Photoluminescent, upconversion luminescent and nonlinear optical metal-organic frameworks: From fundamental photophysics to potential applications

Metal-organic frameworks (MOFs) are a class of porous materials prepared by the self-assembly of metal ions or clusters with organic ligands. The unique characteristics of MOFs, including structural tunability, high surface areas, low densities and tailored pore surface functionalization, have made them leading contenders as high-performance porous materials, alongside the established zeolites and activated carbons. Consequently, the permanent porosity of MOFs has been extensively exploited for gas capture and separation and catalysis. In recent years, the field has been expanded towards new applications and many studies of MOFs are venturing into unexplored avenues. A large number of studies have been focused on photoluminescent, upconversion luminescent, and nonlinear optical MOFs for applications in areas such as white-light emission, bioimaging, sensing, and photocatalysis. Within the first half of this tutorial review, we present the fundamental principles of luminescence, including detailed scientific discussions on the luminescence origin of different materials such as organic dyes, transition metal complexes, quantum dots, and lanthanide compounds. Principles and important parameters for the applications of luminescent MOFs are introduced, followed by a summary of recent interesting publications for each application. In the second half, we introduce nonlinear optical effects including second harmonic generation and two-photon absorption, and upconversion of luminescence, followed by detailed examples of MOFs that exhibit these phenomena. Finally, insights about the remaining challenges and future directions are discussed. (C) 2018 Elsevier B.V. All rights reserved.

Accelerated Discovery of Metal-Organic Frameworks (MOFs) For Energy Related Applications

Arunraj Chidambaram

Metal-Organic Frameworks (MOFs) are an emerging class of materials that consist of metal ions which are linked with organic ligands via coordination bonds to form extended networks. MOF structures consists predominantly of open voids which makes them amongst the most porous materials synthesized to date. The aim of this thesis is to contribute to the development of a platform for a systematic study of the influence of synthetic conditions on MOFs and thereby, assess how this can affect the surface area of MOFs. To achieve this aim, a platform that utilises high-throughput robotic synthesis guided by genetic algorithm and machine learning was devised. This led to a rational synthesis of a MOF with the highest surface area reported to date. MOFs are considered as promising adsorbents for carbon capture from flue gasses emitted from fossil fuel fired power plants. Whilst there are many MOFs that are optimised for separation of CO2 from N2, their separation capability is detrimentally affected when they are subjected to realistic flue gas conditions that contains H2O vapour. This is due to the competition between CO2 and H2O for the same adsorption sites. To address this, through close collaboration with computational scientists, we discovered a new class of materials for CO2/H2O separations.

EPFL2020

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

Kevin Maik Jablonka, Seyedmohamad Moosavi, Daniele Ongari, Berend Smit

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.

2020

Electron Spin Resonance Study of Graphene

Luka Ciric

Graphene, a honeycomb lattice of a single atom thick plane of carbon atoms has captivated the attention of physicists, materials scientists, and engineers because of its extraordinary physical properties. These include exceedingly high charge carrier mobility, current-carrying capacity, thermal conductivity, and mechanical strength. These properties qualify it as an excellent candidate for new electronic technologies both within and beyond metal oxide semiconductors. The attractive properties of graphene for future applications are observed only in high quality graphene produced by mechanical exfoliation of graphite by using the so-called "scotch tape method". However, the yield is low and it satisfies only the needs of basic research. For applications one has to have material which is produced on large scale but preserves the quality of graphene. There are many production routes proposed in the scientific community and their number is still growing. One of the tasks of this PhD work was to test by Electron Spin Resonance (ESR) the quality of graphene produced by various methods. ESR is a suitable technique for looking for ferromagnetic interactions in graphene predicted by theory, and to determine the intrinsic spin relaxation time? These features are important for integration of graphene in spintronics applications. The main results are obtained on a large assembly of three differently prepared samples: 1. Mechanically exfoliated graphite (MEG), 2. Reduced graphene oxide (RGO) and 3. Liquid phase exfoliated (LPE) graphite. Attempts were done to study graphene synthesized by chemical vapor deposition (CVD) and by epitaxial growth on SiC. The mother compound graphite, as a reference sample was also thoroughly investigated by ESR. The LPG sample, obtained by heavy sonication of graphitic powder in NMP solvent, contains more nanographitic particles than graphene. The overall response resembles that of graphite with the exception of detecting a strong magnetic interaction below 26 K. This unambiguously shows the possibility of creating a strong magnetic response in carbon based material, but the challenge to inducing it graphene still remains. The RGO sample, prepared by chemical methods provides a large quantity of sample, suffers from the harsh oxidative and reduction steps involved in the synthesis. Both steps can be incomplete, leaving behind a large number of defects which Anderson-localize the extended states necessary for electronic applications. The best characteristics are obtained on MEG graphene. The temperature dependence of the magnetic susceptibility follows that predicted by theory, and it is distinctly different from that of graphite. Furthermore, the 10-8 s spin lifetime deduced from the ESR linewidth is by two orders of magnitude longer than that obtained in spin-valve experiments and it is encouraging for spintronics applications.

EPFL2011

Electron Spin Resonance Study of Graphene

Luka Ciric

EPFL2011