V. Van Speybroeck
Design of a Tunable, High-performance Mixed Matrix Membrane Platform for Gas Separations
Understanding the entanglement between diffusion and reaction by probing the mobility of ketene in chabazites
Abstract
In zeolite catalysis, diffusion and reaction are generally viewed as separate processes that independently affect catalytic performance due to the significant variation in timescales for diffusion and reaction. Nevertheless, this study reveals that reaction and diffusion can be intertwined, a phenomenon hitherto unexplored. In particular, we highlight this complex relationship for ketene intermediates in chabazite topologies, where the diffusion properties of ketene are notably affected by the reactivity with Brønsted acid sites (BAS) and guest molecules present in the zeolite pores. Ketene is an important intermediate in zeolite catalyzed methanol-to-hydrocarbons and COx-to-hydrocarbons conversion and its diffusion and reaction behavior directly impacts the catalytic performance. Our ab initio molecular dynamics simulations reveal that ketene diffusion is significantly facilitated by hydrogen bonding interactions with BAS during the diffusion through the 8-ring windows of chabazite, and that ketene can also readily react with other guest species along the diffusion pathway. This entanglement between reaction and diffusion can be attributed to the high activity of ketene, resulting in a strong competition between reaction and diffusion, which cannot be viewed as two independent processes. Therefore, our findings concerning the complex interconnection between diffusion and reaction not only contribute to the fundamental understanding of ketene chemistry in chabazite but also have important consequences for other fields of catalysis involving highly active intermediates.
Molecular basis of activity changes in acid catalysis within nanoconfined water
Quantitative Description of Strongly Correlated Materials by Combining Downfolding Techniques and Tensor Networks
Abstract
We present a high-accuracy procedure for electronic structure calculations of strongly correlated materials. To address limitations in current electronic structure methods, we employ density functional theory in combination with the constrained random phase approximation to construct an effective multiband Hubbard model, which is subsequently solved using tensor networks. Our work focuses on one-dimensional and quasi-one-dimensional materials, for which we employ the machinery of matrix product states. We apply this framework to the conjugated polymers trans-polyacetylene and polythiophene, as well as the quasi-one-dimensional charge-transfer insulator Sr2CuO3. The predicted band gaps show quantitative agreement with state-of-the-art computational techniques and experimental measurements. Beyond band gaps, tensor networks provide access to a wide range of physically relevant properties, including spin magnetization and various excitation energies. Their flexibility supports the implementation of complex Hamiltonians with longer-range interactions, while the bond dimension enables systematic control over accuracy. Furthermore, the computational cost scales efficiently with system size, demonstrating the framework’s scalability.
Cluster-Based Machine Learning Potentials to Describe Disordered MetalOrganic Frameworks up to the Mesoscale
Abstract
Metal-organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in various technological fields. Defect engineering requires a fundamental understanding of the nature of spatial disorder and consequent changes in material properties, which is currently lacking. We introduce the cluster-based learning methodology, enabling the development of state-of-the-art machine learning potentials (MLPs) from defective systems at any length scale. Our method identifies atomic interactions in bulk structures and extracts local environments as finite molecular fragments to augment the model's training data where needed. We show that cluster-based learning delivers MLPs capable of accurately describing spatial defects in mesoscopic systems with over 20 thousand atoms. Afterward, we select our best model to investigate some major mechanical properties of spatially disordered UiO-66-derived structures, elucidating the influence of defect concentration and composition on material behavior. Our analysis includes large supercell structures, demonstrating that (near-) ab initio accuracy is within reach at the mesoscale.
Increasing the Phase Stability of CsPbI3 Nanocrystals by Zn2+ and Cd2+ Addition: Synergy of Transmission Electron Microscopy and Molecular Dynamics
Abstract
Metal halide perovskites (MHPs) are emerging as promising materials for optoelectronic and photovoltaic applications due to their favorable electronic properties, including a tunable bandgap. However, achieving high stability for these materials remains a critical challenge, particularly for CsPbI3, whose photoactive phases spontaneously convert into a nonphotoactive yellow orthorhombic δ-phase under ambient conditions. This transformation results in a significant increase in bandgap and a loss of photoactive functionality. In this study, we investigate the impact of Zn2+ and Cd2+ dopants on the phase stability of CsPbI3 nanocrystals (NCs), emphasizing the formation of Ruddlesden–Popper (RP) planar defects, which are frequently observed during compositional tuning. Using transmission electron microscopy (TEM), we follow the temporal evolution of the phase transformation, where black-phase NCs agglomerate and form elongated microtubes with a yellow-phase crystal structure. Our observations demonstrate that doped samples are significantly more stable, while the dopants are key factors in the formation of the RP-like defects with specific atomic arrangements. Using a combination of quantitative TEM and molecular dynamics (MD) simulations we characterize the structure and composition of as-found RP-like defects and elucidate their role in stabilizing the photoactive phases of CsPbI3 through decreased phase transition kinetics.
Formaldehyde-Mediated Initial Carbon–Carbon Bond Formation in Zeolite-Catalyzed Methanol-to-Hydrocarbon Conversion
Abstract
Zeolite-catalyzed methanol-to-hydrocarbon conversion is a promising technology for the sustainable production of valuable hydrocarbon products. However, the mechanism behind the formation of the first carbon–carbon bond has been a subject of controversy for several decades. By comprehensive consideration of previous experimental phenomena and theoretical studies, a formaldehyde (HCHO)-based first carbon–carbon formation mechanism is proposed. Within the new mechanism, hydrated or methylated products of HCHO (methanediol, methyloxymethanol, and dimethyloxymethane) with much weaker C–H bond strengths replace methane in the traditional methane-HCHO mechanism, allowing energetically and kinetically favorable pathways to form the first C–C bond. The formed C–C bond products are further converted to ketene and olefins via the methylation-decarbonylation route. The plausibility of the newly proposed mechanism is confirmed by both theoretical calculations and experiments in various MTH zeolite catalysts. A key intermediate in this mechanism is glycolaldehyde, which was captured in situ by both mass spectrometry and Fourier transform infrared spectroscopy. The viability of the mechanism in different zeolites, as predicted theoretically, was also confirmed by gas chromatography. Not only does this new mechanism introduce an innovative pathway for the first C–C bond formation, but it also provides a comprehensive explanation of the specific role of HCHO in the early stage of the MTH process and associated reactions.
Open Access version available at UGent repositoryMIL-91(Al) to Boost Solid–Solid Conversion Reactions in Li-Se Batteries
Abstract
Lithium-Selenium (Li-Se) batteries have emerged as one of the most promising candidates for next-generation energy storage systems owing to superior electronic conductivity, impressive volumetric capacity, and enhanced compatibility with carbonate electrolyte of selenium, comparable to sulfur. Despite these advantages, the development of Li-Se batteries is impeded by several intrinsic challenges, including volume expansion during the discharge process and the consequent sluggish reaction kinetics that undermine their electrochemical performance. In this study, MIL-91(Al) is used as an electrode additive to accelerate the one-step mutual solid–solid conversion reaction between Se and Li2Se in the carbonate-based electrolyte. By doing so, uncontrollable deposition of Li2Se is effectively mitigated, enhancing the electrochemical performance of the system. Thus, the use of MIL-91(Al) results in reduced internal resistance and faster Li-ion transfer rate, as analyzed by SPEIS and GITT. Ab initio calculations and molecular dynamics simulations further reveal that Li2Se anchors to closely situated dangling oxygens of the phosphonate group of the organic linker of MIL-91(Al), inducing relaxation of the Li-Se-Li angle and stabilizing the overall structure. Accordingly, the MIL-91(Al)-containing Li-Se cells demonstrate a high specific capacity of approximately 530 mAh g−1 at 1C (675 mA g−1) after 100 cycles and retaining a specific capacity of 320 mAh/g even under high current rate (20C) after 200 cycles. This research underlines the importance of the use of electrocatalyst/electroadsorbent materials to enhance the redox kinetics of the conversion reactions between Se and Li2Se, thus paving the way for the development of high-performance Li-Se batteries.
Direct Aminolysis of Methyl Esters With Ammonia in Continuous Flow Through Bayesian Optimization
Abstract
Amides play a crucial role in the pharmaceutical, animal health and agrochemical industry. Despite the availability of various catalytic systems and coupling reagents, many methods suffered from long reaction times and poor atom economy. The direct synthesis of primary amides remained particularly challenging due to the limited availability of suitable nitrogen sources. In this study, continuous flow technology was explored as a process-intensification approach for the direct amidation of methyl esters to produce primary amides. Methanolic ammonia was employed as a nitrogen source to enhance process efficiency while circumventing the limitations of aqueous ammonia and the hazards of gaseous ammonia. Seventeen substrates were screened to assess their aminolysis reactivity under these conditions. As a proof of concept, methyl picolinate was selected for continuous flow optimization using Bayesian Optimization. Therefore, a custom-designed high-pressure, high-temperature continuous flow reactor was utilized to achieve efficient, safe and scalable synthesis (200 °C, 50 bar).
