Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O2

Copper-oxygen adducts: new trends in characterization and properties towards C-H activation

This review summarizes the latest discoveries in the field of C-H activation by copper monoxygenases and more particularly by their bioinspired systems. This work first describes the recent background on copper-containing enzymes along with additional interpretations about the nature of the active copper-oxygen intermediates. It then focuses on relevant examples of bioinorganic synthetic copper-oxygen intermediates according to their nuclearity (mono to polynuclear). This includes a detailed description of the spectroscopic features of these adducts as well as their reactivity towards the oxidation of recalcitrant Csp3 -H bonds. The last part is devoted to the significant expansion of heterogeneous catalytic systems based on copper-oxygen cores (i.e. within zeolite frameworks).

Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space

Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

Effects of Cu Species on Liquid-Phase Partial Oxidation of Methane with H2O2 over Cu-Fe/ZSM-5 Catalysts

In this study, a Cu-promoted Fe/ZSM-5 catalyst was examined to reveal the effects of Cu species in selective oxidation of methane into methane oxygenates using H2O2 in water. Cu/ZSM-5, Cu-Fe/ZSM-5, and Fe/ZSM-5 catalysts were prepared using wet impregnation, solid-state ion exchange, and ion-exchange methods. Various techniques, including nitrogen physisorption, temperature-programmed reduction with H2, UV-vis spectroscopy, and FT-IR spectroscopy after NO adsorption, were utilized to characterize the catalysts. The promotional effect of Cu on the Cu-Fe/ZSM-5 catalyst in terms of methanol selectivity was confirmed. The preparation method has a considerable influence on the catalyst performance, and the ion-exchange method is the most effective. However, leaching of the Cu species was observed during this reaction, which can affect the quantification of formic acid by 1H-NMR. The homogeneous Cu species increase hydrogen peroxide decomposition and CO2 selectivity, which is undesirable for this reaction.

Gas-Phase Selective Oxidation of Methane into Methane Oxygenates

Methane is an abundant resource and its direct conversion into value-added chemicals has been an attractive subject for its efficient utilization. This method can be more efficient than the present energy-intensive indirect conversion of methane via syngas, a mixture of CO and H2. Among the various approaches for direct methane conversion, the selective oxidation of methane into methane oxygenates (e.g., methanol and formaldehyde) is particularly promising because it can proceed at low temperatures. Nevertheless, due to low product yields this method is challenging. Compared with the liquid-phase partial oxidation of methane, which frequently demands for strong oxidizing agents in protic solvents, gas-phase selective methane oxidation has some merits, such as the possibility of using oxygen as an oxidant and the ease of scale-up owing to the use of heterogeneous catalysts. Herein, we summarize recent advances in the gas-phase partial oxidation of methane into methane oxygenates, focusing mainly on its conversion into formaldehyde and methanol.

Partial oxidation of methane to formaldehyde over copper–molybdenum complex oxide catalysts

The Cu₃Mo₂O₉ catalyst forms formaldehyde selectively in the methane oxidation with O₂ in the presence of water.