ZN-TCLB

Tuning catalyst composition through Internal Donor design

After preparation, the precatalytic solid phase is activated using aluminium alkyl compounds, typically in the presence of an External Donor (ED). The interactions between these components strongly influence catalyst performance.

The project investigated whether Internal Donors could be designed with a controlled temperature dependence in their reaction with aluminium alkyl activators. By tailoring this behaviour, the researchers aimed to influence the final catalyst composition and the distribution of active catalytic species. Because catalyst behaviour directly affects the microstructure and properties of the resulting polypropylene, this offers opportunities to design materials with more targeted performance characteristics.

Using machine learning to understand catalyst surfaces

A second objective was to understand how the chemical structure of Internal Donors affects their interaction with different MgCl₂ lattice terminations.

The project employed white-box machine learning approaches to relate molecular structure to chemisorption behaviour. Unlike purely predictive models, these approaches provide insight into the underlying relationships between molecular features and catalyst properties. This knowledge can support the design of donor molecules with targeted interactions at catalyst surfaces.

Towards catalysts with tunable performance 

By combining catalyst chemistry and data-driven modelling, the project aimed to create catalysts with tailored distributions of catalytic species and polymer product properties. 

The resulting knowledge contributes to a better understanding of how catalyst composition, surface structure and molecular design interact. This can support the development of new catalyst formulations with a broader range of controllable performance characteristics for polypropylene production.

Selected project outcomes 

ZN-TCLB concluded in 2025.

ZN-TCLB contributed to the development of advanced high-throughput experimentation and machine-learning workflows for Internal Donor discovery. This work was reported in the publication End-to-End High-Throughput Approach for Data-Driven Internal Donor Development in Heterogeneous Ziegler-Natta Propylene Polymerization (ACS Catalysis, 2024), which demonstrated how automated experimentation and data-driven modelling can accelerate catalyst research and development.

The project also contributed to the publication Data Driven Modeling of Ziegler-Natta Polypropylene Catalysts: Revisiting the Role of the Internal Donor (ACS Catalysis, 2025), providing new insights into the lasting influence of Internal Donors on catalyst performance.

We congratulate Dr Felicia Cannavacciolo and Dr Mostafa Khoshsefat on their scientific achievements and wish them all the best in their future endeavours.