Lithium carbonate-promoted mixed rare earth oxides as a generalized strategy for oxidative coupling of methane with exceptional yields

The oxidative coupling of methane to higher hydrocarbons offers a promising autothermal approach for direct methane conversion, but its progress has been hindered by yield limitations, high temperature requirements, and performance penalties at practical methane partial pressures (~1 atm). In this study, we report a class of Li2CO3-coated mixed rare earth oxides as highly effective redox catalysts for oxidative coupling of methane under a chemical looping scheme. This catalyst achieves a single-pass C2+ yield up to 30.6%, demonstrating stable performance at 700 °C and methane partial pressures up to 1.4 atm. In-situ characterizations and quantum chemistry calculations provide insights into the distinct roles of the mixed oxide core and Li2CO3 shell, as well as the interplay between the Pr oxidation state and active peroxide formation upon Li2CO3 coating. Furthermore, we establish a generalized correlation between Pr4+ content in the mixed lanthanide oxide and hydrocarbons yield, offering a valuable optimization strategy for this class of oxidative coupling of methane redox catalysts.

Catalysts informatics: paradigm shift towards data-driven catalyst design

Designing catalysts is a challenging matter as catalysts are involved with various factors that impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties, catalysts informatics is proposed as an alternative way to design and understand catalysts. The underlying concept of catalysts informatics is to design the catalysts from trends and patterns found in catalysts data. Here, three key concepts are introduced: experimental catalysts database, knowledge extraction from catalyst data via data science, and a catalysts informatics platform. Methane oxidation is chosen as a prototype reaction for demonstrating various aspects of catalysts informatics. This work summarizes how catalysts informatics plays a role in catalyst design. The work covers big data generation via high throughput experiments, machine learning, catalysts network method, catalyst design from small data, catalysts informatics platform, and the future of catalysts informatics via ontology. Thus, the proposed catalysts informatics would help innovate how catalysts can be designed and understood.

Oxidative Addition of Methane and Reductive Elimination of Ethane and Hydrogen on Surfaces: From Pure Metals to Single Atom Alloys

Oxidative addition of CH₄ to the catalyst surface produces CH₃ and H. If the CH₃ species generated on the surface couple with each other, reductive elimination of C₂H₆ may be achieved. Similarly, H's could couple to form H₂. This is the outline of nonoxidative coupling of methane (NOCM). It is difficult to achieve this reaction on a typical Pt catalyst surface. This is because methane is overoxidized and coking occurs. In this study, the authors approach this problem from a molecular aspect, relying on organometallic or complex chemistry concepts. Diagrams obtained by extending the concepts of the Walsh diagram to surface reactions are used extensively. C-H bond activation, i.e., oxidative addition, and C-C and H-H bond formation, i.e., reductive elimination, on metal catalyst surfaces are thoroughly discussed from the point of view of orbital theory. The density functional theory method for structural optimization and accurate energy calculations and the extended Hückel method for detailed analysis of crystal orbital changes and interactions play complementary roles. Limitations of monometallic catalysts are noted. Therefore, a rational design of single atom alloy (SAA) catalysts is attempted. As a result, the effectiveness of the Pt₁/Au(111) SAA catalyst for NOCM is theoretically proposed. On such an SAA surface, one would expect to find a single Pt monatomic site in a sea of inert Au atoms. This is desirable for both inhibiting overoxidation and promoting reductive elimination.

High-Throughput Screening and Literature Data Driven Machine Learning Assisting Investigation of Multi-component La2O3-based Catalysts for Oxidative Coupling of Methane

Multi-component La2O3-based catalysts for oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR),...

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.

Representing Catalytic and Processing Space in Methane Oxidation Reaction via Multioutput Machine Learning

Multioutput support vector regression (SVR) is implemented to simultaneously predict the selectivities and the CH₄ conversion against experimental conditions in methane oxidation catalysts. The predictions unveil the details of how each selectivity and CH₄ conversion behaves in each catalyst. In particular, the selectivity and the CH₄ conversion of Mn-Na₂WO₄/SiO₂, Ti-Na₂WO₄/SiO₂, Pd-Na₂WO₄/SiO₂, and Na₂WO ₄/SiO₂ are predicted, and the effects of Mn, Ti, and Pd are unveiled. In addition, the trade-off points of CO and C₂H₆ are identified for each catalyst, leading to maximization of the C₂H₆ yield. Thus the simultaneous prediction of the reaction trend with catalysts not only will help with the understanding of the catalyst activities but also will provide guidance for designing the experimental conditions.