A novel approach to doubling metal atom catalysts


July 11, 2023

(Nanowerk Highlights) As we grapple with the growing threat of climate change, efforts to decarbonize our atmosphere are more urgent than ever. A promising strategy for reducing CO2 in the atmosphere2 concentration lies in the use of electrocatalytic CO2 reduction reaction (CO2RR) is powered by a clean energy source.

One emerging and interesting area of ​​research in this area centers on the use of single and double atom catalysts (SAC/DAC) supported by two-dimensional (2D) materials. This catalyst not only strengthens the electrocatalytic performance but also optimizes the use of metal atoms, thereby providing an environmentally friendly and efficient approach to CO2 reduction.2 emission.

At the forefront of this research, a recent study led by Prof. Zhong Fang Chen from the University of Puerto Rico, Professor Fengyu Li from Inner Mongolia University, and Dr. Jingsong Huang of Oak Ridge National Lab, introduces an innovative approach to DAC for CO2RR application. Departing from the conventional DAC configuration, which involves metal atoms loaded in the same 2D substrate plane, the team has studied a new catalyst category: DAC is supported on broken graphene with an inverted sandwich structure pattern (MM’⊥gra). This investigation combines the first principles of density functional theory (DFT) and machine learning (ML) techniques to unlock their potential.

“Extensively studied DACs have so far been limited to only DACs with double metal atoms loaded in the same plane of 2D substrates,” Chen explained to Nanowerk. “We designed a new DAC configuration of the reverse sandwich structure: vertically and symmetrically embedded metal dimers in broken graphene.”

This concept was inspired by the experimental synthesis of the La-B cluster inverted sandwich structure, which sparked interest in the performance of graphene-supported double atoms with inverted sandwich structures as gas sensors. After it was found that certain metal dimers in the reverse sandwich structure have a strong interaction with CO2 molecule, Chen and his team suspect that this DAC might hold promise for electrocatalyzing CO22 reduced due to well-activated CO2 molecule. Thus, their exploration of CO2The RR performance of 784 possible DACs of 28 transition metal atoms was initiated, using a mixture of DFT and ML.

The team’s findings, published in ACS catalysis (“Double-Atom Catalysts Display an Inverse Sandwich Structure for CO2 Reduction Reaction: Synergistic First Principles and Investigating Machine Learning”), unveiling the great promise of DAC with this new inverted sandwich configuration to electrocatalyst CO2RR. machine learning and prediction ML training and predictions. (a) Comparison of the UL obtained by the DFT with the predicted value of ML. (b) Feature importance based on the GBR model. (c) ML predicted heatmap of UL values ​​for 784 MM′⊥gra. (Reprinted with permission by the American Chemical Society)

This study reveals a key factor of the inherent atomic properties that correlate with the electrocatalytic performance of these DACs, contributing valuable insights for future catalyst predictions.

Regarding the practicality of this research, Prof. Chen stressed that CO electrocatalytic is scalable and highly efficient2RR catalysis can indeed offer a viable strategy to reduce CO22 emission. By screening many potential DACs, their work helped define a promising DAC for this purpose.

In particular, Rh2⊥gra DAC, based on DFT calculations, was found to outperform the other candidates. Meanwhile, the ML approach was used to correlate key factors with DAC activity and stability. The resulting ML model was then used to predict 154 potential electrocatalysts from a possible 784 DACs featuring the same inverted sandwich configuration.

These findings are important in aiding the development of new DACs to help reduce CO2 in the atmosphere2 concentration. Chen saw great potential in their approach. “A combination of density function theory calculations and machine learning methods was applied to screen a promising graphene-based DAC featuring an inverted sandwich structure for CO2RR,” he explained. “This work helped develop a new DAC to reduce CO2 in the atmosphere2 concentration.”

Going forward, Chen anticipates that this research may drive exploration of new DACs not only on graphene but also on other suitable 2D materials. While synthesizing a DAC with an inverted sandwich structure was challenging, he stated that it was feasible.

“Our study provides a new DAC design strategy for CO2RR and so on,” he says. “While it is very feasible to synthesize a DAC with a reverse sandwich structure, it is difficult to fabricate a DAC with such a novel configuration in experiments.”

In conclusion, the team’s innovative approach to DAC heralds a new era in the CO space2 subtraction. The marriage of DFT calculations and ML techniques in their study brings new perspectives to catalyst design and presents an exciting step towards decarbonizing our atmosphere.

Michael Berger

– Michael is the author of three books by the Royal Society of Chemistry:
Nano-Society: Pushing the Boundaries of Technology,
Nanotechnology: A Small Future And
Nanoengineering: Skills and Tools for Making Technology Invisible
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