等结构
金属有机骨架
材料科学
金属
辐照
密度泛函理论
傅里叶变换红外光谱
化学
结晶学
物理化学
化学工程
计算化学
晶体结构
物理
吸附
工程类
核物理学
冶金
作者
Ashley M. Hastings,Melissa Fairley,Megan C. Wasson,Dario Campisi,Arup Sarkar,Zoë C. Emory,Kieran G. Brunson,Dylan B. Fast,Timur İslamoğlu,May Nyman,Peter Carman Burns,Laura Gagliardi,Omar K. Farha,Amy E. Hixon,Jay A. LaVerne
标识
DOI:10.1021/acs.chemmater.2c02170
摘要
Robust and versatile metal–organic frameworks (MOFs) have emerged as sophisticated scaffolds to meet the critical needs of the nuclear community, but their performance depends on their underexplored structural integrities in high- radiation fields. The contributions of selected metal nodes in the radiation stability of MOFs within the isostructural M-UiO-66 series (where M = Zr, Ce, Hf, Th, and Pu; Zr-UiO-66 experiments were executed in a previous work) have been determined. Ce-, Hf-, and Th-UiO-66 MOF samples were irradiated via gamma and He-ion methodologies to obtain doses up to 3 MGy and 85 MGy, respectively, the latter strikingly higher than that obtained in most other studies. Appreciable self-irradiation constituted the total absorbed doses, up to 31 MGy of the gamma-irradiated Pu-UiO-66 samples. Structural degradation was ascertained by powder X-ray diffraction, X-ray total scattering, vibrational spectroscopy, and, where possible, N 2 physisorption isotherms. Diffuse reflectance infrared Fourier transform spectroscopy provided atomic-level mechanistic insights to reveal that the node-linker connection was most susceptible to radiation damage. Density functional theory calculations were performed on cluster models to evaluate the binding energy of the linkers to each metal node. While the isostructures disclosed the same breakdown signatures, distinct radiation sensitivity as a function of metal selection was evident and followed the trend Hf-UiO-66 ∼ Zr-UiO-66 > Th-UiO-66 > Pu-UiO-66 > Ce-UiO-66. We anticipate that these endeavors will contribute to the rational design of radiation-resistant materials for targeted applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI