1
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Ezenwa S, Gounder R. Advances and challenges in designing active site environments in zeolites for Brønsted acid catalysis. Chem Commun (Camb) 2024. [PMID: 39344420 DOI: 10.1039/d4cc04728a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Zeolites contain proton active sites in diverse void environments that stabilize the reactive intermediates and transition states formed in converting hydrocarbons and oxygenates to chemicals and energy carriers. The catalytic diversity that exists among active sites in voids of varying sizes and shapes, even within a given zeolite topology, has motivated research efforts to position and quantify active sites within distinct voids (synthesis-structure) and to link active site environment to catalytic behavior (structure-reactivity). This Feature Article describes advances and challenges in controlling the position of framework Al centers and associated protons within distinct voids during zeolite synthesis or post-synthetic modification, in identifying and quantifying distinct active site environments using characterization techniques, and in determining the influence of active site environments on catalysis. During zeolite synthesis, organic structure directing agents (SDAs) influence Al substitution at distinct lattice positions via intermolecular interactions (e.g., electrostatics, hydrogen bonding) that depend on the size, structure, and charge distribution of organic SDAs and their mobility when confined within zeolitic voids. Complementary post-synthetic strategies to alter intrapore active site distributions include the selective removal of protons by differently-sized titrants or unreactive organic residues and the selective exchange of framework heteroatoms of different reactivities, but remain limited to certain zeolite frameworks. The ability to identify and quantify active sites within distinct intrapore environments depends on the resolution with which a given characterization technique can distinguish Al T-site positions or proton environments in a given zeolite framework. For proton sites in external unconfined environments, various (post-)synthetic strategies exist to control their amounts, with quantitative methods to distinguish them from internal sites that largely depend on using stoichiometric or catalytic probes that only interact with external sites. Protons in different environments influence reactivity by preferentially stabilizing larger transition states over smaller precursor states and influence selectivity by preferentially stabilizing or destabilizing competing transition states of varying sizes that share a common precursor state. We highlight opportunities to address challenges encountered in the design of active site environments in zeolites by closely integrating precise (post-)synthetic methods, validated characterization techniques, well-defined kinetic probes, and properly calibrated theoretical models. Further advances in understanding the molecular details that underlie synthesis-structure-reactivity relationships for active site environments in zeolite catalysis can accelerate the predictive design of tailored zeolites for desired catalytic transformations.
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Affiliation(s)
- Sopuruchukwu Ezenwa
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47907, USA.
| | - Rajamani Gounder
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47907, USA.
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2
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Ata K, Mineva T, Alonso B. Strength of London Dispersion Forces in Organic Structure Directing Agent-Zeolite Assemblies. Molecules 2024; 29:4489. [PMID: 39339483 PMCID: PMC11434474 DOI: 10.3390/molecules29184489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 09/13/2024] [Accepted: 09/17/2024] [Indexed: 09/30/2024] Open
Abstract
Herein, we study the London dispersion forces between organic structure directing agents (OSDAs)-here tetraalkyl-ammonium or -phosphonium molecules-and silica zeolite frameworks (FWs). We demonstrate that the interaction energy for these dispersion forces is correlated to the number of H atoms in OSDAs, irrespective of the structures of OSDAs or FWs, and of variations in charges and thermal motions. All calculations considered-DFT-D3 and BOMD undertaken by us, and molecular mechanics from an accessible database-led to the same trend. The mean energy of these dispersion forces is ca. -2 kcal.mol-1 per H for efficient H-O contacts.
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Affiliation(s)
- Karima Ata
- ICGM, Université de Montpellier, CNRS, ENSCM, CEDEX 05, 34293 Montpellier, France
| | - Tzonka Mineva
- ICGM, Université de Montpellier, CNRS, ENSCM, CEDEX 05, 34293 Montpellier, France
| | - Bruno Alonso
- ICGM, Université de Montpellier, CNRS, ENSCM, CEDEX 05, 34293 Montpellier, France
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3
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Kowalska D, Sosnowska A, Zdybel S, Stepnik M, Puzyn T. Predicting bioconcentration factors (BCFs) for per- and polyfluoroalkyl substances (PFAS). CHEMOSPHERE 2024; 364:143146. [PMID: 39181470 DOI: 10.1016/j.chemosphere.2024.143146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 08/06/2024] [Accepted: 08/19/2024] [Indexed: 08/27/2024]
Abstract
The bioconcentration factor (BCF) is an important parameter that gives information regarding the ability of a contaminant to be taken up by organisms from the water. Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, causing concern regarding their impact on human health. Due to the lack of available bioaccumulation data for most compounds in the PFAS group, we developed a quantitative structure-property relationship (QSPR) model to predict the log BCF for fish (taxonomic class Teleostei), based on experimental data available for the most studied 33 representatives of this group of compounds. Furthermore, we implemented the developed model to predict log BCF for an external dataset of 2209 PFAS. Consequently, 1045 PFAS were found not to be bioaccumulative, 208 were classified as bioaccumulative, and 956 were predicted to be very bioaccumulative. Finally, we obtained the high correlation (R2 = 0.844) between the log BCFs obtained in laboratory and field studies for 13 PFAS. In silico analyses indicate that PFAS bioconcentration depends on the size (chain length - number of CF2 groups in alkyl tail/chain) of a molecule, as well as on the atomic distribution properties. In general, long-chain PFAS - above 8 and 6 carbon atoms for perfluorinated carboxylic acids (PFCAs)and perfluorinated sulfonic acids (PFSAs), respectively - tend to bioconcentrate more compared to the short-chain ones. In conclusion, predicting BCF on fish is possible for a wide range of fluorinated compounds, which can be further used for estimating PFAS behavior in the environment.
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Affiliation(s)
| | - Anita Sosnowska
- QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland; Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland.
| | - Szymon Zdybel
- QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland; Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland
| | | | - Tomasz Puzyn
- QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland; Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland.
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4
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Mallette AJ, Shilpa K, Rimer JD. The Current Understanding of Mechanistic Pathways in Zeolite Crystallization. Chem Rev 2024; 124:3416-3493. [PMID: 38484327 DOI: 10.1021/acs.chemrev.3c00801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Zeolite catalysts and adsorbents have been an integral part of many commercial processes and are projected to play a significant role in emerging technologies to address the changing energy and environmental landscapes. The ability to rationally design zeolites with tailored properties relies on a fundamental understanding of crystallization pathways to strategically manipulate processes of nucleation and growth. The complexity of zeolite growth media engenders a diversity of crystallization mechanisms that can manifest at different synthesis stages. In this review, we discuss the current understanding of classical and nonclassical pathways associated with the formation of (alumino)silicate zeolites. We begin with a brief overview of zeolite history and seminal advancements, followed by a comprehensive discussion of different classes of zeolite precursors with respect to their methods of assembly and physicochemical properties. The following two sections provide detailed discussions of nucleation and growth pathways wherein we emphasize general trends and highlight specific observations for select zeolite framework types. We then close with conclusions and future outlook to summarize key hypotheses, current knowledge gaps, and potential opportunities to guide zeolite synthesis toward a more exact science.
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Affiliation(s)
- Adam J Mallette
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
| | - Kumari Shilpa
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
| | - Jeffrey D Rimer
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
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5
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Pan E, Kwon S, Jensen Z, Xie M, Gómez-Bombarelli R, Moliner M, Román-Leshkov Y, Olivetti E. ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters. ACS CENTRAL SCIENCE 2024; 10:729-743. [PMID: 38559304 PMCID: PMC10979502 DOI: 10.1021/acscentsci.3c01615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 04/04/2024]
Abstract
Zeolites, nanoporous aluminosilicates with well-defined porous structures, are versatile materials with applications in catalysis, gas separation, and ion exchange. Hydrothermal synthesis is widely used for zeolite production, offering control over composition, crystallinity, and pore size. However, the intricate interplay of synthesis parameters necessitates a comprehensive understanding of synthesis-structure relationships to optimize the synthesis process. Hitherto, public zeolite synthesis databases only contain a subset of parameters and are small in scale, comprising up to a few thousand synthesis routes. We present ZeoSyn, a dataset of 23,961 zeolite hydrothermal synthesis routes, encompassing 233 zeolite topologies and 921 organic structure-directing agents (OSDAs). Each synthesis route comprises comprehensive synthesis parameters: 1) gel composition, 2) reaction conditions, 3) OSDAs, and 4) zeolite products. Using ZeoSyn, we develop a machine learning classifier to predict the resultant zeolite given a synthesis route with >70% accuracy. We employ SHapley Additive exPlanations (SHAP) to uncover key synthesis parameters for >200 zeolite frameworks. We introduce an aggregation approach to extend SHAP to all building units. We demonstrate applications of this approach to phase-selective and intergrowth synthesis. This comprehensive analysis illuminates the synthesis parameters pivotal in driving zeolite crystallization, offering the potential to guide the synthesis of desired zeolites. The dataset is available at https://github.com/eltonpan/zeosyn_dataset.
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Affiliation(s)
- Elton Pan
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Soonhyoung Kwon
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Zach Jensen
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Mingrou Xie
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Rafael Gómez-Bombarelli
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Manuel Moliner
- Instituto
de Tecnología Química, Universitat Politècnica de València-Consejo Superior
de Investigaciones Científicas 46022, Valencia, Spain
| | - Yuriy Román-Leshkov
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Elsa Olivetti
- Department
of Materials Science and Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, United States
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6
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Wijerathne A, Sawyer A, Daya R, Paolucci C. Competition between Mononuclear and Binuclear Copper Sites across Different Zeolite Topologies. JACS AU 2024; 4:197-215. [PMID: 38274255 PMCID: PMC10806779 DOI: 10.1021/jacsau.3c00632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/12/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024]
Abstract
A key challenge for metal-exchanged zeolites is the determination of metal cation speciation and nuclearity under synthesis and reaction conditions. Copper-exchanged zeolites, which are widely used in automotive emissions control and potential catalysts for partial methane oxidation, have in particular evidenced a wide variety of Cu structures that are observed to change with exposure conditions, zeolite composition, and topology. Here, we develop predictive models for Cu cation speciation and nuclearity in CHA, MOR, BEA, AFX, and FER zeolite topologies using interatomic potentials, quantum chemical calculations, and Monte Carlo simulations to interrogate this vast configurational and compositional space. Model predictions are used to rationalize experimentally observed differences between Cu-zeolites in a wide-body of literature, including nuclearity populations, structural variations, and methanol per Cu yields. Our results show that both topological features and commonly observed Al-siting biases in MOR zeolites increase the population of binuclear Cu sites, explaining the small population of mononuclear Cu sites observed in these materials relative to other zeolites such as CHA and BEA. Finally, we used a machine learning classification model to determine the preference to form mononuclear or binuclear Cu sites at different Al configurations in 200 zeolites in the international zeolite database. Model results reveal several zeolite topologies at extreme ends of the mononuclear vs binuclear spectrum, highlighting synthetic options for realization of zeolites with strong Cu nuclearity preferences.
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Affiliation(s)
- Asanka Wijerathne
- Department
of Chemical Engineering, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Allison Sawyer
- Department
of Chemical Engineering, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Rohil Daya
- Cummins
Inc, Columbus, Indiana 47201, United States
| | - Christopher Paolucci
- Department
of Chemical Engineering, University of Virginia, Charlottesville, Virginia 22903, United States
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7
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Kayode G, Montemore MM. Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability. JACS AU 2024; 4:80-91. [PMID: 38274257 PMCID: PMC10807004 DOI: 10.1021/jacsau.3c00419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/27/2024]
Abstract
Machine learning has been successfully applied in recent years to screen materials for a variety of applications. However, despite recent advances, most screening-based machine learning approaches are limited in generality and transferability, requiring new models to be created from scratch for each new application. This is particularly apparent in catalysis, where there are many possible intermediates and transition states of interest in addition to a large number of potential catalytic materials. In this work, we developed a new machine learning framework that is built on chemical principles and allows the creation of general, interpretable, reusable models. Our new architecture uses latent variables to create a set of submodels that each take on a relatively simple learning task, leading to higher data efficiency and promoting transfer learning. This architecture infuses fundamental chemical principles, such as the existence of elements as discrete entities. We show that this architecture allows for the creation of models that can be reused for many different applications, providing significant improvements in efficiency and convenience. For example, our architecture allows simultaneous prediction of adsorption energies for many adsorbates on a broad array of alloy surfaces with mean absolute errors (MAEs) around 0.20-0.25 eV. The integration of latent variables provides physical interpretability, as predictions can be explained in terms of the learned chemical environment as represented by the latent space. Further, these latent variables also serve as new feature representations, allowing efficient transfer learning. For example, new models with useful levels of accuracy can be created with less than 10 data points, including transfer learning to an experimental data set with an MAE less than 0.15 eV. Lastly, we show that our new machine learning architecture is general and robust enough to handle heterogeneous and multifidelity data sets, allowing researchers to leverage existing data sets to speed up screening using their own computational setup.
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Affiliation(s)
- Gbolade
O. Kayode
- Department of Chemical and
Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States
| | - Matthew M. Montemore
- Department of Chemical and
Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States
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8
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Zhang X, Zhou Z, Ming C, Sun YY. GPT-Assisted Learning of Structure-Property Relationships by Graph Neural Networks: Application to Rare-Earth-Doped Phosphors. J Phys Chem Lett 2023; 14:11342-11349. [PMID: 38064589 DOI: 10.1021/acs.jpclett.3c02848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Two challenges facing machine learning tasks in materials science are data set construction and descriptor design. Graph neural networks circumvent the need for empirical descriptors by encoding geometric information in graphs. Large language models have shown promise for database construction via text extraction. Here, we apply OpenAI's Generative Pre-trained Transformer 4 (GPT-4) and the Crystal Graph Convolutional Neural Network (CGCNN) to the problem of discovering rare-earth-doped phosphors for solid-state lighting. We used GPT-4 to datamine the chemical formulas and emission wavelengths of 264 Eu2+-doped phosphors from 274 articles. A CGCNN model was trained on the acquired data set, achieving a test R2 of 0.77. Using this model, we predicted the emission wavelengths of over 40 000 inorganic materials. We also used transfer learning to fine-tune a bandgap-predicting CGCNN model for emission wavelength prediction. The workflow requires minimal human supervision and is generalizable to other fields.
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Affiliation(s)
- Xiang Zhang
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201899, People's Republic of China
| | - Zichun Zhou
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201899, People's Republic of China
| | - Chen Ming
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201899, People's Republic of China
| | - Yi-Yang Sun
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201899, People's Republic of China
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9
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Wang Y, Tong C, Liu Q, Han R, Liu C. Intergrowth Zeolites, Synthesis, Characterization, and Catalysis. Chem Rev 2023; 123:11664-11721. [PMID: 37707958 DOI: 10.1021/acs.chemrev.3c00373] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
Microporous zeolites that can act as heterogeneous catalysts have continued to attract a great deal of academic and industrial interest, but current progress in their synthesis and application is restricted to single-phase zeolites, severely underestimating the potential of intergrowth frameworks. Compared with single-phase zeolites, intergrowth zeolites possess unique properties, such as different diffusion pathways and molecular confinement, or special crystalline pore environments for binding metal active sites. This review first focuses on the structural features and synthetic details of all the intergrowth zeolites, especially providing some insightful discussion of several potential frameworks. Subsequently, characterization methods for intergrowth zeolites are introduced, and highlighting fundamental features of these crystals. Then, the applications of intergrowth zeolites in several of the most active areas of catalysis are presented, including selective catalytic reduction of NOx by ammonia (NH3-SCR), methanol to olefins (MTO), petrochemicals and refining, fine chemicals production, and biomass conversion on Beta, and the relationship between structure and catalytic activity was profiled from the perspective of intergrowth grain boundary structure. Finally, the synthesis, characterization, and catalysis of intergrowth zeolites are summarized in a comprehensive discussion, and a brief outlook on the current challenges and future directions of intergrowth zeolites is indicated.
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Affiliation(s)
- Yanhua Wang
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Chengzheng Tong
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Qingling Liu
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Rui Han
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
| | - Caixia Liu
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
- State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
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10
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Muroga S, Miki Y, Hata K. A Comprehensive and Versatile Multimodal Deep-Learning Approach for Predicting Diverse Properties of Advanced Materials. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302508. [PMID: 37357977 PMCID: PMC10460884 DOI: 10.1002/advs.202302508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/08/2023] [Indexed: 06/27/2023]
Abstract
A multimodal deep-learning (MDL) framework is presented for predicting physical properties of a ten-dimensional acrylic polymer composite material by merging physical attributes and chemical data. The MDL model comprises four modules, including three generative deep-learning models for material structure characterization and a fourth model for property prediction. The approach handles an 18-dimensional complexity, with ten compositional inputs and eight property outputs, successfully predicting 913 680 property data points across 114 210 composition conditions. This level of complexity is unprecedented in computational materials science, particularly for materials with undefined structures. A framework is proposed to analyze the high-dimensional information space for inverse material design, demonstrating flexibility and adaptability to various materials and scales, provided sufficient data are available. This study advances future research on different materials and the development of more sophisticated models, drawing the authors closer to the ultimate goal of predicting all properties of all materials.
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Affiliation(s)
- Shun Muroga
- Nano Carbon Device Research CenterNational Institute of Advanced Industrial Science and TechnologyTsukuba Central 5, 1‐1‐1, HigashiTsukubaIbaraki305‐8565Japan
| | - Yasuaki Miki
- Nano Carbon Device Research CenterNational Institute of Advanced Industrial Science and TechnologyTsukuba Central 5, 1‐1‐1, HigashiTsukubaIbaraki305‐8565Japan
| | - Kenji Hata
- Nano Carbon Device Research CenterNational Institute of Advanced Industrial Science and TechnologyTsukuba Central 5, 1‐1‐1, HigashiTsukubaIbaraki305‐8565Japan
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11
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Li X, Han H, Evangelou N, Wichrowski NJ, Lu P, Xu W, Hwang SJ, Zhao W, Song C, Guo X, Bhan A, Kevrekidis IG, Tsapatsis M. Machine learning-assisted crystal engineering of a zeolite. Nat Commun 2023; 14:3152. [PMID: 37258522 DOI: 10.1038/s41467-023-38738-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 05/10/2023] [Indexed: 06/02/2023] Open
Abstract
It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms' results offers the insight that reduced Na2O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8).
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Affiliation(s)
- Xinyu Li
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA
| | - He Han
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA
- State Key Laboratory of Fine Chemicals, PSU-DUT Joint Center for Energy Research, School of Chemical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning Province, China
| | - Nikolaos Evangelou
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Noah J Wichrowski
- Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Peng Lu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA
| | - Wenqian Xu
- X-ray Science Division, Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Son-Jong Hwang
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Wenyang Zhao
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA
| | - Chunshan Song
- State Key Laboratory of Fine Chemicals, PSU-DUT Joint Center for Energy Research, School of Chemical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning Province, China
| | - Xinwen Guo
- State Key Laboratory of Fine Chemicals, PSU-DUT Joint Center for Energy Research, School of Chemical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning Province, China
| | - Aditya Bhan
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA.
| | - Ioannis G Kevrekidis
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA.
- Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA.
| | - Michael Tsapatsis
- Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA.
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA.
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA.
- Institute for NanoBioTechnology, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA.
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12
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Ferri P, Li C, Schwalbe-Koda D, Xie M, Moliner M, Gómez-Bombarelli R, Boronat M, Corma A. Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others. Nat Commun 2023; 14:2878. [PMID: 37208318 DOI: 10.1038/s41467-023-38544-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
Approaching the level of molecular recognition of enzymes with solid catalysts is a challenging goal, achieved in this work for the competing transalkylation and disproportionation of diethylbenzene catalyzed by acid zeolites. The key diaryl intermediates for the two competing reactions only differ in the number of ethyl substituents in the aromatic rings, and therefore finding a selective zeolite able to recognize this subtle difference requires an accurate balance of the stabilization of reaction intermediates and transition states inside the zeolite microporous voids. In this work we present a computational methodology that, by combining a fast high-throughput screeening of all zeolite structures able to stabilize the key intermediates with a more computationally demanding mechanistic study only on the most promising candidates, guides the selection of the zeolite structures to be synthesized. The methodology presented is validated experimentally and allows to go beyond the conventional criteria of zeolite shape-selectivity.
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Affiliation(s)
- Pau Ferri
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain
| | - Chengeng Li
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain
| | - Daniel Schwalbe-Koda
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Mingrou Xie
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Manuel Moliner
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Mercedes Boronat
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain.
| | - Avelino Corma
- Instituto de Tecnología Química, Universitat Politècnica de València - Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022, Valencia, Spain.
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13
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Wang R, Zhong Y, Dong X, Du M, Yuan H, Zou Y, Wang X, Lin Z, Xu D. Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials. Inorg Chem 2023; 62:4716-4726. [PMID: 36888968 DOI: 10.1021/acs.inorgchem.3c00233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Crystalline borates are an important class of functional materials with wide applications in photocatalysis and laser technologies. Obtaining their band gap values in a timely and precise manner is a great challenge in material design due to the issues of computational accuracy and cost of first-principles methods. Although machine learning (ML) techniques have shown great successes in predicting the versatile properties of materials, their practicality is often limited by the data set quality. Here, by using a combination of natural language processing searches and domain knowledge, we built an experimental database of inorganic borates, including their chemical compositions, band gaps, and crystal structures. We performed graph network deep learning to predict the band gaps of borates with accuracy, and the results agreed favorably with experimental measurements from the visible-light to the deep-ultraviolet (DUV) region. For a realistic screening problem, our ML model could correctly identify most of the investigated DUV borates. Furthermore, the extrapolative ability of the model was validated against our newly synthesized borate crystal Ag3B6O10NO3, supplemented by the discussion of an ML-based material design for structural analogues. The applications and interpretability of the ML model were also evaluated extensively. Finally, we implemented a web-based application, which could be utilized conveniently in material engineering for the desired band gap. The philosophy behind this study is to use cost-effective data mining techniques to build high-quality ML models, which can provide useful clues for further material design.
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Affiliation(s)
- Ruihan Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Yeshuang Zhong
- Department of Physics, School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou 550025, PR China
| | - Xuehua Dong
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Meng Du
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Haolun Yuan
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Yurong Zou
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Xin Wang
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Zhien Lin
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China
| | - Dingguo Xu
- MOE Key Laboratory of Green Chemistry and Technology, College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, PR China.,Research Center for Materials Genome Engineering, Sichuan University, Chengdu, Sichuan 610065, PR China
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14
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Shen SC, Khare E, Lee NA, Saad MK, Kaplan DL, Buehler MJ. Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics. Chem Rev 2023; 123:2242-2275. [PMID: 36603542 DOI: 10.1021/acs.chemrev.2c00479] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.
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Affiliation(s)
- Sabrina C Shen
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Eesha Khare
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Nicolas A Lee
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,School of Architecture and Planning, Media Lab, Massachusetts Institute of Technology, 75 Amherst Street, Cambridge, Massachusetts 02139, United States
| | - Michael K Saad
- Department of Biomedical Engineering, Tufts University, 4 Colby Street, Medford, Massachusetts 02155, United States
| | - David L Kaplan
- Department of Biomedical Engineering, Tufts University, 4 Colby Street, Medford, Massachusetts 02155, United States
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,Center for Computational Science and Engineering, Schwarzman College of Computing, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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15
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Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203899. [PMID: 36285802 PMCID: PMC9798988 DOI: 10.1002/advs.202203899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/27/2022] [Indexed: 06/04/2023]
Abstract
Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.
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Affiliation(s)
- Haoxin Mai
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
| | - Tu C. Le
- School of EngineeringSTEM CollegeRMIT UniversityGPO Box 2476MelbourneVictoria3001Australia
| | - Dehong Chen
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
| | - David A. Winkler
- Monash Institute of Pharmaceutical SciencesMonash UniversityParkvilleVIC3052Australia
- School of Biochemistry and ChemistryLa Trobe UniversityKingsbury DriveBundoora3042Australia
- School of PharmacyUniversity of NottinghamNottinghamNG7 2RDUK
| | - Rachel A. Caruso
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
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16
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Dai N. Analysis of Data Interaction Process Based on Data Mining and Neural Network Topology Visualization. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1817628. [PMID: 35814595 PMCID: PMC9259330 DOI: 10.1155/2022/1817628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/06/2022] [Accepted: 06/08/2022] [Indexed: 01/13/2023]
Abstract
This paper addresses data mining and neural network model construction and analysis to design a data interaction process model based on data mining and topology visualization. This paper performs preprocessing data operations such as data filtering and cleaning of the collected data. A typical multichannel convolutional neural network (MCNN) in deep learning techniques is applied to alert students' academic performance. In addition, the network topology of the CNN is optimized to improve the performance of the model. The CNN has many hyperparameters that need to be tuned to construct an optimal model that can effectively interact with the data. In this paper, we propose a method to visualize the network topology within unstable regions to address the current problem of lacking an effective way to layout the network topology into specified areas. The technique transforms the network topology layout problem within the unstable region into a circular topology diffusion problem within a convex polygon, ensuring a clear, logical topology connection, and dramatically reducing the gaps in the area, making the layout more uniform beautiful. This paper constructs a real-time data interaction model based on JSON format and database triggers using message queues for reliable delivery. A platform-based real-time data interaction solution is designed by combining the timer method with the original key. The solution designed in this paper considers the real-time accuracy, security and reliability of data interaction. It satisfies the platform's initial and newly discovered requirements for data interaction.
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Affiliation(s)
- Nina Dai
- Shanghai Donghai Vocational & Technical College, Shanghai 200241, China
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17
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Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7138515. [PMID: 35747723 PMCID: PMC9213165 DOI: 10.1155/2022/7138515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/02/2022] [Accepted: 03/07/2022] [Indexed: 11/17/2022]
Abstract
This work is conducted to deal with the information security of enterprise management under the background of current global informatization, popularize the modern Internet of Things (IoT) management technology of enterprises, and maintain the information security of enterprises and provide modern upgrading means for enterprise management. In this work, it firstly introduces the application scenarios of current Internet and Artificial Intelligence (AI) technology and expounds the IoT technology. Secondly, the enterprise management platform is designed, the requirements of enterprise modern management are analyzed, and then the design requirements of system functions and the design of the information security architecture of the IoT are proposed. Furthermore, an enterprise information security management platform is designed, which covers four parts: IoT data mining management, equipment management, key management, and database management. In addition, the performance of the security management platform is tested from four parts: concurrency testing, stress testing, large data volume testing, and security testing. The research results show that the IoT-based enterprise information security management platform designed in this work under the background of AI has perfect functions and stable performance of each module. Concurrency testing, stress testing, large data volume testing, and stability testing are performed on it, and the success rate of the platform in each task reaches 100%. The average response time of concurrent testing and stress testing is about 0.13 seconds, and that of the event entry events is 0.25 seconds. The central processing unit (CPU) occupancy rate in each monitoring task is always lower than 20%. Therefore, it is determined that the performance of the IoT-based enterprise information security management platform designed in this work is sufficient to meet the daily management of enterprises. This work can provide a guarantee for enterprise information security management using AI technology, setting an example for future related research.
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18
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Yang RX, Jan K, Chen CT, Chen WT, Wu KCW. Thermochemical Conversion of Plastic Waste into Fuels, Chemicals, and Value-Added Materials: A Critical Review and Outlooks. CHEMSUSCHEM 2022; 15:e202200171. [PMID: 35349769 DOI: 10.1002/cssc.202200171] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/27/2022] [Indexed: 06/14/2023]
Abstract
Plastic waste is an emerging environmental issue for our society. Critical action to tackle this problem is to upcycle plastic waste as valuable feedstock. Thermochemical conversion of plastic waste has received growing attention. Although thermochemical conversion is promising for handling mixed plastic waste, it typically occurs at high temperatures (300-800 °C). Catalysts can play a critical role in improving the energy efficiency of thermochemical conversion, promoting targeted reactions, and improving product selectivity. This Review aims to summarize the state-of-the-art of catalytic thermochemical conversions of various types of plastic waste. First, general trends and recent development of catalytic thermochemical conversions including pyrolysis, gasification, hydrothermal processes, and chemolysis of plastic waste into fuels, chemicals, and value-added materials were reviewed. Second, the status quo for the commercial implementation of thermochemical conversion of plastic waste was summarized. Finally, the current challenges and future perspectives of catalytic thermochemical conversion of plastic waste including the design of sustainable and robust catalysts were discussed.
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Affiliation(s)
- Ren-Xuan Yang
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01851, USA
- Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10607, Taiwan
- Institute of Environmental Engineering and Management, National Taipei University of Technology, No.1 Sec. 3, Chung-Hsiao E. Rd., Taipei, 106344, Taiwan
| | - Kalsoom Jan
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01851, USA
| | - Ching-Tien Chen
- Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10607, Taiwan
| | - Wan-Ting Chen
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01851, USA
| | - Kevin C-W Wu
- Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10607, Taiwan
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19
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Nandy A, Duan C, Kulik HJ. Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2021.100778] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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20
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Boronat M, Climent MJ, Concepción P, Díaz U, García H, Iborra S, Leyva-Pérez A, Liu L, Martínez A, Martínez C, Moliner M, Pérez-Pariente J, Rey F, Sastre E, Serna P, Valencia S. A Career in Catalysis: Avelino Corma. ACS Catal 2022. [DOI: 10.1021/acscatal.2c01043] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Mercedes Boronat
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Maria J. Climent
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Patricia Concepción
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Urbano Díaz
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Hermenegildo García
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Sara Iborra
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Antonio Leyva-Pérez
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Lichen Liu
- Department of Chemistry, Tsinghua University, Beijing 100084, China
| | - Agustin Martínez
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Cristina Martínez
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Manuel Moliner
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Joaquín Pérez-Pariente
- Instituto de Catálisis y Petroleoquímica, Consejo Superior de Investigaciones Científicas, Marie Curie 2, Madrid 28049, Spain
| | - Fernando Rey
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
| | - Enrique Sastre
- Instituto de Catálisis y Petroleoquímica, Consejo Superior de Investigaciones Científicas, Marie Curie 2, Madrid 28049, Spain
| | - Pedro Serna
- ExxonMobil Technology and Engineering Company, Catalysis Fundamentals, Annandale, New Jersey 08801, United States
| | - Susana Valencia
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas (UPV-CSIC), Av. de los Naranjos s/n, Valencia 46022, Spain
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21
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Luo Y, Bag S, Zaremba O, Cierpka A, Andreo J, Wuttke S, Friederich P, Tsotsalas M. MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning. Angew Chem Int Ed Engl 2022; 61:e202200242. [PMID: 35104033 PMCID: PMC9310626 DOI: 10.1002/anie.202200242] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Indexed: 11/24/2022]
Abstract
Despite rapid progress in the field of metal–organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web‐tool on https://mof‐synthesis.aimat.science.
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Affiliation(s)
- Yi Luo
- Institute of Functional Interfaces, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Saientan Bag
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Orysia Zaremba
- Basque Center for Materials, Applications & Nanostructures, Edif. Martina Casiano, Pl. 3 Parque Científico UPV/EHU Barrio Sarriena, 48940, Leioa, Bizkaia, Spain
| | - Adrian Cierpka
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131, Karlsruhe, Germany
| | - Jacopo Andreo
- Basque Center for Materials, Applications & Nanostructures, Edif. Martina Casiano, Pl. 3 Parque Científico UPV/EHU Barrio Sarriena, 48940, Leioa, Bizkaia, Spain
| | - Stefan Wuttke
- Basque Center for Materials, Applications & Nanostructures, Edif. Martina Casiano, Pl. 3 Parque Científico UPV/EHU Barrio Sarriena, 48940, Leioa, Bizkaia, Spain.,Ikerbasque, Basque Foundation for Science, Bilbao, 48013, Spain
| | - Pascal Friederich
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.,Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131, Karlsruhe, Germany
| | - Manuel Tsotsalas
- Institute of Functional Interfaces, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.,Institute of Organic Chemistry, Karlsruhe Institute of Technology, Kaiserstrasse 12, 76131, Karlsruhe, Germany
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22
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Nandy A, Terrones G, Arunachalam N, Duan C, Kastner DW, Kulik HJ. MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks. Sci Data 2022; 9:74. [PMID: 35277533 PMCID: PMC8917177 DOI: 10.1038/s41597-022-01181-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/17/2022] [Indexed: 11/09/2022] Open
Abstract
We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models. Measurement(s) | thermal decomposition | Technology Type(s) | thermogravimetry |
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Affiliation(s)
- Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Gianmarco Terrones
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Naveen Arunachalam
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - David W Kastner
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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23
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Luo Y, Bag S, Zaremba O, Cierpka A, Andreo J, Wuttke S, Friederich P, Tsotsalas M. Vorhersage der MOF‐Synthese durch automatisches Data‐Mining und maschinelles Lernen**. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202200242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Yi Luo
- Institute of Functional Interfaces Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Deutschland
| | - Saientan Bag
- Institute of Nanotechnology Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Deutschland
| | - Orysia Zaremba
- Basque Center for Materials, Applications & Nanostructures Edif. Martina Casiano, Pl. 3 Parque Científico UPV/EHU Barrio Sarriena 48940 Leioa Bizkaia Spanien
| | - Adrian Cierpka
- Institute of Theoretical Informatics Karlsruhe Institute of Technology Am Fasanengarten 5 76131 Karlsruhe Deutschland
| | - Jacopo Andreo
- Basque Center for Materials, Applications & Nanostructures Edif. Martina Casiano, Pl. 3 Parque Científico UPV/EHU Barrio Sarriena 48940 Leioa Bizkaia Spanien
| | - Stefan Wuttke
- Basque Center for Materials, Applications & Nanostructures Edif. Martina Casiano, Pl. 3 Parque Científico UPV/EHU Barrio Sarriena 48940 Leioa Bizkaia Spanien
- Ikerbasque Basque Foundation for Science Bilbao 48013 Spanien
| | - Pascal Friederich
- Institute of Nanotechnology Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Deutschland
- Institute of Theoretical Informatics Karlsruhe Institute of Technology Am Fasanengarten 5 76131 Karlsruhe Deutschland
| | - Manuel Tsotsalas
- Institute of Functional Interfaces Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Deutschland
- Institute of Organic Chemistry Karlsruhe Institute of Technology Kaiserstrasse 12 76131 Karlsruhe Deutschland
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24
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Duan C, Nandy A, Kulik HJ. Machine Learning for the Discovery, Design, and Engineering of Materials. Annu Rev Chem Biomol Eng 2022; 13:405-429. [PMID: 35320698 DOI: 10.1146/annurev-chembioeng-092320-120230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based modes, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , , .,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; , ,
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25
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Bertolazzo AA, Dhabal D, Molinero V. Polymorph Selection in Zeolite Synthesis Occurs after Nucleation. J Phys Chem Lett 2022; 13:977-981. [PMID: 35060725 DOI: 10.1021/acs.jpclett.2c00033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Zeolites are porous crystals with extensive polymorphism. The hydrothermal synthesis of zeolites is a multistage process involving amorphous precursors that evolve continuously in solubility and local order toward those of the crystal. These results pose several questions: Why does a first-order transition appear as a continuous transformation? At which stage is the polymorph selected? How large are the barriers and critical sizes for zeolite nucleation? Here we address these questions using nucleation theory with experimental data. We find that the nucleation barriers and critical zeolite nuclei are extremely small at temperatures of hydrothermal synthesis, resulting in spinodal-like crystallization that produces a mosaic of tiny zeolitic crystallites that compete to grow inside each glassy precursor nanoparticle. The subnanometer size of the critical nuclei reveals that the selection between zeolite polymorphs occurs after the nucleation stage, during the growth and coarsening of the crystals around the excluded volume of the structure-directing agents.
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Affiliation(s)
- Andressa A Bertolazzo
- Department of Chemistry, The University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112-0850, United States
| | - Debdas Dhabal
- Department of Chemistry, The University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112-0850, United States
| | - Valeria Molinero
- Department of Chemistry, The University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112-0850, United States
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26
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Chaikittisilp W, Yamauchi Y, Ariga K. Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials? ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2107212. [PMID: 34637159 DOI: 10.1002/adma.202107212] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/05/2021] [Indexed: 05/27/2023]
Abstract
Materials science and chemistry have played a central and significant role in advancing society. With the shift toward sustainable living, it is anticipated that the development of functional materials will continue to be vital for sustaining life on our planet. In the recent decades, rapid progress has been made in materials science and chemistry owing to the advances in experimental, analytical, and computational methods, thereby producing several novel and useful materials. However, most problems in material development are highly complex. Here, the best strategy for the development of functional materials via the implementation of three key concepts is discussed: nanotechnology as a game changer, nanoarchitectonics as an integrator, and materials informatics as a super-accelerator. Discussions from conceptual viewpoints and example recent developments, chiefly focused on nanoporous materials, are presented. It is anticipated that coupling these three strategies together will open advanced routes for the swift design and exploratory search of functional materials truly useful for solving real-world problems. These novel strategies will result in the evolution of nanoporous functional materials.
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Affiliation(s)
- Watcharop Chaikittisilp
- JST-ERATO Yamauchi Materials Space-Tectonics Project, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
| | - Yusuke Yamauchi
- JST-ERATO Yamauchi Materials Space-Tectonics Project, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Australian Institute for Bioengineering and Nanotechnology (AIBN) and School of Chemical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Katsuhiko Ariga
- JST-ERATO Yamauchi Materials Space-Tectonics Project, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan
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27
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Fu D, Davis ME. Carbon dioxide capture with zeotype materials. Chem Soc Rev 2022; 51:9340-9370. [DOI: 10.1039/d2cs00508e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
This review describes the application of zeotype materials for the capture of CO2 in different scenarios, the critical parameters defining the adsorption performances, and the challenges of zeolitic adsorbents for CO2 capture.
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Affiliation(s)
- Donglong Fu
- Chemical Engineering, California Institute of Technology, Mail Code 210-41, Pasadena, California 91125, USA
| | - Mark E. Davis
- Chemical Engineering, California Institute of Technology, Mail Code 210-41, Pasadena, California 91125, USA
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28
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Jain R, Mallette AJ, Rimer JD. Controlling Nucleation Pathways in Zeolite Crystallization: Seeding Conceptual Methodologies for Advanced Materials Design. J Am Chem Soc 2021; 143:21446-21460. [PMID: 34914871 DOI: 10.1021/jacs.1c11014] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A core objective of synthesizing zeolites for widespread applications is to produce materials with properties and corresponding performances that exceed conventional counterparts. This places an impetus on elucidating and controlling processes of crystallization where one of the most critical design criteria is the ability to prepare zeolite crystals with ultrasmall dimensions to mitigate the deleterious effects of mass transport limitations. At the most fundamental level, this requires a comprehensive understanding of nucleation to address this ubiquitous materials gap. This Perspective highlights recent methodologies to alter zeolite nucleation by using seed-assisted protocols and the exploitation of interzeolite transformations to design advanced materials. Introduction of crystalline seeds in complex growth media used to synthesize zeolites can have wide-ranging effects on the physicochemical properties of the final product. Here we discuss the diverse pathways of zeolite nucleation, recent breakthroughs in seed-assisted syntheses of nanosized and hierarchical materials, and shortcomings for developing generalized guidelines to predict synthesis outcomes. We offer a critical analysis of state-of-the-art approaches to tailor zeolite crystallization wherein we conceptualize whether parallels between network theory and zeolite synthesis can be instrumental for translating key findings of individual discoveries across a broader set of zeolite crystal structures and/or synthesis conditions.
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Affiliation(s)
- Rishabh Jain
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
| | - Adam J Mallette
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
| | - Jeffrey D Rimer
- Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, United States
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Schwalbe-Koda D, Corma A, Román-Leshkov Y, Moliner M, Gómez-Bombarelli R. Data-Driven Design of Biselective Templates for Intergrowth Zeolites. J Phys Chem Lett 2021; 12:10689-10694. [PMID: 34709806 DOI: 10.1021/acs.jpclett.1c03132] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Zeolites are inorganic materials with wide industrial applications due to their topological diversity. Tailoring confinement effects in zeolite pores, for instance by crystallizing intergrown frameworks, can improve their catalytic and transport properties, but controlling zeolite crystallization often relies on heuristics. In this work, we use computational simulations and data mining to design organic structure-directing agents (OSDAs) to favor the synthesis of intergrown zeolites. First, we propose design principles to identify OSDAs which are selective toward both end members of the disordered structure. Then, we mine a database of hundreds of thousands of zeolite-OSDA pairs and downselect OSDA candidates to synthesize known intergrowth zeolites such as CHA/AFX, MTT/TON, and BEC/ISV. The computationally designed OSDAs balance phase competition metrics and shape selectivity toward the frameworks, thus bypassing expensive dual-OSDA approaches typically used in the synthesis of intergrowths. Finally, we propose potential OSDAs to obtain hypothesized disordered frameworks such as AEI/SAV. This work may accelerate zeolite discovery through data-driven synthesis optimization and design.
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Affiliation(s)
- Daniel Schwalbe-Koda
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Avelino Corma
- Instituto de Tecnología Química, Universitat Politécnica de Valéncia-Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022 Valencia, Spain
| | - Yuriy Román-Leshkov
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Manuel Moliner
- Instituto de Tecnología Química, Universitat Politécnica de Valéncia-Consejo Superior de Investigaciones Científicas, Avenida de los Naranjos s/n, 46022 Valencia, Spain
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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30
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Nandy A, Duan C, Kulik HJ. Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks. J Am Chem Soc 2021; 143:17535-17547. [PMID: 34643374 DOI: 10.1021/jacs.1c07217] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules. From nearly 4000 manuscripts, we use natural language processing and image analysis to obtain over 2000 solvent-removal stability measures and 3000 thermal degradation temperatures. We analyze the relationships between stability properties and the chemical and geometric structures in this set to identify limits of prior heuristics derived from smaller sets of MOFs. By training predictive machine learning (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property relationships with graph- and pore-structure-based representations, we are able to make predictions of stability orders of magnitude faster than conventional physics-based modeling or experiment. Interpretation of important features in ML models provides insights that we use to identify strategies to engineer increased stability into typically unstable 3d-transition-metal-containing MOFs that are frequently targeted for catalytic applications. We expect our approach to accelerate the time to discovery of stable, practical MOF materials for a wide range of applications.
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Affiliation(s)
- Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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31
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Xie D. Rational Design and Targeted Synthesis of Small-Pore Zeolites with the Assistance of Molecular Modeling, Structural Analysis, and Synthetic Chemistry. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c02878] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Dan Xie
- Chevron Technical Center, 100 Chevron Way, Richmond, California 94801, United States
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Abstract
[Figure: see text].
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Affiliation(s)
- Watcharop Chaikittisilp
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Ibaraki 305-0044, Japan
| | - Tatsuya Okubo
- Department of Chemical System Engineering, The University of Tokyo, Tokyo 113-8656, Japan
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33
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Schwalbe-Koda D, Kwon S, Paris C, Bello-Jurado E, Jensen Z, Olivetti E, Willhammar T, Corma A, Román-Leshkov Y, Moliner M, Gómez-Bombarelli R. A priori control of zeolite phase competition and intergrowth with high-throughput simulations. Science 2021; 374:308-315. [PMID: 34529493 DOI: 10.1126/science.abh3350] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
[Figure: see text].
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Affiliation(s)
- Daniel Schwalbe-Koda
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Soonhyoung Kwon
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cecilia Paris
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, 46022 Valencia, Spain
| | - Estefania Bello-Jurado
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, 46022 Valencia, Spain
| | - Zach Jensen
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Elsa Olivetti
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tom Willhammar
- Department of Materials and Environmental Chemistry, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Avelino Corma
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, 46022 Valencia, Spain
| | - Yuriy Román-Leshkov
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Manuel Moliner
- Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, 46022 Valencia, Spain
| | - Rafael Gómez-Bombarelli
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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