Metal-organic framework (MOF) nanoparticles, also called porous coordination polymers, are a major part of nanomaterials science, and their role in catalysis is becoming central. The extraordinary variability and richness of their structures afford engineering synergies between the metal nodes, functional linkers, encapsulated substrates, or nanoparticles for multiple and selective heterogeneous interactions and activations in these MOF-based nanocatalysts. Pyrolysis of MOF-nanoparticle composites forms highly porous N- or P-doped graphitized MOF-derived nanomaterials that are increasingly used as efficient catalysts especially in electro- and photocatalysis. This review first briefly summarizes this background of MOF nanoparticle catalysis and then comprehensively reviews the fast-growing literature reported during the last years. The major parts are catalysis of organic and molecular reactions, electrocatalysis, photocatalysis, and views of prospects. Major challenges of our society are addressed using these well-defined heterogeneous catalysts in the fields of synthesis, energy, and environment. In spite of the many achievements, enormous progress is still necessary to improve our understanding of the processes involved beyond the proof-of-concept, particularly for selective methane oxidation, hydrogen production, water splitting, CO2 reduction to methanol, nitrogen fixation, and water depollution.

State of the Art and Prospects in Metal-Organic Framework (MOF)-Based and MOF-Derived Nanocatalysis.

More than 95% (in volume) of all of today’s chemical products are manufactured through catalytic processes, making research into more efficient catalytic materials a thrilling and very dynamic rese...

Metal–Organic Frameworks in Heterogeneous Catalysis: Recent Progress, New Trends, and Future Perspectives

In recent years, nanocrystals of metal sulfide materials have attracted scientific research interest for renewable energy applications due to the abundant choice of materials with easily tunable electronic, optical, physical and chemical properties. Metal sulfides are semiconducting compounds where sulfur is an anion associated with a metal cation; and the metal ions may be in mono-, bi- or multi-form. The diverse range of available metal sulfide materials offers a unique platform to construct a large number of potential materials that demonstrate exotic chemical, physical and electronic phenomena and novel functional properties and applications. To fully exploit the potential of these fascinating materials, scalable methods for the preparation of low-cost metal sulfides, heterostructures, and hybrids of high quality must be developed. This comprehensive review indicates approaches for the controlled fabrication of metal sulfides and subsequently delivers an overview of recent progress in tuning the chemical, physical, optical and nano- and micro-structural properties of metal sulfide nanocrystals using a range of material fabrication methods. For hydrogen energy production, three major approaches are discussed in detail: electrocatalytic hydrogen generation, powder photocatalytic hydrogen generation and photoelectrochemical water splitting. A variety of strategies such as structural tuning, composition control, doping, hybrid structures, heterostructures, defect control, temperature effects and porosity effects on metal sulfide nanocrystals are discussed and how they are exploited to enhance performance and develop future energy materials. From this literature survey, energy conversion currently relies on a limited range of metal sulfides and their composites, and several metal sulfides are immature in terms of their dissolution, photocorrosion and long-term durability in electrolytes during water splitting. Future research directions for innovative metal sulfides should be closely allied to energy and environmental issues, along with their advanced characterization, and developing new classes of metal sulfide materials with well-defined fabrication methods.

Recent advances in metal sulfides: from controlled fabrication to electrocatalytic, photocatalytic and photoelectrochemical water splitting and beyond

Hydrogen (H2) production via photocatalytic water splitting is one of the most promising technologies for clean solar energy conversion to emerge in recent decades. The achievement of energy production from water splitting would mean that we could use water as a fuel for future energy need. Among the various photocatalytic materials, titanium dioxide (TiO2) is the dominant and most widely studied because of its exceptional physico-chemical characteristics. Surface decoration of metal/non-metal on TiO2 nanoparticles is an outstanding technique to revamp its electronic properties and enrich the H2 production efficiency. Metal dopants play a vital role in separation of electron-hole pairs on the TiO2 surface during UV/visible/simulated solar light irradiation. In this paper, the basic principles, photocatalytic-reactor design, kinetics, key findings, and the mechanism of metal-doped TiO2 are comprehensively reviewed. We found that Langmuir-Hinshelwood kinetic model is commonly employed by the researchers to demonstrate the rate of H2 production. Copper (Cu), gold (Au) and platinum (Pt) are the most widely studied dopants for TiO2, owing to their superior work function. The metal dopants can amplify the H2 production efficiency of TiO2 through Schottky barrier formation, surface plasmon resonance (SPR), generation of gap states by interaction with TiO2 VB states. The recent advances and important consequences of 2D materials, perovskites, and other novel photocatalysts for H2 generation have also been reviewed.

Photocatalytic hydrogen production using metal doped TiO2: A review of recent advances

Dramatically increased CO2 concentration from several point sources is perceived to cause severe greenhouse effect towards the serious ongoing global warming with associated climate destabilization, inducing undesirable natural calamities, melting of glaciers, and extreme weather patterns. CO2 capture and utilization (CCU) has received tremendous attention due to its significant role in intensifying global warming. Considering the lack of a timely review on the state-of-the-art progress of promising CCU techniques, developing an appropriate and prompt summary of such advanced techniques with a comprehensive understanding is necessary. Thus, it is imperative to provide a timely review, given the fast growth of sophisticated CO2 capture and utilization materials and their implementation. In this work, we critically summarized and comprehensively reviewed the characteristics and performance of both liquid and solid CO2 adsorbents with possible schemes for the improvement of their CO2 capture ability and advances in CO2 utilization. Their industrial applications in pre- and post-combustion CO2 capture as well as utilization were systematically discussed and compared. With our great effort, this review would be of significant importance for academic researchers for obtaining an overall understanding of the current developments and future trends of CCU. This work is bound to benefit researchers in fields relating to CCU and facilitate the progress of significant breakthroughs in both fundamental research and commercial applications to deliver perspective views for future scientific and industrial advances in CCU.

Industrial carbon dioxide capture and utilization: state of the art and future challenges

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. 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 show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning 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. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.

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

Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures. At present there are databases with over 500,000 predicted or synthesized MOF structures, yet a method to establish whether a new material adds new information does not exist. Here the authors propose a machine-learning based approach to quantify the structural and chemical diversity in common MOF databases.

/pdf/understanding-the-diversity-of-the-metal-organic-framework-2vhgf2amvj.pdf

Understanding the diversity of the metal-organic framework ecosystem

Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable, and it can also be obtained from the refined unit cell by a number of computational techniques. In this work, we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium, and probe center pore volumes, by studying a database of more than 5000 frameworks. We developed a new technique to fully characterize the internal void of a microporous material and to compute the probe-accessible and -occupiable pore volume. We show that, unlike the other definitions of pore volume, the occupiable pore volume can be directly related to the experimentally measured pore volumes from nitrogen isotherms.

http://pubs.acs.org/doi/pdf/10.1021/acs.langmuir.7b01682

Accurate Characterization of the Pore Volume in Microporous Crystalline Materials.

By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are databases with over 500,000 predicted structures. This raises the question whether a new experimental or predicted structure adds new information. For MOF-chemists the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we show how machine learning can be used to quantify similarities of MOFs. This quantification allows us to use techniques from ecology to analyse the chemical diversity of these materials in terms of diversity metrics. In particular, we show that this diversity analysis can identify biases in the databases, and how such bias can lead to incorrect conclusions. This formalism provides us with a simple and powerful practical guideline to see whether a set of new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.

/pdf/understanding-the-diversity-of-the-metal-organic-framework-19y3sw603p.pdf

Understanding the Diversity of the Metal-Organic Framework Ecosystem

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.

Daniele Ongari

Papers

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

Understanding the diversity of the metal-organic framework ecosystem

Accurate Characterization of the Pore Volume in Microporous Crystalline Materials.

Understanding the Diversity of the Metal-Organic Framework Ecosystem

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