Influence of organoclay structural characteristics on properties and hydration of cement pastes

Čėsnienė, Jūratė; Baltušnikas, Arūnas; Lukošiūtė, Irena; Brinkienė, Kristina; Kalpokaitė-Dičkuvienė, Regina

doi:10.1016/j.conbuildmat.2018.01.099

Cited by 18 publications

(6 citation statements)

References 43 publications

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“…The hydration products have negligible change after adding CMT, which is similar to the results of other calcined clay introduced into OPC [40]. However, the addition of CMT provides additional chemical active components such as SiO 2 and Al 2 O 3 [41]. In the highly alkaline environment, SiO 2 , Al 2 O 3 , and other hydration products will produce additional C-S-H, C-A-H, and other hydration products.…”

Section: Phase Transformationsupporting

confidence: 78%

Hydration Characteristics and Mechanical Properties of Cement-Based Materials Modified by Calcined Zeolite and Montmorillonite

Qin¹,

Zhou²,

Yan³

et al. 2023

Journal of Renewable Materials

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Montmorillonite and clinoptilolite zeolite were used as representative materials to prepare calcined clay-cement binary cementitious materials in order to study the effect of calcination treatment on the activation of clay minerals and the activity difference between layered and framed clays in this research. The influence of different calcined clay content (2%, 4%, 6%, 8%, 10%) on the fluidity, compressive strength, microstructure, phase change, and hydration heat of cement-based materials were analyzed. The calcined clay improves the fluidity of cement-based materials as compared with the uncalcined group. The addition of calcined montmorillonite (CMT) improves the development of mechanical strength, and the optimal compressive strength reaches 85 MPa at 28 days with 8% CMT. However, the activity of calcined clinoptilolite zeolite (CZL) is weak with few reaction sites, which slightly reduced the mechanical strength as compared to the blank sample. The addition of CMT changes the microscopic morphology of hydration products such as C-S-H and C-A-H, leading to the formation and transformation of ettringite in the early stage. It promotes the gradual polymerization of Si-O bonds into Si-O-Si bonds simultaneously, which accelerates the early hydration process. However, CZL acts mainly as a filling function in the cementitious system. In brief, CMT as an admixture can improve the mechanical properties of cement, but CZL has little effect. This work provides a guideline for the applications of calcined clay in cement, considering the influence of clay type on workability and mechanical strength.

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Section: Phase Transformationsupporting

confidence: 78%

Hydration Characteristics and Mechanical Properties of Cement-Based Materials Modified by Calcined Zeolite and Montmorillonite

Qin¹,

Zhou²,

Yan³

et al. 2023

Journal of Renewable Materials

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“…OMMT of lower cation exchange degree (0.6 M) shows pozzolanic behaviour after 14 days, while OMMT of higher cation exchange degree (0.8 and 1 M) displays the activity later only after 28 days. [27] The work presented in this paper was part of a much broader research project, namely FIBCEM, (Nanotechnology-enhanced Extruded Fibre Reinforced For Cement-based Environmentally Friendly Sandwich Material for Building Applications) supported by the EU, involving industrial and academic partners throughout Europe, to investigate nanotechnologically enhanced cements. A number of studies have been conducted and published by the authors on ternary, quaternary, or quinary pastes, which did not include fibers or superplasticizers [28][29][30][31][32].…”

Section: Manufacturing and Fabrication Methods Cement MIX Characterizamentioning

confidence: 99%

From Nanostructural Characterization of Nanoparticles to Performance Assessment of Low Clinker Fiber–Cement Nanohybrids

Paine

2019

Applied Sciences

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With the current paper three nano-Montmorillonites (nMt) are applied in cement nanohybrids: an organomodified nMt dispersion, nC2; an inorganic nMt dispersion, nC3; and an organomodified powder, nC4. nC4 is fully characterized in this paper (X-ray diffraction, scanning electron microscopy/X-ray energy dispersive spectroscopy and thermal gravimetric analysis/differential thermogravimetry. Consecutively a ternary non pozzolanic combination of fiber–cement nanohybrids (60% Portland cement (PC) and 40% limestone (LS)) was investigated in terms of flexural strength, thermal properties, density, porosity, and water impermeability. Flexural strength was improved after day 28, particularly with the addition of the inorganic nMt dispersion. There was no change in density or enhancement in pozzolanic reactions for the powder nMt. Mercury intrusion porosimetry showed that the pore related parameters were increased. This can be attributed to mixing effects and the presence of fibers. Water impermeability tests yielded ambiguous results. Clearly, novel manufacturing processes of cement nanohybrids must be developed to eliminate mixing issues recorded in this research.

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“…Nanoclay consisted of an octahedral sheet of magnesia or alumina sandwiched between two tetrahedral sheets of silica. 38 High concentrations of oxides of silica and alumina existed in the tested nanoclay as shown in Figure 17 .…”

Section: Methodsmentioning

confidence: 92%

“…When drilling to the deeper depths, the cement sheath also provides protection to the casing from corrosion and shock loads. 4 , 5 The success of cementing job depends on the quality of cement, its additives, mixing, and pumpability. At the rig side, cement is prepared by adding a cementitious material in water with various additives such as accelerators, retarders, friction loss controllers, polymers, etc.…”

Section: Introductionmentioning

confidence: 99%

Development of New Rheological Models for Class G Cement with Nanoclay as an Additive Using Machine Learning Techniques

2020

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The rheology of the oil well cement plays a pivotal role in the cement placement. Accurate prediction of cement rheological parameters helps to monitor the durability and pumpability of the cement slurry. In this study, an artificial neural network is used to develop different models for the prediction of various rheological parameters such as shear stress, apparent viscosity, plastic viscosity, and yield point of a class G cement slurry with nanoclay as an additive. An extensive experimental study was conducted to generate enough data set for the training of artificial intelligence models. The class G oil well cement slurries were prepared by fixing the water–cement ratio to 0.44 and adding organically modified nanoclays as a strength enhancer. The rheological properties of the oil well cement slurries were investigated at a wide range of temperatures (37 ≤ T ≤ 90 °C) and shear rates (5 ≤ γ ≤ 500 s –1 ). Experimental data generated were used for the training of feed-forward neural networks. The predicted values of the rheological properties from the trained model showed a good agreement when compared with the experimental values. The average absolute percentage error was less than 5% in both training and validation phases of modeling. A trend analysis was carried out to ensure that the proposed models can define the underlying physics. From the validation and the trend analysis, it was found that the new models can be used to predict cement rheological properties within the range of data set on which the models were trained. The proposed models are independent of laboratory-dependent variables and can give quick and real-time values of the rheological parameters.

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Influence of organoclay structural characteristics on properties and hydration of cement pastes

Cited by 18 publications

References 43 publications

Hydration Characteristics and Mechanical Properties of Cement-Based Materials Modified by Calcined Zeolite and Montmorillonite

Hydration Characteristics and Mechanical Properties of Cement-Based Materials Modified by Calcined Zeolite and Montmorillonite

From Nanostructural Characterization of Nanoparticles to Performance Assessment of Low Clinker Fiber–Cement Nanohybrids

Development of New Rheological Models for Class G Cement with Nanoclay as an Additive Using Machine Learning Techniques

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