Low-temperature (144 K) xenon-129 NMR of amorphous materials: effects of pore size distribution on chemical shift

1989 ◽  
Vol 93 (22) ◽  
pp. 7549-7552 ◽  
Author(s):  
Tin Tack P. Cheung
Keyword(s):  
Chemical Shift ◽  
Low Temperature ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Amorphous Materials

scholarly journals The Study of Bound Water Status and Pore Size Distribution of Chinese Fir and Poplar Cell Wall by Low-Field NMR

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huimin Cao ◽  
Jianxiong Lyu ◽  
Yongdong Zhou ◽  
Xin Gao
Keyword(s):  
Cell Wall ◽  
Low Temperature ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Adsorption Isotherms ◽  
Bound Water ◽  
Chinese Fir ◽  
Fast Growing ◽  
Pore Size Distributions

With the increasing shortage of timber resources and the advancement of environmental protection projects, many artificial fast-growing forests are planted and used as raw materials in China. There are significant differences in the properties of natural forest wood and artificial fast-growing forest wood, and the properties of wood mainly depend on the change in the status of bound water in the cell wall. In this study, the fiber saturation point (FSP) and pore size distributions within the cell wall of six species of fast-growing forest wood were studied by low-temperature nuclear magnetic resonance (NMR) technology. The effects of species, growth rings, and extractives on the FSP and pore structure were analyzed. The water vapor sorption experiments were performed, and the adsorption isotherms of the samples were fitted through the Guggenheim-Anderson-de Boer (GAB) equation. According to the least-square regression of the adsorption isotherms and combined with the low-temperature NMR experiments, the content and proportion of the different types of bound water were analyzed. The results showed that the average FSP of each Chinese fir was about 40% and that of each poplar was about 35%. There is about a 10% difference between the FSP measured by NMR technology and the adsorption bound water content obtained by adsorption isothermal. The pore size distribution results show that in all samples, the proportion of pores larger than 10.5 nm is very low, about 10%; the proportion of 1.92-10.5 nm pores is about 30%; and the proportion of pores smaller than 1.92nm is more than 50%. This work will be helpful to the study of the wood moisture status and provide reference data for wood modification.

pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Isosteric Heat ◽  
Material Characterisation ◽  
Mixture Adsorption ◽  
Surface Energetics ◽  
Adsorption Processing ◽  
User Friendly

Material characterisation through adsorption is a widely-used laboratory technique. The isotherms obtained through volumetric or gravimetric experiments impart insight through their features but can also be analysed to determine material characteristics such as specific surface area, pore size distribution, surface energetics, or used for predicting mixture adsorption. The pyGAPS (python General Adsorption Processing Suite) framework was developed to address the need for high-throughput processing of such adsorption data, independent of the origin, while also being capable of presenting individual results in a user-friendly manner. It contains many common characterisation methods such as: BET and Langmuir surface area, t and α plots, pore size distribution calculations (BJH, Dollimore-Heal, Horvath-Kawazoe, DFT/NLDFT kernel fitting), isosteric heat calculations, IAST calculations, isotherm modelling and more, as well as the ability to import and store data from Excel, CSV, JSON and sqlite databases. In this work, a description of the capabilities of pyGAPS is presented. The code is then be used in two case studies: a routine characterisation of a UiO-66(Zr) sample and in the processing of an adsorption dataset of a commercial carbon (Takeda 5A) for applications in gas separation.

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