Influence of silica fume on the microstructure of cement pastes: New insights from 1H NMR relaxometry

Performance of Portland cement pastes containing nano-silica and different types of silica

“…One example of this application is silica fume, one of the mineral additions that are most widely used in construction and building since the 1970s and that remains the focus of research due to its high reactivity [1][2][3][4].…”

Multi-scale Review for Possible Mechanisms of Natural Frequency Change of Reinforced Concrete Structures under an Ordinary Drying Condition

“…Small angle scattering profiles (Chiang et al 2012(Chiang et al , 2013Nicoleau et al 2013;Trapote-Barreira et al 2015) and 1 H-NMR relaxometry (Korb 2009;McDonald et al 2010;Muller et al 2012Muller et al , 2013Muller et al , 2015Valori et al 2013) give insights on this topic. An example of elaboration to explain the relationships between C-S-H microstructure change under the first drying and physical properties is shown in the references (Maruyama et al 2014a(Maruyama et al , 2016a.…”

Porosity development in a thermo-hygral finite element model for cementitious materials

“…The latter term is obtained experimentally, but can be neglected if drying takes place under sealed conditions, since it is relatively small (≈1300 μstrains for ordinary cement and ≈200 μstrains for ordinary concrete at ≈700 days from casting [43]). As mentioned in Section 2, the mass of the grouped water forms and the associated capillary porosity might change if the model were based on the findings of Müller et al [17][18][19] rather than the work of Jennings [14]. This might change the values of w i ult and the values of C η_S j used in Eq.…”

“…Recent work by Müller et al [17][18][19], in which H NMR Relaxometry was used to explore the nanoscale morphology of C-S-H, cast doubt over the existence of the large gel pores and suggests that there are only two water forms within the C-S-H gel nanostructure. Their findings also suggest that the volume of capillary pores may be less than predicted by previous models.…”