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.

Dataset of energetically accessible structures of MgCl2/TiCl4 clusters for Ziegler-Natta catalysts

This data article provides a dataset of the energetically accessible structures including the most stable structures of xMgCl₂/yTiCl₄ nanoplates (x = 6-19, y = 0-4). TiCl₄-capped MgCl₂ nanoplates are regarded as the building block of the Ziegler-Natta catalyst. The most stable structures were determined for MgCl₂/TiCl₄ nanoplates of different sizes and chemical compositions using a combination of the genetic algorithm and the DFT geometry optimization. The evolution in the genetic algorithm produced a number of meta-stable structures. A set of isomeric structures having similar energy to the most stable structure (termed energetically accessible structures) are provided as realistic models of MgCl₂/TiCl₄ nanoplates. These structures are useful for further investigation on the structural distribution of Ti species on MgCl₂ regarding the Ziegler-Natta catalyst.

Multidimensional Classification of Catalysts in Oxidative Coupling of Methane through Machine Learning and High-Throughput Data

Understanding the unique features of catalysts is a complex matter as it requires quantitative analysis with a relatively large selection of catalyst data. Here, unique features of each catalyst within the oxidative methane of coupling (OCM) reaction are investigated by combining data science and high throughput experimental data. Visualization of high-throughput OCM data reveals that there are several groups of catalysts based on their response against experimental conditions. Unsupervised machine learning, in particular, the Gaussian mixture model, classifies the OCM catalysts into six groups based on similarity in catalytic activities. Data visualization and parallel coordinates unveil the unique catalytic features of each classified group. Each classified group is statistically analyzed where unique features of each group are defined in term of C₂ selectivity, CH₄ conversion, and their composition in each calssified group. Thus, systematic design of catalysts can be achieved in principle on the basis of the unique features of catalysts uncovered via data science.

Modulator-free approach towards missing-cluster defect formation in Zr-based UiO-66

By rigorous control of water, missing-cluster defects in Zr-based UiO-66 were generated to a remarkable extent without the need of acidic modulators. The presence of missing-cluster defects created hierarchical pore structures, which had a profound effect on the catalytic performance.

Development of Large-Scale Stopped-Flow Technique and its Application in Elucidation of Initial Ziegler-Natta Olefin Polymerization Kinetics

The stopped-flow (SF) technique has been extensively applied to study Ziegler-Natta (ZN) olefin polymerization kinetics within an extremely short period (typically <0.2 s) for understanding the nature of the active sites as well as the polymerization mechanisms through microstructure analyses of obtained polymers. In spite of its great applicability, a small amount of polymer that is yielded in a short-time polymerization has been a major bottleneck for polymer characterizations. In order to overcome this limitation, a large-scale SF (LSF) system has been developed, which offers stable and scalable polymerization over an expanded time range from a few tens milliseconds to several seconds. The scalability of the LSF technique has been further improved by introducing a new quenching protocol. With these advantages, the LSF technique has been effectively applied to address several unknown issues in ZN catalysis, such as the role of physical and chemical transformations of a catalyst on the initial polymerization kinetics, and regiochemistry of ZN propylene polymerization. Here, we review the development of the LSF technique and recent efforts for understanding heterogeneous ZN olefin polymerization catalysis with this new system.

Nano-Dispersed Ziegler-Natta Catalysts for 1 μm-Sized Ultra-High Molecular Weight Polyethylene Particles

A catalytic approach to synthesize microfine ultra-high molecular weight polyethylene (UHMWPE) particles was proposed based on the exploitation of nano-sized catalysts. By utilizing MgO nanoparticles as a core material, a Ziegler-Natta-type MgO/MgCl2/TiCl4 core-shell catalyst with the particle size in a nano-range scale was prepared in a simple preparation step. The organic modification of MgO surfaces prior to catalyzation prevented agglomeration and facilitated the full dispersion of catalyst particles at a primary particle level for the first time. The nano-dispersed catalysts successfully afforded a direct access to UHMWPE having the particle size in the range of 1-2 μm at a reasonable activity. Extremely fine polymer particles yielded several advantages, especially at a significantly lower fusion temperature in compression molding.

High-Throughput Synthesis of Support Materials for Olefin Polymerization Catalyst

Rational catalyst design necessitates fundamental knowledge on the structure-performance relationship, while the synthetic throughput for heterogeneous Ziegler-Natta olefin polymerization catalysts has long prevented the acquisition of a statistical database. In this contribution, an in-house reactor system was developed to realize the parallel synthesis of support materials for Ziegler-Natta catalysts for the first time. The developed system enabled parallel synthesis of 24 magnesium ethoxide samples with excellent reproducibility and morphological control comparable to a conventional experiment. Our demonstration revealed that the generation of diverse particle characteristics could be achieved through the addition of a third component as a structural modulator, in which the in-house parallel reactor system combined with the first principle component analysis enabled fast screening of effective modulators.