hITeQ: A new workflow-based computing environment for streamlining discovery. Application in materials science

[EN] This paper presents the implementation of the recent methodology called Adaptable Time Warping (ATW) for the automatic identification of mixture of crystallographic phases from powder X-ray diffraction data, inside the framework of a new integrative platform named hITeQ. The methodology is encapsulated into a so-called workflow, and we explore the benefits of such an environment for streamlining discovery in R&D. Beside the fact that ATW successfully identifies and classifies crystalline phases from powder XRD for the very complicated case of zeolite ITQ-33 for which has been employed a high throughput synthesis process, we stress on the numerous difficulties encountered by academic laboratories and companies when facing the integration of new software or techniques. It is shown how an integrative approach provides a real asset in terms of cost, efficiency, and speed due to a unique environment that supports well-defined and reusable processes, improves knowledge management, and handles properly multi-disciplinary teamwork, and disparate data structures and protocols.EU Commission FP6 (TOPCOMBI Project) is gratefully acknowledged.Baumes, LA.; Jiménez Serrano, S.; Corma Canós, A. (2011). hITeQ: A new workflow-based computing environment for streamlining discovery. Application in materials science. Catalysis Today. 159(1):126-137. doi:10.1016/j.cattod.2010.03.067S126137159

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures

An integrated optical Bragg grating refractometer for volatile organic compound detection

We report an integrated optical Bragg grating detector, fabricated using a direct UV-writing approach, that when coated with a thin-film of a hydrophobic siloxane co-polymer can perform as an all-optically accessed detector for hydrocarbon vapour. Upon exposure to a series of organic solvent vapours, both negative and positive Bragg wavelength shifts of differing magnitudes were measured. This was attributed to a combination of swelling and/or hydrocarbon solvent filling the free volume within the polymer film. A quantitative structural property relationship (QSPR) approach was utilised to create a multiple variable linear regression model, built from parameters that chemically described the hydrocarbons and the intermolecular interactions present between the co-polymer and hydrocarbon molecules. The resulting linear regression model indicated that the degree of swelling of the polysiloxane thin film when exposed to vapours of different hydrocarbons was due to the physico-chemical properties of the hydrocarbons and that this was the main causative factor of the measured Bragg wavelength shifts. Furthermore, this linear regression model allows for the prediction of the Bragg wavelength shift that would be measured upon exposure to vapours of another defined hydrocarbon. This detector is intrinsically safe in flammable environments. It includes on-chip thermal compensation, operates at telecoms wavelengths and has a predictable response to a variety of hydrocarbons making it ideal for detection of flammable hydrocarbon vapours in industrial and domestic processes

Recent Perspectives in Pyrolysis Research

Recent Perspectives in Pyrolysis Research presents and discusses different routes of pyrolytic conversions. It contains exhaustive and comprehensive reports and studies of the use of pyrolysis for energy and materials production and waste management

Machine learning to empower electrohydrodynamic processing

Electrohydrodynamic (EHD) processes are promising healthcare fabrication technologies, as evidenced by the number of commercialised and food-and-drug administration (FDA)-approved products produced by these processes. Their ability to produce both rapidly and precisely nano-sized products provides them with a unique set of qualities that cannot be matched by other fabrication technologies. Consequently, this has stimulated the development of EHD processing to tackle other healthcare challenges. However, as with most technologies, time and resources will be needed to realise fully the potential EHD processes can offer. To address this bottleneck, researchers are adopting machine learning (ML), a subset of artificial intelligence, into their workflow. ML has already made ground-breaking advancements in the healthcare sector, and it is anticipated to do the same in the materials domain. Presently, the application of ML in fabrication technologies lags behind other sectors. To that end, this review showcases the progress made by ML for EHD workflows, demonstrating how the latter can benefit greatly from the former. In addition, we provide an introduction to the ML pipeline, to help encourage the use of ML for other EHD researchers. As discussed, the merger of ML with EHD has the potential to expedite novel discoveries and to automate the EHD workflow

DFT-Derived Location and Hydroxide Coordination of Lanthanum Ions in Zeolite Y Structures

Fluid Catalytic Cracking is a process of great interest in oil refining. This process is governed by the stability and activity of acid sites held within zeolite frameworks. Rare earth exchanged, specifically lanthanum exchanged, zeolite Y is known to have increased resistance to framework dealumination. This study examines lanthanum exchanged zeolite Y (Si/Al = 3) through the use of density functional theory calculations with the purpose of elucidating the location and nature of La species held within the zeolite framework. This is accomplished through calculation of silicon chemical shifts to determine the arrangement of Al atoms in the framework. Followed by stability calculations for the position and hydroxide coordination of La(OH)X (X is the number of OH groups and ranges from 0 to 2) species at each of the seven unique ion exchange position in zeolite Y. The most stable single La position is found to be at the center of the hexagonal prism (site I) as a bare La3+ cation, followed closely by LaOH located atop the hexagonal prism (site I’). Lanthanum clusters of three are not preferred. However, La clusters of two inside the sodalite cage are preferred over all other lanthanum orientations. After finding the most stable positions, proton chemical shifts were calculated for lanthanum containing structures and compared to corresponding deprotonation energies, however, no trend between the two is found. Values of deprotonation energy for H connected to La molecules are too high to be strong Bronsted acid sites, though they could potentially act as strong Lewis acid sites

Into the Unknown: How Computation Can Help Explore Uncharted Material Space

Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials

Ab initio design of efficient zeolite catalysts for methanol and hydrocarbons conversion

[ES] Toda esta disertación ha utilizado la química computacional como herramienta fundamental para el análisis científico. Por ello, en el Capítulo 2 se explican los modelos y métodos teóricos sobre este tema. La primera parte del capítulo se centra en los fundamentos de la química cuántica y, en concreto, se explica con detalle la Teoría del Funcional de la Densidad la cual constituye la base de los métodos computacionales aplicados. En esta sección, las nociones básicas del método Hartree-Fock sirven de prólogo a la DFT. El Capítulo 3 presenta los primeros resultados de este trabajo correspondientes a la reacción de metanol a olefinas catalizada por diferentes zeolitas con cavidades de poro pequeño. Esta reacción es un proceso industrial relevante que produce olefinas de cadena corta como eteno (C2=), propeno (C3=) y buteno (C4=) a escala industrial a partir de la biomasa. El sistema catalítico comprende tanto la estructura inorgánica de la zeolita que contiene los sitios ácidos Brønsted como las especies orgánicas confinadas, que forman la "hydrocarbon pool" y producen olefinas ligeras mediante pasos sucesivos de metilación y craqueo. Hemos centrado nuestros esfuerzos en comprender la naturaleza de la "hydrocarbon pool", una molécula de benceno polimetilada, y sus mecanismos de reacción para poder discernir entre ellos e identificar los catalizadores adecuados para mejorar la producción de propeno o eteno en función de la topología de cada cavidad zeolitica. Hemos podido identificar el grado de metilación de la "hydrocarbon pool" como el factor clave para potenciar el mecanismo de la ruta "paring", donde el propeno es el producto mayoritario, o el mecanismo de la ruta "side-chain", siendo el eteno el producto predominante. Este hallazgo nos permite establecer una relación entre la estabilización de los dos intermedios clave y la selectividad experimental observada con un alto grado de correlación. En el Capitulo 4 presentamos una nueva herramienta para el estudio de reacciones competitivas catalizadas por zeolitas. Utilizando un cribado computacional rápido con "force fields" para los intermedios clave de la reacción y un detallado estudio mecanístico usando la teoría del funcional de la densidad somos capaces de reconocer y cuantificar sutiles diferencias en la estabilización de intermedios y estados de transición dentro de huecos microporosos similares, aproximándonos así al nivel de reconocimiento molecular de las enzimas. Con estas herramientas somos capaces de seleccionar como catalizador una zeolita que obstaculice el mecanismo "alkyl-transfer" reduciendo la producción de eteno no deseado y potenciando al mismo tiempo el mecanismo "diaryl-mediated pathway". También somos capaces de obstaculizar la desproporción de dietilbenceno, una ruta no deseada del mecanismo "diaryl-mediated pathway" que conduce a la producción de trietilbenceno, mientras que se favorece la transalquilación de dietilbenceno aumentando el rendimiento obtenido de etilbenceno. en la primera sección del Capítulo 5, estudiamos la afinidad energética de cationes alquilamonio comercialmente disponibles con ligeras diferencias en sus grupos alquilo, TEA, MTEA y DMDEA, para la síntesis de CHA y sus efectos sobre la calidad del material obtenido. Evaluamos las energías de interacción entre la zeolita y el catión de diferentes combinaciones de agentes directores y cationes Na+ con métodos DFT periódicos pudiendo distinguir pequeños efectos de estabilización causados por ligeras diferencias estructurales entre moléculas que repercuten en la estructura final sintetizada. Durante la segunda sección del Capítulo 5, identificamos las características estructurales de diferentes agentes directores de estructura para la síntesis de AEI que mejoran las probabilidades de dispersión del Al en posiciones tetraédricas distintas de T1 obteniendo un catalizador AEI diferente de los sintetizados clásicamente.[CA] Tota aquesta dissertació utilitza la química computacional com eina fonamental per a l'anàlisi científica. Per això, en el Capítol 2 s'expliquen els models i mètodes teòrics sobre aquest tema. La primera part del capítol es centra en els fonaments de la química quàntica i, en concret, s'explica amb detall la Teoria del Funcional de la Densitat la qual constitueix la base dels mètodes computacionals aplicats. En aquesta secció, les nocions bàsiques del mètode Hartree-Fock serveixen de pròleg a la DFT. El Capítol 3 presenta els primers resultats d'aquest treball corresponents a la reacció de metanol a olefines catalitzada per diferents zeolites amb cavitats de porus petit. Aquesta reacció és un procés industrial rellevant que produeix olefines de cadena curta com etè (C2=), propè (C3=) i butè (C4=) a escala industrial a partir de la biomassa. El sistema catalític comprèn tant l'estructura inorgànica de la zeolita que conté els llocs àcids Brønsted com les espècies orgàniques confinades, que formen la "hydrocarbon pool" i produeixen olefines lleugeres mitjançant passos successius de metilació i craqueig. Hem centrat els nostres esforços en comprendre la naturalesa de la "hydrocarbon pool", una molècula de benzè polimetilada, i els seus mecanismes de reacció per a poder discernir entre ells i identificar els catalitzadors adequats per millorar la producció de propè o etè en funció de la topologia de cada cavitat zeolitica. Hem pogut identificar el grau de metilació de la "hydrocarbon pool" com el factor clau per a potenciar el mecanisme de la ruta "paring", on el propè és el producte majoritari, o el mecanisme de la ruta "side-chain", sent l'etè el producte predominant. Al Capítol 4 presentem una nova eina per a l'estudi de reaccions competitives catalitzades per zeolites. Utilitzant un cribratge computacional ràpid amb "force fields" per als intermedis clau de la reacció i un detallat estudi mecanístic amb la teoria del funcional de la densitat som capaços de reconèixer i quantificar subtils diferències en l'estabilització d'intermedis i estats de transició dins de buits microporosos similars, aproximant-nos així al nivell de reconeixement molecular dels enzims. en la primera secció del Capítol 5, estudiem l'afinitat energètica de cations alquilamoni comercialment disponibles amb lleugeres diferències als seus grups alquil, TEA, MTEA i DMDEA, per a la síntesi de CHA i els seus efectes sobre la qualitat del material obtingut. Avaluem les energies d'interacció entre la zeolita i el catió entre diferents combinacions d'agents directors i cations Na+ amb mètodes DFT periòdics podent distingir petits efectes d'estabilització causats per lleugeres diferències estructurals entre molècules que repercuteixen en l'estructura final sintetitzada. Durant la segona secció del Capítol 5, identifiquem les característiques estructurals de diferents agents directors d'estructura per a la síntesi d'AEI que milloren les probabilitats de propagació de l'Al a través de posicions tetrahedriques diferents de T1 obtenint un catalitzador AEI diferent dels sintetitzats clàssicament.[EN] Computational chemistry has been used as the fundamental tool during the whole work. Therefore, the theoretical models and methods on this subject are explained in Chapter 2. The first part sketches the fundamentals of quantum chemistry and specifically explains the Density Functional Theory that constitutes the basis of the computational methods applied. In this section, basic notions of the Hartree-Fock method serve as prologue for DFT after which more practical aspects are elucidated. Chapter 3 presents the first results of this work corresponding to the methanol to olefins reaction catalysed by different small-pore cage-like zeolites. This reaction is a relevant process that produces short chain olefins such as ethene, propene and butene at industrial scale from biomass. The catalytic system comprises both the zeolite inorganic framework containing the Brønsted acid sites and the confined organic species, that form the hydrocarbon pool and produce light olefins by successive methylation and cracking steps. Our efforts are focused on understanding the nature of the hydrocarbon pool, a polymethylated benzene molecule, and its reaction mechanisms in order to be able to discern between them and identify the proper catalysts to enhance propene or ethene production based on each zeolite cavity topology. We have been able to identify the hydrocarbon pool methylation degree as the key factor to enhance paring route mechanism where propene is the predominant product, or side-chain mechanism, with ethene being the predominant product. This finding enables us to establish a relation between the stabilization of the two key intermediates and the experimental selectivity observed with a high degree of correlation. In Chapter 4 we present a new tool for the study of competing reactions catalyzed by zeolites. Using a fast computational screening with force fields for the key intermediates of the reaction and a detailed density functional theory mechanistic study we are able to recognize and quantify subtle differences in the stabilization of intermediates and transition states within similar microporous voids, thus approaching the level of molecular recognition of enzymes. With these tools we are able to select a zeolite catalyst that hinders alkyl-transfer mechanism reducing the production of non-desired ethene while enhancing the diaryl-mediated pathyways mechanism. Once we discard the non-desired mechanism, we are also able to hinder the diethylbenzene disproportionation, a non-desired route of the diaryl-mediated pathways that leads to triethylbenzene production, while favouring diethylbenzene transalkylation increasing the obtained yield of ethylbenzene. To close this chapter, the theoretical results are compared with experimental selectivities obtained for eight candidate zeolites obtaining a good correlation between theory and experiment. in the first section of Chapter 5, we study the energetic affinity of commercially available alkylammonium cations with slight differences on their alkyl chain groups, as TEA, MTEA and DMDEA, for CHA synthesis and its effects on the quality of the material obtained. We evaluate the host-guest interaction energies of different combinations of OSDAs and Na+ cations with periodic DFT methods being able to distinguish small stabilization effects caused by slight structural differences between molecules that have an impact on the final structure synthesized. On the other hand, we present a new theoretical methodology to address Al positioning prediction in SSZ-39 zeolite with the AEI framework. During the second section of Chapter 5, we identify the structural features of different OSDAs for AEI synthesis that improve the probabilities of spreading Al through different T-site positions other than T1 obtaining an AEI catalyst different from the classically synthesized.Vull agrair al Instituto de Tecnología Química per la concessió d’un contracte predoctoral, a la Red Española de Supercomputación (RES), al Centre de Càlcul de la Universitat de València, al Flemish Supercomputer Center (VSC) de la Ghent University pels recursos computacionals i el suport tècnic, a la Unió Europea i al Gobierno de España pel finançament d’aquest projecte a traves dels programes ERC-AdG-2014- 671093 (SynCatMatch) “Severo Ochoa” (SEV-2016-0683, MINECO) i dels projectes MAT2017-82288-C2-1-P i PID2020-112590GB-C21 (AEI/FEDER, UE), i al CSIC pel finançament de la estada al CMM a través del projecte i- Link (LINKA20381).Ferri Vicedo, P. (2023). Ab initio design of efficient zeolite catalysts for methanol and hydrocarbons conversion [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19349

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre- and post-synthetic computer simulations are mostly carried out in a piecemeal and ad hoc manner. Whilst high throughput approaches have been used for more than 30 years in the porous material fields, routine integration of experimental and computational processes is only now becoming more established. Herein, important developments are highlighted and emerging challenges for the community identified, including the need to work toward more integrated workflows