Mechanistic understanding and design of non-noble metal-based single-atom catalysts supported on two-dimensional materials for CO₂ electroreduction

Abstract

Single-atom catalysts (SACs) composing of low-cost, earth-abundant metals, with two-dimensional material supports have displayed great potential in a wide range of electrochemical reactions, including CO2 reduction reaction (CO2RR) to convert...

Advances in studies of the structural effects of supported Ni catalysts for CO2hydrogenation: from nanoparticle to single atom catalyst

“…Ni clusters are needed for the full hydrogenation of CO 2 with the 8e − redox cycle. 35 The unique characteristics of the SAC render Ni-SAC active for electro-chemical CO 2 reduction to CO. 154–157 Frei and his co-workers 35 confirmed that the MgO supported Ni-SAC is active for RWGS. MgO supported Ni NPs are normally highly active for CO 2 methanation.…”

Machine learning for design principles for single atom catalysts towards electrochemical reactions

“…In addition, structure-activity relationships were established for predicting CO and H adsorption energies based on structural properties using active learning across reaction intermediates. 104,105 In fact, an automated screening approach through the integration and optimization of ML was presented to guide DFT calculations for predicting catalytic activity. 105 The feasibility of this approach was demonstrated by screening various alloys combining 31 elements, which resulted in 131 candidate surfaces across 54 alloys being identied for the CO 2 RR and identication of 258 surfaces across 102 alloys for the HER.…”

“…104,105 In fact, an automated screening approach through the integration and optimization of ML was presented to guide DFT calculations for predicting catalytic activity. 105 The feasibility of this approach was demonstrated by screening various alloys combining 31 elements, which resulted in 131 candidate surfaces across 54 alloys being identied for the CO 2 RR and identication of 258 surfaces across 102 alloys for the HER. 104,105 Likewise, active learning was then used to accelerate the screening of CO adsorption energy on Cu based components.…”

“…105 The feasibility of this approach was demonstrated by screening various alloys combining 31 elements, which resulted in 131 candidate surfaces across 54 alloys being identied for the CO 2 RR and identication of 258 surfaces across 102 alloys for the HER. 104,105 Likewise, active learning was then used to accelerate the screening of CO adsorption energy on Cu based components. 106 The ML-predicted adsorption energies of reaction intermediates were also used for the investigation and optimization of the reaction network of the syngas reaction (CO + H 2 ) over Rh (111) catalysts at 573 K and 1 atm.…”

“…In addition to the HER, ML algorithms were employed based on DFT-calculated data for the fast screening of efficient NRR and CO 2 RR electrocatalysts. 105 For instance, graph-based convolutional neural network (GCNN) was applied for the accelerated screening of SACs for the NRR. The results show superior NRR selectivity over the HER with overpotentials of 0.44 V, 0.40 V, 0.24 V, 0.60 V, 0.17 V, 0.17 V, 0.64 V, 0.37 V and 0.58 V, respectively, for SACs embedded in MBenes, defect-engineered 2D-materials, and 2D p-conjugated polymers, TaB, NbTe 2 , NbB, HfTe 2 , MoB, MnB, HfSe 2 , TaSe 2 and Nb.…”