https://hdl.handle.net/2072/300912 www.iciq.cat 2026-03-09T16:07:26Z https://hdl.handle.net/2072/489266 Single atoms of indium on hafnia enable superior CO2-based methanol synthesis Chiang, Yung-Tai; Ritopecki, Milica; Willi, Patrik O.; Raue, Katja; Morales-Vidal, Jordi; Zou, Tangsheng; Agrachev, Mikhail; Eliasson, Henrik; Wang, Jianyang; Erni, Rolf; Stark, Wendelin J.; Jeschke, Gunnar; Grass, Robert N.; López, Núria; Mitchell, Sharon; Pérez-Ramírez, Javier Indium–zirconium oxides rank among the most selective and stable catalysts for CO2 hydrogenation to methanol. Yet, despite extensive research, the mechanistic origin of the exceptional role of monoclinic zirconia remains unresolved and continues to set the benchmark in the field. Here we show that monoclinic hafnia, a wide-bandgap oxide rarely explored in catalysis, can outperform this benchmark. Nanostructured indium–hafnium oxides synthesized via flame spray pyrolysis achieve up to 70% higher indium-specific methanol productivity than indium–zirconium oxides, with the largest gains observed for single atoms of indium. Experimental and theoretical analyses reveal that a combination of stable monoclinic support surfaces, flexible chemical potential of indium single atoms and the presence of a cooperative hydride–proton reservoir collectively enhance CO2 activation and intermediate hydrogenation. Crucially, the precise control of surface hydroxylation is required. These findings establish a new benchmark for green methanol synthesis and provide generalizable design principles for next-generation oxide supports in single-atom catalysis. 2026-03-02T00:00:00Z https://hdl.handle.net/2072/489265 Dynamic Bonds Enable Repair and Reprocessing of Glycol-Modified PET Mak-iad, Chaninya; Berrocal, José Augusto; Formon, Georges J. M.; Weder, Christoph Self-healing polymers promise extended lifetimes, improved sustainability, and reduced maintenance costs. These attributes are particularly valuable in protective coatings. Here, we introduce a new class of healable glycol-modified polyethylene terephthalates (PETGs) that leverage hindered urea bonds (HUBs) to enable rapid, intrinsic repair through dissociative exchange. These polymers are synthesized in a readily manner from hydroxy-terminated PETG telechelics, hexamethylene diisocyanate, and N,N’-di-tert-butylethylenediamine (TBEU), which introduces the sterically demanding amines required for HUB formation. The systematic variation in TBEU content reveals a tradeoff between mechanical robustness and healing efficiency. Higher TBEU levels improve the healability but reduce strength. Formulations with nearly equimolar fractions of TBEU and PETG telechelic offer an attractive balance, combining a tensile strength of 20 MPa with complete crack disappearance in just 30 s at 130°C. However, healing causes a reduction in molecular weight and embrittlement, likely due to trapping of dissociated isocyanates and amines upon cooling to the glassy state. A comparative analysis with dynamic PETGs incorporating other dynamic bonds is provided to contextualize the performance of the new polymers. 2026-02-28T00:00:00Z https://hdl.handle.net/2072/489264 Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis Loveday, Oliver; Kaźmierczak, Kamila; López, Núria The design of catalysts gets its fundamental rationale from accurate and efficient modeling of reactivity on surfaces and materials. To reach this detailed atomistic understanding, density functional theory (DFT) has been the key computational technique. However, the emergence of machine learning interatomic potentials (MLIPs) marks a significant paradigm shift, offering the potential to match DFT accuracy at a drastically reduced computational cost. This perspective provides an overview of state-of-the-art MLIPs for heterogeneous catalysis as “out-of-the-box” tools. We summarize the different families of MLIPs and their training processes and then apply these pretrained models to heterogeneous catalysis problems. Furthermore, we critically address the challenges of model transferability and integration in unified frameworks, underscoring the necessity for standardized protocols to benchmark performance across different architectures. Finally, we assess the capacity of pretrained models to democratize computational catalysis, highlighting the specific hurdles that remain in achieving reliable, predictive power for widespread use. 2026-02-18T00:00:00Z https://hdl.handle.net/2072/489261 The Coming of Age in Iodane-Guided ortho-C−H Propargylation: From Insight to Synthetic Potential Izquierdo, Susana; Bouvet, Sébastien; Wu, Yichen; Molina, Sonia; Shafir, Alexandr As early as 1991 Ochiai et al. reported that an acid-activated form of phenyliodine diacetate, PhI(OAc)2, undergoes a reaction with propargyl-silanes, germanes and stannanes to give the ortho-propargyl iodobenzene. This formal C−H alkylation was proposed to take place through an unusual (even to date) iodonio-based [3,3] rearrangement of an intermediate allenylsilane. Although this mechanistic principle has been invoked in related iodane-directed C−H coupling reaction, some underlying principles have remained unaddressed, and the reaction rarely employed. Herein, DFT evidence for a mechanism best described as iodine-guided electrophilic aromatic substitution is presented. Using a newly optimized reaction protocol that significantly reduces the undesired reduction process, the potency of the method is showcased through the synthesis of >40 structurally diverse ortho-iodo propargyl (or allenyl) arenes. 2018-10-17T00:00:00Z