Catalytic N2O decomposition in the presence of O2 is a key process for addressing environmental challenges, such as greenhouse gas emissions and ozone layer depletion. However, the identification of efficient catalysts for this reaction remains challenging owing to the limitations of conventional methods. In this study, we employ a machine learning approach designed to accelerate the discovery of effective direct N2O decomposition catalysts. Starting with 51 catalysts and conducting 37 cycles of a closed-loop discovery system (machine-learning prediction + experiment), 633 catalysts are experimentally tested. Over 10 multi-elemental catalysts exhibiting superior activity are identified, surpassing the performance of the originally identified best catalyst. Among them, Rh(1)–Pd(2)/ZrO2_EP exhibits the highest catalytic performance for N2O decomposition. Through control experiments and a combination of ex situ and in situ characterizations, we identify the essential role of each component within the catalyst system. Catalytic N2O decomposition in the presence of O2 is a key process for addressing environmental challenges, yet identifying efficient catalysts for this reaction remains challenging. Here, the authors employ a machine learning approach to accelerate the discovery of effective catalysts for direct N₂O decomposition.
Chenxi He, S. Mine, Yuan Jing et al.· Nature Communications· 0 citations
The discovery and development of high-performance catalysts, which is crucial across all catalysis areas, requires advanced technologies and innovative approaches. Recently, machine learning (ML) has shown promise in accelerating this process, but its capability and examples of discovery of truly novel catalysts have remained limited. In this study, we describe an ML approach that goes beyond the traditional element pool, incorporating elements that have not been previously studied, to develop highly efficient catalysts for ethanol synthesis via CO2 hydrogenation. Starting with an initial data set of 58 catalysts (274 data points obtained at reaction temperatures ranging from 240-400 °C), we conducted 24 iterations of a closed-loop discovery system (ML predictions + experimental validation), testing a total of 555 catalysts (2477 data points), and building a large experimental data set. More than 50 catalysts with superior activity were discovered through this data-driven approach. The multielemental Pd(0.8)-Au(0.3)/K(2.5)-Sr(1)-Fe(20)-Zn(4)-Cd(2)-Yb(1)-Re(1)/CeO2(25%)-ZrO2 catalyst, where the numbers in parentheses represent weight percent (wt %), was identified as the most effective catalyst for ethanol synthesis (ethanol space-time yield: 8.2 mmol gcat-1 h-1 with a CO2 conversion of 57.6% and an ethanol selectivity of 23.2% under reaction conditions of 360 °C, 4 MPa, 12 L gcat-1 h-1, H2/CO2 = 3/1). Comprehensive characterizations, including in situ/operando techniques such as X-ray absorption spectroscopy (XAS), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), enable us to highlight the critical roles of each constituting element in improving ethanol synthesis efficiency.
Pengfei Du, Abdellah Ait El Fakir, S. Mine et al.· Journal of the American Chem...· 0 citations