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In this work, we demonstrate that amorphous and porous molybdenum silicate microspheres are highly active catalysts for heterogeneous propylene metathesis. Homogeneous molybdenum silicate microspheres and aluminum-doped molybdenum silicate microspheres were synthesized via a nonaqueous condensation of a hybrid molybdenum biphenyldicarboxylate-based precursor solution with (3-aminopropyl)triethoxysilane. The as-prepared hybrid metallosilicate products were calcined at 500 °C to obtain amorphous and porous molybdenum silicate and aluminum-doped molybdenum silicate microspheres with highly dispersed molybdate species inserted into the silicate matrix. These catalysts contain mainly highly dispersed MoOx species, which possess high catalytic activity in heterogeneous propylene metathesis to ethylene and butene. Compared to conventional silica-supported MoOx catalysts prepared via incipient wetness impregnation (MoIWI), the microspheres with low Mo content (1.5-3.6 wt %) exhibited nearly 2 orders of magnitude higher steady-state propylene metathesis rates at 200 °C, approaching site time yields of 0.11 s-1

[EN] Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA-zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates.

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Chemistry of Materials, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.chemmater.9b02696., [EN] Ge-containing zeolites were used as precursors for the synthesis of highly conductive and stable hybrid electronic materials by postsynthetic thermal treatment of the crystals in the presence of an olefin. Treating the as-prepared Ge zeolites in 1-butene at 700 degrees C formed a graphitic matrix within and outside the crystals due to the thermal degradation of the organic structure-directing agent inside the pores and the polymerization of the olefin. Importantly, these conditions forced Ge out of the framework, leading to the collapse of the crystalline structure and subsequent formation of metallic Ge nanoparticles distributed either as small, well-dispersed nanoparticles within the silica matrix or larger carbon-coated core-shell Ge@C nanoparticles on the external surface of the carbon-silica composite. Varying the zeolite topology influenced the size of the Ge@C nanoparticles, with those obtained using the multipore zeolite ITQ-22 (IWW, 12 x 10 x 8 rings) featuring smaller sizes (30-60 nm) than those obtained with the large-pore zeolite ITQ33 (ITT, 18 x 10 x 10 rings) (80-120 nm), where the lack of diffusional limitations increased metal sintering rates. The zeolite topology also influenced the final carbon content and dispersion of the Ge nanoparticles. The best performing Ge-based hybrid material was obtained by thermal treatment of ITQ-22 (Si/Ge = 4) at 700 degrees C in 1-butene. Unlike Ge-free hybrid controls, an ITQ L -22 (Si/Ge = 4) sample treated at 700 degrees C in 1-butene showed a conductivity value of similar to 2 S/m (measured at 1 V), which is in the range of a commercially available graphene. The simple methodology presented here is an alternative route for the efficient preparation of highly stable Ge-based hybrid composites with excellent conductivity for potential use as high-capacity electrodes.

[EN] Zeolites are porous, aluminosilicate materials with many industrial and "green" applications. Despite their industrial relevance, many aspects of zeolite synthesis remain poorly understood requiring costly trial and error synthesis. In this paper, we create natural language processing techniques and text markup parsing tools to automatically extract synthesis information and trends from zeolite journal articles. We further engineer a data set of germanium-containing zeolites to test the accuracy of the extracted data and to discover potential opportunities for zeolites containing germanium. We also create a regression model for a zeolite's framework density from the synthesis conditions. This model has a cross-validated root mean squared error of 0.98 T/1000 angstrom(3) , and many of the model decision boundaries correspond to known synthesis heuristics in germanium-containing zeolites. We propose that this automatic data extraction can be applied to many different problems in zeolite synthesis and enable novel zeolite morphologies.

[EN] CONSPECTUS: Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined by multiple interconnected variables (e.g., reagent sources, molar ratios, aging treatments, reaction time and temperature, among others), but unfortunately their distinctive influence, particularly on the nucleation and crystallization processes, is still far from being understood. Thus, the discovery and/or optimization of specific zeolites is closely related to the exploration of the parametric space through trial-and-error methods, generally by studying the influence of each parameter individually. In the past decade, machine learning (ML) methods have rapidly evolved to address complex problems involving highly nonlinear or massively combinatorial processes that conventional approaches cannot solve. Considering the vast and interconnected multiparametric space in zeolite synthesis, coupled with our poor understanding of the mechanisms involved in their nucleation and crystallization, the use of ML is especially timely for improving zeolite synthesis. Indeed, the complex space of zeolite synthesis requires draWing inferences from incomplete and imperfect information, for which ML methods are very well-suited to replace the intuition-based approaches traditionally used to guide experimentation. In this Account, we contend that both existing and new ML approaches can provide the "missing link" needed to complete the traditional zeolite synthesis workflow used in our quest to rationalize zeolite synthesis. Within this context, we have made important efforts on developing ML tools in different critical areas, such as (1) data-mining tools to process the large amount of data generated using high-throughput platforms; (2) novel complex algorithms to predict the formation of energetically stable hypothetical zeolites and guide the synthesis of new zeo

[EN] Conversion of carbohydrates to 5-(hydroxymethyl)furfural (HMF) may provide a step forward toward achieving a renewable biomass-based chemicals and fuels platform. Recently, we reported that a tin-containing, high-silica molecular sieve with the zeolite beta topology (Sn-Beta) can efficiently catalyze the isomerization of glucose to fructose in aqueous media at low pH. Herein, we describe the combination of Sn-Beta with acid catalysts in a one vessel, biphasic reactor system to synthesize HMF from carbohydrates such as glucose, cellobiose, and starch with high efficiency. HMF selectivities over 70% were obtained using this ¿one-pot¿ biphasic water/tetrahydrofuran (THF) reactor system. The key to successfully achieving the conversions/selectivities reported is that Sn-Beta is able to convert glucose to fructose at pH near 1 and in saturated aqueous salt solutions.