Design of Metal-Organic Framework Templated Materials Using High-Throughput Computational Screening

Covalent organic frameworks: spotlight on applications in the pharmaceutical arena

Covalent organic frameworks (COFs) have much potential in the field of analytical separation research due to their distinctive characteristics, including easy modification, low densities, large specific surface areas and permanent porosity. This article provides a historical overview of the synthesis and broad perspectives on the applications of COFs. The use of COF-based membranes in gas separation, water treatment (desalination, heavy metals and dye removal), membrane filtration, photoconduction, sensing and fuel cells is also covered. However, these COFs also demonstrate great promise as solid-phase extraction sorbents and solid-phase microextraction coatings. In addition to various separation applications, this work aims to highlight important advancements in the synthesis of COFs for chiral and isomeric compounds.

Nanomaterials as Photocatalysts-Synthesis and Their Potential Applications

Increasing demand for energy and environmental degradation are the most serious problems facing the man. An interesting issue that can contribute to solving these problems is the use of photocatalysis. According to literature, solar energy in the presence of a photocatalyst can effectively (i) be converted into electricity/fuel, (ii) break down chemical and microbial pollutants, and (iii) help water purification. Therefore, the search for new, efficient, and stable photocatalysts with high application potential is a point of great interest. The photocatalysts must be characterized by the ability to absorb radiation from a wide spectral range of light, the appropriate position of the semiconductor energy bands in relation to the redox reaction potentials, and the long diffusion path of charge carriers, besides the thermodynamic, electrochemical, and photoelectrochemical stabilities. Meeting these requirements by semiconductors is very difficult. Therefore, efforts are being made to increase the efficiency of photo processes by changing the electron structure, surface morphology, and crystal structure of semiconductors. This paper reviews the recent literature covering the synthesis and application of nanomaterials in photocatalysis.

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning

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.

FAIR data enabling new horizons for materials research

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

Current Trends in Metal-Organic and Covalent Organic Framework Membrane Materials

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have been thoroughly investigated with regards to applications in gas separation membranes in the past years. More recently, new preparation methods for MOFs and COFs as particles and thin-film membranes, as well as for mixed-matrix membranes (MMMs) have been developed. We will highlight novel processes and highly functional materials: Zeolitic imidazolate frameworks (ZIFs) can be transformed into glasses and we will give an insight into their use for membranes. In addition, liquids with permanent porosity offer solution processability for the manufacture of extremely potent MMMs. Also, MOF materials influenced by external stimuli give new directions for the enhancement of performance by in situ techniques. Presently, COFs with their large pores are useful in quantum sieving applications, and by exploiting the stacking behavior also molecular sieving COF membranes are possible. Similarly, porous polymers can be constructed using MOF templates, which then find use in gas separation membranes.