Hybrid Organic−Inorganic Solids That Show Shape Selectivity

Hybrid organic−inorganic solids featuring millimolar/gram concentrations of intracrystalline organic moieties and shape-selectivity are synthesized. Pure-silica zeolite beta crystals are coated with zirconia and treated in aqueous sodium hydroxide to create defects and mesoporosity within the crystalline structure. Aminopropyl organic groups are subsequently grafted onto the generated intracrystalline silanol groups. After grafting, characterization data indicate a high organic concentration localized primarily within the intracrystalline voids. Specifically, thermogravimetric analysis shows an organic loading of 0.7 mmol of NH_2/g, ^(29)Si solid-state nuclear magnetic resonance (NMR) spectra display a quantitative decrease in Q^3 silicon atoms with a corresponding resharpening of the Q^4 resonances, and N_2 adsorption data show a decrease in micropore volume to 0.10 cm^3/g. Knoevenagel condensation reactions are catalyzed by the aminopropyl-functionalized materials using differently sized aldehydes and the results show that the zirconia-protected functionalized solid have shape selective properties

Mechanism of Glucose Isomerization Using a Solid Lewis Acid Catalyst in Water

^1H and ^(13)C NMR spectroscopy on isotopically labeled glucose reveals that in the presence of tin-containing zeolite Sn-Beta, the isomerization reaction of glucose in water proceeds by way of an intramolecular hydride shift (see scheme) rather than proton transfer. This is the first mechanistic demonstration of Sn-Beta acting as a Lewis acid in a purely aqueous environment

Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

[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 zeolite structures; (3) new "ab initio" organic structure directing agent predictions to direct the synthesis of hypothetical or known zeolites; (4) an automated tool for nonsupervised data extraction and classification from published research articles. ML has already revolutionized many areas in materials science by enhancing our ability to map intricate behavior to process variables, especially in the absence of well-understood mechanisms. Undoubtedly, ML is a burgeoning field with many future opportunities for further breakthroughs to advance the design of molecular sieves. For this reason, this Account includes an outlook of future research directions based on current challenges and opportunities. We envision this Account will become a hallmark reference for both well-established and new researchers in the field of zeolite synthesis.This work has been supported by the EU through ERC-AdG2014-671093, by the Spanish Government through SEV-20160683 and RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE), and by La Caixa-Foundation through MIT -SPAIN MISTI program (LCF/PR/MIT17/11820002). Y.R.-L. thanks the DoE for funding through the Office of Basic Energy Sciences (DE-SC0016214).Moliner Marin, M.; Román-Leshkov, Y.; Corma Canós, A. (2019). Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery. Accounts of Chemical Research. 52(10):2971-2980. https://doi.org/10.1021/acs.accounts.9b00399S29712980521

Supported molybdenum oxides as effective catalysts for the catalytic fast pyrolysis of lignocellulosic biomass

The catalytic fast pyrolysis (CFP) of pine was investigated over 10 wt% MoO[subscript 3]/TiO[subscript 2] and MoO[subscript 3]/ZrO[subscript 2] at 500 °C and H[subscript 2] pressures ≤0.75 bar. The product distributions were monitored in real time using a molecular beam mass spectrometer (MBMS). Both supported MoO[subscript 3] catalysts show different levels of deoxygenation based on the cumulative biomass to MoO[subscript 3] mass ratio exposed to the catalytic bed. For biomass to MoO[subscript 3] mass ratios <1.5, predominantly olefinic and aromatic hydrocarbons are produced with no detectable oxygen-containing species. For ratios ≥1.5, partially deoxygenated species comprised of furans and phenols are observed, with a concomitant decrease of olefinic and aromatic hydrocarbons. For ratios ≥5, primary pyrolysis vapours break through the bed, indicating the onset of catalyst deactivation. Product quantification with a tandem micropyrolyzer–GCMS setup shows that fresh supported MoO[subscript 3] catalysts convert ca. 27 mol% of the original carbon into hydrocarbons comprised predominantly of aromatics (7 C%), olefins (18 C%) and paraffins (2 C%), comparable to the total hydrocarbon yield obtained with HZSM-5 operated under similar reaction conditions. Post-reaction XPS analysis on supported MoO[subscript 3]/ZrO[subscript 2] and MoO[subscript 3]/TiO[subscript 2] catalysts reveal that ca. 50% of Mo surface species exist in their partially reduced forms (i.e., Mo5[superscript +] and Mo3[superscript +]), and that catalyst deactivation is likely associated to coking.BP (Firm) (MIT Energy Initiative. Advanced Conversion Research Program)National Science Foundation (U.S.) (Award 1454299

Ge-Based Hybrid Composites from Ge-Rich Zeolites as Highly Conductive and Stable Electronic Materials

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.This work has been supported by the Spanish Government through 'Severo Ochoa"(SEV-2016-0683, MINECO) and RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE) and by La Caixa Foundation through MIT-SPAIN SEED FUND (LCF/PR/MIT17/11820002). A.R.F. acknowledges the Spanish Government-MINECO for a FPU scholarship (FPU2017/01521). The Electron Microscopy Service of the UPV is acknowledged for their help in sample characterization. We thank Javier van Herpt for the cyclic voltammetry experiment.Rodríguez-Fernández, A.; Atienzar Corvillo, PE.; Martínez, C.; Román-Leshkov, Y.; Moliner Marin, M. (2019). Ge-Based Hybrid Composites from Ge-Rich Zeolites as Highly Conductive and Stable Electronic Materials. Chemistry of Materials. 31(18):7723-7731. https://doi.org/10.1021/acs.chemmater.9b02696S77237731311

A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction

[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.We would like to acknowledge funding from the National Science Foundation Award No. 1534340, DMREF that provided support to make this work possible, support from the Office of Naval Research (ONR) under Contract No. N00014-16-1-2432, and the MIT Energy Initiative. Early work was collaborative under the Department of Energy Basic Energy Science Program through the Materials Project under Grant No. EDCBEE. This work has also been supported by the Spanish Government through the Severo Ochoa Program SEV-2016-0683 and the Grant No. MAT2015971261-R, and by La Caxia Foundation through the MIT-SPAIN SEED FUND Program (LCF/PR/MIT17/11820002).Jensen, Z.; Kim, E.; Kwon, S.; Gani, TZ.; Román-Leshkov, Y.; Moliner Marin, M.; Corma Canós, A.... (2019). A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction. ACS Central Science. 5(5):892-899. https://doi.org/10.1021/acscentsci.9b00193S8928995

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure-Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx

[EN] A novel ab initio methodology based on high-throughput simulations has permitted designing unique biselective organic structure-directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well-established commercial CHA-type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties.E.B.-J., C.P., M.M. and A.C. acknowledge financial support by the Spanish Government [Grant RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE)], and by CSIC [I-link+ Program (LINKA20381)]. D.S.-K. and R.G.-B. acknowledge the Energy Initiative (MITEI) and MIT International Science and Technology Initiatives (MISTI) Seed Funds. D.S.-K. was additionally funded by the MIT Energy Fellowship. Y.R.-L acknowledges support by the U.S. Department of Energy, Office of Basic Energy Sciences under Award DE-SC0016214. E.B.-J. acknowledges the Spanish Government for an FPI scholarship (PRE2019-088360). T.W. acknowledges financial support by the Swedish Research Council (Grant No. 2019-05465). T.W. and T.U. acknowledge funding from the Swedish Strategic Res. Foundation (project nr. ITM17-0301). The Electron Microscopy Service of the UPV is also acknowledged for their help in sample characterization. Computer calculations were executed at the Massachusetts Green High-Performance Computing Center with support from MIT Research Computing, and at the Extreme Sci. and Eng. Discovery Environment (XSEDE)[33] Expanse through allocation TG-DMR200068.Bello-Jurado, E.; Schwalbe-Koda, D.; Nero, M.; Paris, C.; Uusimäki, T.; Román-Leshkov, Y.; Corma Canós, A.... (2022). Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure-Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx. Angewandte Chemie International Edition. 61(28):1-6. https://doi.org/10.1002/anie.20220183716612

Electroactive nanoporous metal oxides and chalcogenides by chemical design

The archetypal silica- and aluminosilicate-based zeolite-type materials are renowned for wide-ranging applications in heterogeneous catalysis, gas-separation and ion-exchange. Their compositional space can be expanded to include nanoporous metal chalcogenides, exemplified by germanium and tin sulfides and selenides. By comparison with the properties of bulk metal dichalcogenides and their 2D derivatives, these open-framework analogues may be viewed as three-dimensional semiconductors filled with nanometer voids. Applications exist in a range of molecule size and shape discriminating devices. However, what is the electronic structure of nanoporous metal chalcogenides? Herein, materials modeling is used to describe the properties of a homologous series of nanoporous metal chalcogenides denoted np-MX2, where M = Si, Ge, Sn, Pb, and X = O, S, Se, Te, with Sodalite, LTA and aluminum chromium phosphate-1 structure types. Depending on the choice of metal and anion their properties can be tuned from insulators to semiconductors to metals with additional modification achieved through doping, solid solutions, and inclusion (with fullerene, quantum dots, and hole transport materials). These systems form the basis of a new branch of semiconductor nanochemistry in three dimensions

Propylene metathesis over molybdenum silicate microspheres with dispersed active sites

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-1CF CryoE; European Regional Development Fund-Project “UP CIISB, (CZ.02.1.01/0.0/0.0/18_046/0015974, LM2018110); Francqui Foundation; Grant Agency of Masaryk University, (MUNI/A/1298/2022, MUNI/J/0007/2021); U.S. Department of Energy, USDOE; Basic Energy Sciences, BES, (DE-SC0016214); Massachusetts Institute of Technology, MIT; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT, (LM2023042, RP/CPS/2022/007); Grantová Agentura České Republiky, GA ČR, (GJ20-03636Y); Central European Institute of Technology, CEITECMinistry of Education, Youth, and Sports of the Czech Republic within the INTER-EXCELLENCE II program; Ministry of Education, Youth, and Sports of the Czech Republic [RP/CPS/2022/007]; U.S. Department of Energy, Office of Basic Energy Sciences [DE-SC0016214]; European Regional Development Fund-Project "UP CIISB" [CZ.02.1.01/0.0/0.0/18_046/0015974, LM2018110]; MEYS CR [GJ20-03636Y, LM2023042]; Czech Science Foundation; Grant Agency of Masaryk University [MUNI/J/0007/2021, MUNI/A/1298/2022]; Francqui Foundation for the Francqui Research Professor chai

Stable and Active Oxygen Reduction Catalysts with Reduced Noble Metal Loadings through Potential Triggered Support Passivation

The development of stable, cost‐efficient and active materials is one of the main challenges in catalysis. The utilization of platinum in the electroreduction of oxygen is a salient example where the development of new material combinations has led to a drastic increase in specific activity compared to bare platinum. These material classes comprise nanostructured thin films, platinum alloys, shape‐controlled nanostructures and core–shell architectures. Excessive platinum substitution, however, leads to structural and catalytic instabilities. Herein, we introduce a catalyst concept that comprises the use of an atomically thin platinum film deposited on a potential‐triggered passivating support. The model catalyst exhibits an equal specific activity with higher atom utilization compared to bulk platinum. By using potential‐triggered passivation of titanium carbide, irregularities in the Pt film heal out via the formation of insoluble oxide species at the solid/liquid interface. The adaptation of the described catalyst design to the nanoscale and to high‐surface‐area structures highlight the potential for stable, passivating catalyst systems for various electrocatalytic reactions such as the oxygen reduction reaction