Hydration Properties and Interlayer Organization in Synthetic C-S-H.
IEEE Geoscience and Remote Sensing Letters(2020)CCF CSCI 2区SCI 3区
Bur Rech Geol & Minieres | Univ Strasbourg | CNRS CEMHTI | Andra | Univ Lorraine
Abstract
Water in calcium silicate hydrate (C-S-H) is one of the key parameters driving the macroscopic behavior of cement materials for which water vapor partial pressure has an impact on Young's modulus and the volumic properties. Several samples of C-S-H with a bulk Ca/Si ratio ranging between 0.6 and 1.6 were characterized to study their dehydration/hydration behavior under water-controlled conditions using29Si NMR, water adsorption volumetry, X-ray diffraction, and Fourier-transform near-infrared diffuse reflectance under various water pressures. Coherent with several previous studies, it was observed that an increase in the Ca/Si ratio is due to the progressive omission of Si bridging tetrahedra, with the resulting charge being compensated for by interlayer Ca, and that water conditioning influences the layer-to-layer distance and the achieved NMR spectral resolution. Water desorption experiments exhibit one step toward low relative pressure, accompanied by a decrease in the layer-to-layer distance. When sufficient energy is provided to the system (T ≥ 40 °C under vacuum) to remove the interlayer water, the shrinkage/swelling is partially reversible in our experimental conditions. A change in layer-to-layer distance of less than 3 Å is measured in the C-S-H between the wet and dried states. When the bridging SiO2 tetrahedra are omitted, interlayer Ca interacts with layer O and water interacts with the cations and potentially with the surfaces. This structural organization is interpreted as a mid-plane monolayer of water in the interlayer space, this latter accounting for about 30% of the volume of C-S-H particles.
MoreTranslated text
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
1976
被引用36 | 浏览
2000
被引用84 | 浏览
2004
被引用146 | 浏览
2008
被引用112 | 浏览
1981
被引用90 | 浏览
2013
被引用54 | 浏览
2005
被引用157 | 浏览
2012
被引用275 | 浏览
2016
被引用163 | 浏览
2017
被引用115 | 浏览
2018
被引用15 | 浏览
2019
被引用49 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话