Enhanced Surface Activity of MWW Zeolite Nanosheets Prepared via a One-Step Synthesis.

The synthesis of two-dimensional (2D) zeolites has garnered attention due to their superior properties for applications that span catalysis to selective separations. Prior studies of 2D zeolite catalysts demonstrated enhanced mass transport for improved catalyst lifetime and selectivity. Moreover, the significantly higher external surface area of 2D materials allows for reactions of bulky molecules too large to access interior pores. There are relatively few protocols for preparing 2D materials, owing to the difficultly of capping growth in one direction to only a few unit cells. To accomplish this, it is often necessary to employ complex, commercially unavailable organic structure-directing agents (OSDAs) prepared via multistep synthesis. However, a small subset of zeolite structures exist as naturally layered materials where postsynthesis steps can be used to exfoliate samples and produce ultrathin 2D nanosheets. In this study, we selected a common layered zeolite, the MWW framework, to explore methods of preparing 2D nanosheets via one-pot synthesis in the absence of complex organic templates. Using a combination of high-resolution microscopy and spectroscopy, we show that 2D MMW-type layers with an average thickness of 3.5 nm (ca. 1.5 unit cells) can be generated using the surfactant cetyltrimethylammonium (CTA), which operates as a dual OSDA and exfoliating agent to affect Al siting and to eliminate the need for postsynthesis exfoliation, respectively. We tested these 2D catalysts using a model reaction that assesses external (surface) Brønsted acid sites and observed a marked increase in the conversion relative to three-dimensional MWW (MCM-22) and 2D layers prepared from postsynthesis exfoliation (ITQ-2). Collectively, our findings identify a facile and effective route to directly synthesize 2D MWW-type materials, which may prove to be more broadly applicable to other layered zeolites.

Ethylene Dehydroaromatization over Ga-ZSM-5 Catalysts: Nature and Role of Gallium Speciation.

Bifunctional catalysis in zeolites possessing both Brønsted and Lewis acid sites offers unique opportunities to tailor shape selectivity and enhance catalyst performance. Here, we examine the impact of framework and extra-framework gallium species on enriched aromatics production in zeolite ZSM-5. We compare three distinct methods of preparing Ga-ZSM-5 and reveal direct (single step) synthesis leads to optimal catalysts compared to post-synthesis methods. Using a combination of state-of-the-art characterization, catalyst testing, and density functional theory calculations, we show that Ga Lewis acid sites strongly favor aromatization. Our findings also suggest Ga(framework)-Ga(extra-framework) pairings, which can only be achieved in materials prepared by direct synthesis, are the most energetically favorable sites for reaction pathways leading to aromatics. Calculated acid site exchange energies between extra-framework Ga at framework sites comprised of either Al or Ga reveal a site-specific preference for stabilizing Lewis acids, which is qualitatively consistent with experimental measurements. These findings indicate the possibility of tailoring Lewis acid siting by the placement of Ga heteroatoms at distinct tetrahedral sites in the zeolite framework, which can have a marked impact on catalyst performance relative to conventional H-ZSM-5.

Charge-enhanced acidity and catalyst activation.

Acidities are commonly measured in polar solvents but catalytic reactions are typically carried out in nonpolar media. IR spectra of a series of phenols in CCl4 and 1% CD3CN/CCl4 provide relative acidities. Nonprotonated charged substituents with an appropriate counterion are found to enhance their Brønsted acidities and improve catalyst performance by orders of magnitude.

Predictive analysis of concrete slump using a stochastic search-consolidated neural network.

Attaining a dependable measurement of concrete slump is crucial as it is a valuable indication of concrete workability. On the other hand, complexities associated with costly traditional approaches have driven engineers to use indirect efficient models such as metaheuristic-based machine learning for approximating the slump. While the literature shows promising application of some metaheuristic techniques for this purpose, the large variety of these algorithms calls for evaluating the most capable ones to keep the solution updated. Stochastic fractal search (SFS) is one of the most powerful optimization algorithms in the literature that has not received appropriate attention in analyzing concrete mechanical parameters. In the present research, a multi-layer perceptron neural network (NN-MLP), is enhanced using the SFS. The proposed SFS-NN-MLP model aims to predict the slump based on the amount of ingredients in the mixture, as well as the curing age of specimens. Accuracy assessment revealed that the proposed model can deal with the assigned task with excellent accuracy. It indicates that the SFS could properly tune the parameters required for training the NN-MLP, and consequently, the trained network could reliably calculate the slump of specimens that were not analyzed before. For comparative validation, the SFS was replaced with two similar optimizers, namely elephant herding optimization algorithm (EHO) and slime mould algorithm (SMA). Based on the calculated mean square errors of 5.6526, 6.1129, and 7.3561 along with mean absolute errors of 4.6657, 5.0078, and 6.3066, as well as the percentage-Pearson correlation coefficients of 78.06 %, 73.95 %, and 58.11 %, respectively for the SFS-NN-MLP, EHO-NN-MLP, and SMA-NN-MLP, it was shown that the SFS-NN-MLP is the most accurate predictor. Hereupon, the SFS-NN-MLP model is recommended to be effectively used for obtaining a cost-efficient approximation of concrete slump in real-world projects.