Joaquin Martinez-Ortigosa

Combining NMR with machine learning opens new opportunities to address the still‑challenging elucidation of aluminum siting in zeolite frameworks.

Zeolite catalysts often face a fundamental trade-off between the molecular accessibility and shape-selective confinement. Here, we show that this trade-off can be mitigated by accessing kinetically stabilized crystalline FAU–CHA intermediates...

The development of catalysts capable of overcoming mass transport limitations remains a crucial challenge in hydrocracking processes, particularly for bulky linear hydrocarbons, such as long-chain paraffins. In this study, we report the synthesis of hierarchical Pt/HUSY catalysts via a surfactant-templating strategy that introduces highly interconnected intracrystalline mesoporosity while preserving most of the zeolite’s crystallinity and acidity, significantly enhancing molecular diffusion, facilitating better access to acid sites, and alleviating the diffusion limitations typically associated with purely microporous zeolites. Catalytic testing revealed a marked increase in hexadecane cracking activity, especially in catalysts with longer surfactant treatment times (Pt/CBV780_20h and Pt/CBV720_41h), which exhibited superior conversion rates and selectivities toward lighter hydrocarbons. Notably, the surfactant-templated catalysts achieved similar product distributions at lower temperatures than those of their parent materials. Additionally, our study demonstrates the importance of well-connected intracrystalline mesoporosity in reducing catalyst deactivation by preventing coke accumulation, particularly in high-aluminum Pt/CBV720 catalysts, which showed higher stability over time despite greater coke deposition. These findings highlight the effectiveness of surfactant-templated mesoporosity as a powerful strategy to enhance both the performance and lifetime of zeolite-based catalysts for industrial hydrocarbon conversion processes.

Determining the distribution of aluminum in zeolite frameworks remains a significant challenge, due to the limited sensitivity of conventional characterization techniques and the complexity of possible Al configurations. In this work, we have developed a procedure which combines experimental two-dimensional (2D) dipolar-based 27Al–29Si D-HMQC and MQMAS solid-state NMR spectroscopy with machine learning (ML) techniques to achieve a detailed and quantitative assignment of Al environments in zeolites. We applied this approach to investigate the Al distribution in RTH-type zeolites (Si/Al = 16 and 28), which contain four crystallographically distinct T-sites. A reactive ML potential was used to model framework dynamics under realistic conditions, including temperature and hydration, while kernel ridge regression enabled the accurate prediction of isotropic NMR chemical shifts. Comparison between theoretical and experimental spectra reveals that Al preferentially occupies the T2 (39 %) and T1 (27 %) sites, with lower populations in T3 (18 %) and T4 (16 %). The excellent agreement between computed and observed NMR data demonstrates that this ML- augmented spectroscopic approach is a powerful tool for quantitative elucidation of Al distributions in structurally complex zeolites via solid state NMR.

Synthesis conditions dictate charge distribution in pure silica MFI zeolite, influencing structural defects and fluoride positioning.

By optimizing zeolite pore confinement, polymer diffusion and up-cycling efficiency in hierarchical catalysts are significantly enhanced, reducing plastic degradation temperatures and providing a suitable route to sustainable plastic-to-fuel pathways.