共 47 条
How safe are nanoscale metal-organic frameworks?
被引:10
作者:

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Chakraborty, Swaroop
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Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham, England Univ Cambridge, Dept Phys, Cavendish Lab, Cambridge, England
机构:
[1] Univ Cambridge, Dept Phys, Cavendish Lab, Cambridge, England
[2] Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham, England
来源:
FRONTIERS IN TOXICOLOGY
|
2023年
/
5卷
关键词:
metal organic framework (MOF);
nanosafety;
safe by design;
machine learning;
toxicology;
STABILITY;
CHEMISTRY;
TOXICITY;
NANOPARTICLES;
PREDICTION;
D O I:
10.3389/ftox.2023.1233854
中图分类号:
R99 [毒物学(毒理学)];
学科分类号:
100405 ;
摘要:
Owing to the size scales that can be accessed, the nanoscale has opened doors to new physical and chemical properties, not seen in the bulk. These properties are leveraged by nanomaterials (NMs) across a plethora of applications. More recently, nanoscale metal-organic frameworks (nMOFs) have witnessed explosive growth due to the modularity of their chemical constituents, the ability to modify their composition and structure, and exceptional properties such as permanent porosity and high surface areas. These properties have prompted the investigation of these materials for applications in biological and environmental contexts. However, one aspect that is often ignored in these discussions is their safety at a nanoscale. In this mini review, we aim to initiate a discussion on the safety and toxicity of nMOFs, drawing parallels with the existing guidelines and literature on the safety of inorganic NMs. We first describe why nMOFs are of considerable interest to the scientific community followed by a discussion on routes through which they can be exposed to the environment and living organisms, particularly shedding light on their transformation mechanisms. The review also discusses the factors affecting toxicity of nMOFs, such as their size, shape, morphology, and composition. We briefly highlight potential mechanisms of toxicity and conclude with describing the need to transition towards data-intensive computational approaches such as machine learning to establish nMOFs as credible materials for their envisioned applications.
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