Generation of Amorphous Silica Surfaces with Controlled Roughness

Amorphous silica (a-SiO2) surfaces, when grafted with select metals on the active sites of the functionalized surfaces, can act as useful heterogeneous catalysts. From a molecular modeling perspective, one challenge has been generating a-SiO2 slab models with controllable surface roughness to facilitate the study of the effect of surface morphology on the material properties. Previous computational methods either generate relatively flat surfaces or periodically corrugated surfaces that do not mimic the full range of potential surface roughness of the amorphous silica material. In this work, we present a new method, inspired by the capillary fluctuation theory of interfaces, in which rough silica slabs are generated by cleaving a bulk amorphous sample using a cleaving plane with Fourier components randomly generated from a Gaussian distribution. The width of this Gaussian distribution (and thus the degree of surface roughness) can be tuned by varying the surface roughness parameter α. Using the van Beest, Kramer, and van Santen (BKS) force field, we create a large number of silica slabs using cleaving surfaces of varying roughness (α) and using two different system sizes. These surfaces are then characterized to determine their roughness (mean-squared displacement), density profile, and ring size distribution. This analysis shows a higher concentration of surface defects (under-/overcoordinated atoms and strained rings) as the surface roughness increases. To examine the effect of the roughness on surface reactivity, we re-equilibriate a subset of these slabs using the reactive force field ReaxFF and then expose the slabs to water and observe the formation of surface silanols. We observe that the rougher surfaces exhibit higher silanol concentrations as well as bimodal acidity.

The Role of In Situ/Operando IR Spectroscopy in Unraveling Adsorbate-Induced Structural Changes in Heterogeneous Catalysis

Heterogeneous catalysts undergo thermal- and/or adsorbate-induced dynamic changes under reaction conditions, which consequently modify their catalytic behavior. Hence, it is increasingly crucial to characterize the properties of a catalyst under reaction conditions through the so-called "operando" approach. Operando IR spectroscopy is probably one of the most ubiquitous and versatile characterization methods in the field of heterogeneous catalysis, but its potential in identifying adsorbate- and thermal-induced phenomena is often overlooked in favor of other less accessible methods, such as XAS spectroscopy and high-resolution microscopy. Without detracting from these techniques, and while aware of the enormous value of a multitechnique approach, the purpose of this Review is to show that IR spectroscopy alone can provide relevant information in this field. This is done by discussing a few selected case studies from our own research experience, which belong to the categories of both "single-site"- and nanoparticle-based catalysts.

Promoting active site renewal in heterogeneous olefin metathesis catalysts

As an atom-efficient strategy for the large-scale interconversion of olefins, heterogeneously catalysed olefin metathesis sees commercial applications in the petrochemical, polymer and speciality chemical industries1. Notably, the thermoneutral and highly selective cross-metathesis of ethylene and 2-butenes1 offers an appealing route for the on-purpose production of propylene to address the C3 shortfall caused by using shale gas as a feedstock in steam crackers2,3. However, key mechanistic details have remained ambiguous for decades, hindering process development and adversely affecting economic viability4 relative to other propylene production technologies2,5. Here, from rigorous kinetic measurements and spectroscopic studies of propylene metathesis over model and industrial WOx/SiO2 catalysts, we identify a hitherto unknown dynamic site renewal and decay cycle, mediated by proton transfers involving proximal Brønsted acidic OH groups, which operates concurrently with the classical Chauvin cycle. We show how this cycle can be manipulated using small quantities of promoter olefins to drastically increase steady-state propylene metathesis rates by up to 30-fold at 250 °C with negligible promoter consumption. The increase in activity and considerable reduction of operating temperature requirements were also observed on MoOx/SiO2 catalysts, showing that this strategy is possibly applicable to other reactions and can address major roadblocks associated with industrial metathesis processes.

Site-Averaged Ab Initio Kinetics: Importance Learning for Multistep Reactions on Amorphous Supports

Single-atom centers on amorphous supports include catalysts for polymerization, partial oxidation, metathesis, hydrogenolysis, and more. The disordered environment makes each site different, and the kinetics exponentially magnifies these differences to make ab initio site-averaged kinetics calculations extremely difficult. This work extends the importance learning algorithm for efficient and precise site-averaged kinetics estimates to ab initio calculations and multistep reaction mechanisms. Specifically, we calculate site-averaged proton transfer relaxation rates on an ensemble of cluster models representing Brønsted acid sites on silica-alumina. We include direct and water-assisted proton transfer pathways and simultaneously estimate the water adsorption and activation enthalpies for forward and backward proton transfers. We use density functional theory (DFT) to obtain a site-averaged rate, somewhat like a turnover frequency, for the proton transfer relaxation rate. Finally, we show that importance learning can provide orders-of-magnitude acceleration over standard sampling methods for site-averaged rate calculations in cases where the rate is dominated by a few highly active sites.

Determining the Three-Dimensional Structures of Silica-Supported Metal Complexes from the Ground Up

The immobilization of molecularly precise metal complexes to substrates, such as silica, provides an attractive platform for the design of active sites in heterogeneous catalysts. Specific steric and electronic variations of the ligand environment enable the development of structure-activity relationships and the knowledge-driven design of catalysts. At present, however, the three-dimensional environment of the precatalyst, much less the active site, is generally not known for heterogeneous single-site catalysts. We explored the degree to which NMR-based surface-to-complex interatomic distances could be used to solve the three-dimensional structures of three silica-supported metal complexes. The structure solution revealed unexpected features related to the environment around the metal that would be difficult to discern otherwise. This approach appears to be highly robust and, due to its simplicity, is readily applied to most single-site catalysts with little extra effort.

Importance learning estimator for the site-averaged turnover frequency of a disordered solid catalyst

For disordered catalysts such as atomically dispersed "single-atom" metals on amorphous silica, the active sites inherit different properties from their quenched-disordered local environments. The observed kinetics are site-averages, typically dominated by a small fraction of highly active sites. Standard sampling methods require expensive ab initio calculations at an intractable number of sites to converge on the site-averaged kinetics. We present a new method that efficiently estimates the site-averaged turnover frequency (TOF). The new estimator uses the same importance learning algorithm [Vandervelden et al., React. Chem. Eng. 5, 77 (2020)] that we previously used to compute the site-averaged activation energy. We demonstrate the method by computing the site-averaged TOF for a simple disordered lattice model of an amorphous catalyst. The results show that with the importance learning algorithm, the site-averaged TOF and activation energy can now be obtained concurrently with orders of magnitude reduction in required ab initio calculations.

Site-averaged kinetics for catalysts on amorphous supports: an importance learning algorithm

We combine importance sampling and kernel regression techniques to efficiently predict site-averaged kinetics for isolated catalyst sites on amorphous supports.