Miguel López-Haro

Isolated dual-atom sites constitute a promising yet largely unexplored strategy for directing reaction intermediates in CO2 hydrogenation to methanol. In this work, we identify isolated Cu+–O–Ga3+ dual-atom motifs and provide a mechanistically consistent interpretation linking them to enhanced methanol synthesis. This finding is elucidated through the combined use of steady-state isotopic transient kinetic analysis (SSITKA) with diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory (DFT) calculations. Isolated Cu+–O–Ga3+ pairs, anchored within Cu–Ga–Mg–Al hydrotalcite–derived catalysts, are enabled by ionic-radius compatibility and the preferential Cu+–Ga3+ association within the oxide lattice. X-ray absorption spectroscopy and deep learning-assisted atomic-resolution HAADF-STEM imaging, combined with statistical pair analysis, provide complementary evidence supporting a statistically relevant population of short-range Cu+–O–Ga3+ pairs. Together with the kinetic, spectroscopic, and theoretical analyses, these findings support Cu+–O–Ga3+ proximal motifs as the most plausible structural origin of the enhanced methanol-forming functionality and provide a rational design principle for improved methanol synthesis catalysts.

This work presents a strategy to control not only the distance and proximity of active sites at the atomic or nanoscale but also the nature of sites in zeolite-based catalysts, promoting the coupling rate of surface intermediate species and accordingly the formation rate of dimethyl ether (DME) by a direct CO₂ hydrogenation path. We use a one-pot synthesis strategy and a thermal-induced detachment process of framework elements, such as Ga³⁺ ions, to stabilize PdGa alloys and Ga⁺ sites in close proximity to Brønsted acid sites under reductive conditions. Using this strategy, a production of oxygenates of up to 42,864 g_MeOH+DME·kg_Pd^-1·h^-1 at 45 bar, 260 °C, and WHSV = 15,000 mL·g_cat^-1·h^-1, is obtained with 80% selectivity to oxygenated (19% methanol/61% DME), outperforming the most active Pd-based CO₂ hydrogenation catalysts in the literature. Time-resolved kinetic studies, in situ X-ray adsorption, and in situ and operando IR offer strong proof of the key role of isolated Ga⁺ Lewis acid sites in close proximity to Brønsted acid sites in stabilizing monoformate intermediate species and facilitating the direct production of DME. Finally, this work highlights the key role of the zeolite in metal confinement, conferring excellent stability, oxidation resistance, hydrophilicity, and close proximity of active sites together with the stabilization of low-coordinated Lewis acid sites.

Fibrillar protein hydrogels are promising sustainable biomaterials for biomedical applications, but their practical use is often limited by insufficient mechanical strength and stability. To address these challenges, we transformed native proteins into amyloid fibrils (AFs) and incorporated a fibrillar polysaccharide, phytagel (PHY), to engineer interpenetrating polymer network (IPN) hydrogels. Notably, we report for the first time the formation of an amyloid-based hydrogel from apoferritin (APO), with PHY reinforcing the network's mechanical integrity. In situ self-assembly of APO within the PHY matrix yields fully natural, biopolymer-based IPNs. Rheological analyses confirm synergistic interactions between AF and PHY fibers, with the composite hydrogels exhibiting significantly enhanced viscoelastic moduli compared with individual components. The AF-PHY hydrogels also demonstrate excellent self-healing behavior, rapidly restoring their storage modulus after high-strain deformation. A major advancement of this study is the application of deep learning (DL)-based image analysis, using convolutional neural networks, to automate the identification, segmentation, and quantification of fibrillar components in high-resolution scanning electron microscopy images. This AI-driven method enables precise differentiation between AF and PHY fibers and reveals the three-dimensional microarchitecture of the IPN, overcoming key limitations of traditional image analysis. Complementary real-time confocal laser scanning microscopy, with selective fluorescent labeling of protein and polysaccharide components, further validates the IPN structure of the hybrid hydrogels. Our results demonstrate that DL significantly enhances structural characterization and provides insights into gelation processes. This approach sets a new guide for the analysis of complex soft materials and underlines the potential of AF-PHY hydrogels as mechanically robust, self-healing, and fully sustainable biomaterials for biomedical engineering applications.

Modulating the microenvironment of zeolite-encapsulated subnanometer metal active sites represents a frontier in the design of solid catalysts because of its marked impacts on the catalytic properties. Different from most prior works, which mainly focus on tuning the particle size and chemical composition of the encapsulated metal species, the present work focuses on the modulation of the morphology of the zeolite support to effectively stabilize subnanometer Pt clusters for H₂ production from the dehydrogenation of methylcyclohexane to toluene. By confining Pt clusters in the perpendicularly connected 10MR channels of MEL zeolite mesocrystals, we can maintain sufficient confinement of the zeolite framework to stabilize Pt clusters against sintering and to facilitate mass transport properties of the methylcyclohexane and toluene molecules, resulting in a markedly high specific H₂ production rate, excellent stability against sintering and coke deposition, and exceptionally low concentration of CH₄ impurity in the outlet H₂.

Correlated single‐atom catalysts offer transformative potential in catalysis, particularly in the field of electrocatalysis, with a focus on oxygen evolution reactions. Advanced characterization is critical to understanding their atomic‐scale properties when techniques usually used in molecular science (Nuclear Magnetic Resonance (NMR), X‐ray Diffraction (XRD), Infrared spectroscopy (IR), or Mass Spectrometry (MS)) cannot be applied after dispersing them on a carrier material. Here, a methodology that combines machine learning and mathematical optimization techniques to detect and quantify metal–metal interactions within heterobinuclear Au(III)‐Pd(II) macrocyclic complexes on atomically resolved high‐angle annular dark‐field scanning transmission electron microscopy (HAADF‐STEM) images is introduced. Both supervised and unsupervised machine learning methods are evaluated, with the U‐net architecture demonstrating superior performance in distinguishing the two involved chemical species. Mathematical optimization models further enhance the reliability of metal pair identification by providing precise distance metrics for the pairs. This methodology allows for the study of both the dynamics and bond interaction of heterobinuclear Au(III)‐Pd(II) complexes. Notably, the analysis of time series of images reveals that most metal pairs remained stable under the high‐energy electron beam irradiation conditions. Likewise, the Au–Pd distance within the pairs remains unchanged, indicating a robust interaction of the two metals with the ligand even after being deposited on the amorphous carbon substrate.