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Hydrogen bonded frameworks: smart materials used smartly
Molecular Systems Design & Engineering ( IF 3.6 ) Pub Date : 2021-08-09 , DOI: 10.1039/d1me00055a Anna Yusov 1, 2, 3, 4 , Alexandra M. Dillon 1, 2, 3, 4 , Michael D. Ward 1, 2, 3, 4
Molecular Systems Design & Engineering ( IF 3.6 ) Pub Date : 2021-08-09 , DOI: 10.1039/d1me00055a Anna Yusov 1, 2, 3, 4 , Alexandra M. Dillon 1, 2, 3, 4 , Michael D. Ward 1, 2, 3, 4
Affiliation
Hydrogen-bonded frameworks (HBFs) have been studied for decades owing to their fascinating and diverse architectures, always with an eye toward the role of hydrogen bonding in their design as well as their utility in various applications. This review addresses recent advances in HBFs that illustrate their versatility and utility stemming from their unique attributes compared with other classes of molecular frameworks. Guanidinium organosulfonate hydrogen-bonded frameworks, pioneered in our lab and one of the most extensive and versatile collections of HBFs, are used to illustrate molecular design concepts and the principle of architectural isomerism that expands access to a greater structural landscape. Recognizing the growing role of computation in materials design, from ab initio methods to machine learning, this review also touches on their emerging use in the design and synthesis of HBFs. The growth of the HBF arsenal promises continuing innovations, with applications ranging from electronic materials and chemical separations to gas adsorption and catalysis.
中文翻译:
氢键框架:巧妙使用的智能材料
氢键框架 (HBF) 因其迷人和多样化的结构而被研究了几十年,始终着眼于氢键在其设计中的作用以及它们在各种应用中的实用性。这篇综述讨论了 HBF 的最新进展,这些进展说明了 HBF 的多功能性和实用性,与其他类别的分子框架相比,它们具有独特的属性。胍盐有机磺酸盐氢键框架在我们的实验室中首创,是最广泛和最通用的 HBF 集合之一,用于说明分子设计概念和结构异构原理,从而扩展了对更大结构景观的访问。认识到计算在材料设计中日益重要的作用,从头开始机器学习的方法,这篇评论还涉及它们在 HBF 的设计和合成中的新兴用途。HBF 武器库的增长有望持续创新,其应用范围从电子材料和化学分离到气体吸附和催化。
更新日期:2021-08-09
中文翻译:
氢键框架:巧妙使用的智能材料
氢键框架 (HBF) 因其迷人和多样化的结构而被研究了几十年,始终着眼于氢键在其设计中的作用以及它们在各种应用中的实用性。这篇综述讨论了 HBF 的最新进展,这些进展说明了 HBF 的多功能性和实用性,与其他类别的分子框架相比,它们具有独特的属性。胍盐有机磺酸盐氢键框架在我们的实验室中首创,是最广泛和最通用的 HBF 集合之一,用于说明分子设计概念和结构异构原理,从而扩展了对更大结构景观的访问。认识到计算在材料设计中日益重要的作用,从头开始机器学习的方法,这篇评论还涉及它们在 HBF 的设计和合成中的新兴用途。HBF 武器库的增长有望持续创新,其应用范围从电子材料和化学分离到气体吸附和催化。