Structure-performance relationship of Mg(OEt)2-based Ziegler-Natta catalysts

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine's process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries.

Parallel Catalyst Synthesis Protocol for Accelerating Heterogeneous Olefin Polymerization Research

The data scientific approach has become an indispensable tool for capturing structure-performance relationships in complex systems, where the quantity and quality of data play a crucial role. In heterogeneous olefin polymerization research, the exhaustive and multi-step nature of Ziegler-Natta catalyst synthesis has long posed a bottleneck in synthetic throughput and data generation. In this contribution, a custom-designed 12-parallel reactor system and a catalyst synthesis protocol were developed to achieve the parallel synthesis of a magnesium ethoxide-based Ziegler-Natta catalyst. The established system, featuring a miniature reaction vessel with magnetically suspended stirring, allows for over a tenfold reduction in synthetic scale while ensuring the consistency and reliability of the synthesis. We demonstrate that the established protocol is highly efficient for the generation of a catalyst library with diverse compositions and physical features, holding promise as a foundation for the data-driven establishment of the structure-performance relationship in heterogeneous olefin polymerization catalysis.

Modified Magnesium Alkyls for Ziegler–Natta Catalysts

Magnesium alkyls such as butyl octyl magnesium and butyl ethyl magnesium are used as precursors for highly active and water-free magnesium chloride support materials for Ziegler–Natta catalysts. These alkyls show a high viscosity in hydrocarbon solvents which negatively affect their industrial application. Density functional theory (DFT) calculations supported the hypothesis that magnesium alkyls can form oligomeric chain structures responsible for the high viscosity. Heterocumulenes such as isocyanates, isothiocyanates and carbodiimides were studied as additives reducing the viscosity, supported by DFT calculations. The modified alkyls have further been tested in catalyst synthesis and in the polymerization of ethylene. The polymerization results showed high activities and similar polymer properties compared with a catalyst prepared without modified magnesium alkyl.

Exploring cocatalyst type effect on the Ziegler–Natta catalyzed ethylene polymerizations: experimental and DFT studies

Due to the important role of cocatalyst in the polymerization process employing industrially favored Ziegler–Natta catalysts, its effect on kinetic behavior, catalyst activity, and polymer properties is discussed. In this paper, triethyl aluminum (TEA) and triisobutyl aluminum (TIBA) have been used as the main cocatalyst ingredient with 10–20 mol percent of diethyl aluminum chloride (DEAC) and ethyl aluminum dichloride (EADC) cocatalysts, being neat TEA the cocatalysts with the highest activity. Moreover, TEA-DEAC and TEA-EADC cocatalysts revealed a built-up kinetic profile, while TIBA-DEAC and TIBA-EADC show a decay-type kinetic curve. According to melt flow index results, no considerable change in flowability was detected in the synthesized polyethylenes (PE). On the other hand, the ethylene insertion and chain termination mechanisms were investigated by means of density functional calculations using Ti active center located in (110) and (104) facets of the MgCl2 surface. To shed light on the bulkiness level of employed cocatalysts, buried volume (VBur) together with the two-dimensional map of cocatalyst systems were considered. Higher VBur of TIBA complex can explain its lower activity and decay type kinetic profile obtained by experimental studies.

Effect of Internal Donors on Raman and IR Spectroscopic Fingerprints of MgCl2/TiCl4 Nanoclusters Determined by Machine Learning and DFT

To go deep into the origin of MgCl2 supported Ziegler-Natta catalysis we need to fully understand the structure and properties of precatalytic nanoclusters MgCl2/TiCl4 in presence of Lewis bases as internal donors (ID). In this work MgCl2/TiCl4 nanoplatelets derived by machine learning and DFT calculations have been used to model the interaction with ethyl-benzoate EB as ID, with available exposed sites of binary TixCly/MgCl2 systems. The influence of vicinal Ti2Cl8 and coadsorbed TiCl4 on energetic, structural and spectroscopic behaviour of EB has been considered. The adsorption of homogeneous-like TiCl4EB and TiCl4(EB)2 at the various surface sites have been also simulated. B3LYP-D2 and M06 functionals combined with TZVP quality basis set have been adopted for calculations. The adducts have been characterized by computing IR and Raman spectra that have been found to provide specific fingerprints useful to identify surface species; IR spectra have been successfully compared to available experimental data.

Irreversible and Self-Healing Electrically Conductive Hydrogels Made of Bio-Based Polymers

Electrically conductive materials that are fabricated based on natural polymers have seen significant interest in numerous applications, especially when advanced properties such as self-healing are introduced. In this article review, the hydrogels that are based on natural polymers containing electrically conductive medium were covered, while both irreversible and reversible cross-links are presented. Among the conductive media, a special focus was put on conductive polymers, such as polyaniline, polypyrrole, polyacetylene, and polythiophenes, which can be potentially synthesized from renewable resources. Preparation methods of the conductive irreversible hydrogels that are based on these conductive polymers were reported observing their electrical conductivity values by Siemens per centimeter (S/cm). Additionally, the self-healing systems that were already applied or applicable in electrically conductive hydrogels that are based on natural polymers were presented and classified based on non-covalent or covalent cross-links. The real-time healing, mechanical stability, and electrically conductive values were highlighted.