The Current Status of MOF and COF Applications Freund, Ralph; Zaremba, Orysia; Arnauts, Giel ...
Angewandte Chemie International Edition,
November 2, 2021, Volume:
60, Issue:
45
Journal Article
Peer reviewed
Open access
The amalgamation of different disciplines is at the heart of reticular chemistry and has broadened the boundaries of chemistry by opening up an infinite space of chemical composition, structure, and ...material properties. Reticular design has enabled the precise prediction of crystalline framework structures, tunability of chemical composition, incorporation of various functionalities onto the framework backbone, and as a consequence, fine‐tuning of metal–organic framework (MOF) and covalent organic framework (COF) properties beyond that of any other material class. Leveraging the unique properties of reticular materials has resulted in significant advances from both a fundamental and an applied perspective. Here, we wish to review the milestones in MOF and COF research and give a critical view on progress in their real‐world applications. Finally, we briefly discuss the major challenges in the field that need to be addressed to pave the way for industrial applications.
Leveraging the unique properties of reticular materials has resulted in significant advances from both a fundamental and an applied perspective. This Review reports on the milestones in MOF and COF research and gives a critical view on progress in their real‐world applications. The major challenges in the field that need to be addressed to pave the way for industrial applications are also discussed.
Solid-state spin systems including nitrogen-vacancy (NV) centers in diamond constitute an increasingly favored quantum sensing platform. However, present NV ensemble devices exhibit sensitivities ...orders of magnitude away from theoretical limits. The sensitivity shortfall both handicaps existing implementations and curtails the envisioned application space. This review analyzes present and proposed approaches to enhance the sensitivity of broadband ensemble-NV-diamond magnetometers. Improvements to the spin dephasing time, the readout fidelity, and the host diamond material properties are identified as the most promising avenues and are investigated extensively. This analysis of sensitivity optimization establishes a foundation to stimulate development of new techniques for enhancing solid-state sensor performance.
In this work, high temperature and large temperature gradient are addressed for the first time in the topology optimization of thermo‐elastic structures. The conventional assumption of constant ...material properties (CMPs) is broken through with the full consideration of temperature‐dependent material properties (TDMPs) including thermal conductivity, elastic tensor, and coefficient of thermal expansion. Nonlinear heat conduction is thus implemented to give varying temperature fields in thermoelasticity. The maximum displacement of the specified region is taken as the objective function in the formulation of the optimization problem. The Kreisselmeier‐Steinhauser (KS) function is employed to approximate the regional maximum displacement/temperature. Corresponding sensitivity analyses, which are carried out using the adjoint method, theoretically reveal how TDMPs affect the thermo‐elastic optimization problem. Typical numerical examples are investigated to validate the proposed approach. The results show that the use of TDMPs produces optimized structures of high fidelity with displacements precisely predicted and temperature constraints rigorously satisfied under large temperature gradient, while thermo‐elastic analysis and optimization with CMPs lead to undesirable designs with significant inaccuracy.
Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has been the focus of decades of research. Traditionally, synthetic images of a scene are generated using ...rendering algorithms such as rasterization or ray tracing, which take specifically defined representations of geometry and material properties as input. Collectively, these inputs define the actual scene and what is rendered, and are referred to as the scene representation (where a scene consists of one or more objects). Example scene representations are triangle meshes with accompanied textures (e.g., created by an artist), point clouds (e.g., from a depth sensor), volumetric grids (e.g., from a CT scan), or implicit surface functions (e.g., truncated signed distance fields). The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering. Neural rendering is closely related, and combines ideas from classical computer graphics and machine learning to create algorithms for synthesizing images from real‐world observations. Neural rendering is a leap forward towards the goal of synthesizing photo‐realistic image and video content. In recent years, we have seen immense progress in this field through hundreds of publications that show different ways to inject learnable components into the rendering pipeline. This state‐of‐the‐art report on advances in neural rendering focuses on methods that combine classical rendering principles with learned 3D scene representations, often now referred to as neural scene representations. A key advantage of these methods is that they are 3D‐consistent by design, enabling applications such as novel viewpoint synthesis of a captured scene. In addition to methods that handle static scenes, we cover neural scene representations for modeling non‐rigidly deforming objects and scene editing and composition. While most of these approaches are scene‐specific, we also discuss techniques that generalize across object classes and can be used for generative tasks. In addition to reviewing these state‐of‐the‐art methods, we provide an overview of fundamental concepts and definitions used in the current literature. We conclude with a discussion on open challenges and social implications.
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