosting CO2 electroreduction on pyridinic nitrogen. Angew. Chem., Int. Ed. 2020, 59, 4914–4919.
DOI Google Scholar
[65]

Guo, Y.; Yang, H. J.; Zhou, X.; Liu, K. L.; Zhang, C.; Zhou, Z. Y.; Wang, C.; Lin, W. B. Electrocatalytic reduction of CO2to CO with 100% faradaic efficiency by using pyrolyzed zeolitic imidazolate frameworks supported on carbon nanotube networks. J. Mater. Chem. A 2017, 5, 24867–24873.
DOI Google Scholar
[66]

Wang, X. P.; Ding, S. S.; Yue, T.; Zhu, Y.; Fang, M. W.; Li, X. Y.; Xiao, G. Z.; Zhu, Y.; Dai, L. M. Universal domino reaction strategy for mass production of single-atom metal-nitrogen catalysts for boosting CO2 electroreduction. Nano Energy 2021, 82, 105689.
DOI Google Scholar
[67]

Gu, J.; Hsu, C. S.; Bai, L. C.; Chen, H. M.; Hu, X. L. Atomically dispersed Fe3+ sites catalyze efficient CO2 electroreduction to CO. Science 2019, 364, 1091–1094.
DOI Google Scholar
[68]

Wang, X. W.; Wu, D.; Dai, C. Z.; Xu, C. Y.; Sui, P.; Feng, R. F.; Wei, Y. P.; Fu, X. Z.; Luo, J. L. Novel folic acid complex derived nitrogen and nickel co-doped carbon nanotubes with embedded Ni nanoparticles as efficient electrocatalysts for CO2 reduction. J. Mater. Chem. A 2020, 8, 5105–5114.
DOI Google Scholar
[69]

Li, L. L.; Hasan, I.; Farwa; He, R. N. Peng, L. W.; Xu, N. N.; Niazi, N. K.; Zhang, J. N.; Qiao, J. L. Copper as a single metal atom based photo-, electro- and photoelectrochemical catalyst decorated on carbon nitride surface for efficient CO2 reduction: A review. Nano Res. Energy 2022, 1: e9120015.
DOI Google Scholar
[70]

Zhu, S.; Wan, K. W.; Wang, H.; Guo, L. J.; Shi, X. H. The role of supported dual-atom on graphitic carbon nitride for selective and efficient CO2 electrochemical reduction. Nanotechnology 2021, 32, 385404.
DOI Google Scholar
[71]
Cui, X. D.; An, W.; Liu, X. Y.; Wang, H.; Men, Y.; Wang, J. G. C2N-graphene supported single-atom catalysts for CO2 electrochemical reduction reaction: Mechanistic insight and catalyst screening. Nanoscale 2018, 10, 15262–15272.
DOI
[72]

Lambie, S.; Low, J. L.; Gaston, N.; Paulus, B. Catalytic potential of post-transition metal doped graphene-based single-atom catalysts for the CO2 electroreduction reaction. ChemPhysChem 2022, 23, e202200024.
DOI Google Scholar
[73]

Copelli, M.; Eichhorn, R.; Kinouchi, O.; Biehl, M.; Simonetti, R.; Riegler, P.; Caticha, N. Noise robustness in multilayer neural networks. Europhys. Lett. 1997, 37, 427–432.
DOI Google Scholar
[74]

Takenouchi, T.; Eguchi, S.; Murata, N.; Kanamori, T. Robust boosting algorithm against mislabeling in multiclass problems. Neural Comput. 2008, 20, 1596–1630.
DOI Google Scholar
[75]

Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V. et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 2011, 12, 2825–2830.
Google Scholar
[76]

Martín, A. J.; Larrazábal, G. O.; Pérez-Ramírez, J. Towards sustainable fuels and chemicals through the electrochemical reduction of CO2: Lessons from water electrolysis. Green Chem. 2015, 17, 5114–5130.
DOI Google Scholar
File
12274_2022_4763_MOESM1_ESM.pdf (74.5 MB)
Publication history
Copyright
Acknowledgements

Publication history

Received: 17 May 2022
Revised: 26 June 2022
Accepted: 11 July 2022
Published: 31 August 2022
Issue date: January 2023

Copyright

© Tsinghua University Press 2022

Acknowledgements

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Nos. 22125205 and 92045302). Part of the computational work is done in the Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.

Return