In Silico Design of Covalent Organic Framework-Based Electrocatalysts
Covalent organic frameworks (COFs) are an emerging type of porous crystalline material for efficient catalysis of the oxygen evolution reaction (OER). However, it remains a grand challenge to address the best candidates from thousands of possible COFs. Here, we report a methodology for the design of...
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| Vydáno v: | JACS Au Ročník 1; číslo 9; s. 1497 - 1505 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
American Chemical Society
27.09.2021
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| ISSN: | 2691-3704, 2691-3704 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Covalent organic frameworks (COFs) are an emerging type of porous crystalline material for efficient catalysis of the oxygen evolution reaction (OER). However, it remains a grand challenge to address the best candidates from thousands of possible COFs. Here, we report a methodology for the design of the best candidate screened from 100 virtual M-N x O y (M = 3d transition metal)-based model catalysts via density functional theory (DFT) and machine learning (ML). The intrinsic descriptors of OER activity of M-N x O y were addressed by the machine learning and used for predicting the best structure with OER performances. One of the predicted structures with a Ni-N2O2 unit is subsequently employed to synthesize the corresponding Ni-COF. X-ray absorption spectra characterizations, including XANES and EXAFS, validate the successful synthesis of the Ni-N2O2 coordination environment. The studies of electrocatalytic activities confirm that Ni-COF is comparable with the best reported COF-based OER catalysts. The current density reaches 10 mA cm-2 at a low overpotential of 335 mV. Furthermore, Ni-COF is stable for over 65 h during electrochemical testing. This work provides an accelerating strategy for the design of new porous crystalline-material-based electrocatalysts.Covalent organic frameworks (COFs) are an emerging type of porous crystalline material for efficient catalysis of the oxygen evolution reaction (OER). However, it remains a grand challenge to address the best candidates from thousands of possible COFs. Here, we report a methodology for the design of the best candidate screened from 100 virtual M-N x O y (M = 3d transition metal)-based model catalysts via density functional theory (DFT) and machine learning (ML). The intrinsic descriptors of OER activity of M-N x O y were addressed by the machine learning and used for predicting the best structure with OER performances. One of the predicted structures with a Ni-N2O2 unit is subsequently employed to synthesize the corresponding Ni-COF. X-ray absorption spectra characterizations, including XANES and EXAFS, validate the successful synthesis of the Ni-N2O2 coordination environment. The studies of electrocatalytic activities confirm that Ni-COF is comparable with the best reported COF-based OER catalysts. The current density reaches 10 mA cm-2 at a low overpotential of 335 mV. Furthermore, Ni-COF is stable for over 65 h during electrochemical testing. This work provides an accelerating strategy for the design of new porous crystalline-material-based electrocatalysts. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 2691-3704 2691-3704 |
| DOI: | 10.1021/jacsau.1c00258 |