Optimization of Cement Grouts Containing Silica Fume and Viscosity Modifying Admixture

There is an increasing need to identify the effect of mix composition on the rheological properties of cementitious grouts using minislump, Marsh cone, cohesion plate, washout test, and cubes to determine the fluidity, the cohesion, and other mechanical properties of grouting applications. Mixture proportioning involves the tailoring of several parameters to achieve adequate fluidity, cohesion, washout resistance and compressive strength. This paper proposes a statistical design approach using a composite fractional factorial design which was carried out to model the influence of key parameters on the performance of cement grouts. The responses relate to performance included minislump, flow time using Marsh cone, cohesion measured by Lombardi plate meter, washout mass loss and compressive strength at 3, 7, and 28 days. The statistical models are valid for mixtures with water-to-binder ratio of 0.37–0.53, 0.4–1.8% addition of high-range water reducer (HRWR) by mass of binder, 4–12% additive of silica fume as replacement of cement by mass, and 0.02–0.8% addition of viscosity modifying admixture (VMA) by mass of binder. The models enable the identification of underlying factors and interactions that influence the modeled responses of cement grout. The comparison between the predicted and measured responses indicated good accuracy of the established models to describe the effect of the independent variables on the fluidity, cohesion, washout resistance and the compressive strength. This paper demonstrates the usefulness of the models to better understand trade-offs between parameters. The multiparametric optimization is used to establish isoresponses for a desirability function for cement grout. An increase of HRWR led to an increase of fluidity and washout, a reduction in plate cohesion value, and a reduction in the Marsh cone time. An increase of VMA demonstrated a reduction of fluidity and the washout mass loss, and an increase of Marsh cone time and plate cohesion. Results indicate that the use of silica fume increased the cohesion plate and Marsh cone, and reduced the minislump. Additionally, the silica fume improved the compressive strength and the washout resistance.

Influence of solution viscosity and injection protocol on distribution patterns of jet injectors: Application to photodynamic tumour targeting

Photodynamic therapy of deep or nodular skin tumours is currently limited by the poor tissue penetration of the porphyrin precursor 5-aminolevulinic acid (ALA) and preformed photosensitisers. In this study, we investigated the potential of jet injection to deliver both ALA and a preformed photosensitiser (meso-tetra (N-methyl-4-pyridyl) porphine tetra tosylate, TMP) into a defined volume of skin. Initial studies using a model hydrogel showed that as standoff distance is increased, injection depth decreases. As the ejected volume is increased, injection depth increases. It was also shown, for the first time, that, as injection solution viscosity was increased, for a given injection setting and standoff distance, both total depth of jet penetration, L-t, and depth at which the maximum width of the penetration pattern occurred, L-m, decreased progressively. For a standoff distance of zero, the maximum width of the penetration pattern, L-w, increased progressively with increasing viscosity at each of the injection settings. Conversely, when the standoff distance was 2.5 mm, L-w decreased progressively with increasing viscosity. Studies with neonate porcine skin revealed that an injection protocol comprising an 8.98 mPas solution, an arbitrary injection setting of 8 and a standoff distance of zero was capable of delivering photosensitisers to a volume of tissue (L-t of 2.91 mm, L-m of 2.14 mm, L-w of 5. 10 mm) comparable to that occupied by a typical nodular basal cell carcinoma. Both ALA and TMP were successfully delivered using jet injection, with peak tissue concentrations (67.3 mg cm(-3) and 5.6 mg cm(-3), respectively) achieved at a depth of around 1.0 mm and substantial reductions in drug concentration seen at depths below 3.0 mm. Consequently, jet injection may be suitable for selective targeting of ALA or preformed photosensitisers to skin tumours. (c) 2007 Elsevier B.V. All rights reserved.

Radiocarbon dating of bulk peat samples from raised bogs: nonexistence of a previously reported 'reservoir effect'?

In 1995. an unexpected reservoir effect was reported in sequences of bulk C-14 dates of raised bog peat. In most peat Studies bulk C-14 dates are used for obtaining chronologies. Therefore it is important to confirm and quantify such a C-14 reservoir effect. Five bulk peat samples from the raised bog Engbertsdijksveen were conventionally C-14 dated. The same core had previously been precisely dated by C-14 AMS dates of carefully selected above-ground plant remains. The existence of a reservoir effect in bulk peat C-14 samples could not be confirmed. Other explanations for the reported reservoir effect are discussed. (C) 2004 Elsevier Ltd. All rights reserved.

Small volume laboratory on a chip measurements incorporating the quartz crystal microbalance to measure the viscosity-density product of room temperature ionic liquids

A microfluidic glass chip system incorporating a quartz crystal microbalance (QCM) to measure the square root of the viscosity-density product of room temperature ionic liquids (RTILs) is presented. The QCM covers a central recess on a glass chip, with a seal formed by tightly clamping from above outside the sensing region. The change in resonant frequency of the QCM allows for the determination of the square root viscosity-density product of RTILs to a limit of similar to 10 kg m(-2) s(-0.5). This method has reduced the sample size needed for characterization from 1.5 ml to only 30 mu l and allows the measurement to be made in an enclosed system.

Investigation on drop penetration and wetting characteristics of powder-liquid systems in relation to the mixing of acrylic bone cement

This study investigated methyl methacrylate – polymethyl methacrylate powder bed interactions through droplet analyses, using model fluids and commercially available bone cement. The effects of storage temperature of liquid monomer and powder packing configuration on drop penetration time were investigated. Methyl methacrylate showed much more rapid imbibition than caprolactone due to decrease in both contact angle and fluid viscosity. Drop penetration of caprolactone through polymethyl methacrylate increased with decrease in bed macro-voids and increase in bulk density as predicted by the modified constant drawing area penetration model and confirmed by drop penetration images. Linear relationships were found between droplet mass and drawing area with imbibition time. Further experiments showed gravimetric analysis of the polymerised methyl methacrylate – polymethyl methacrylate matrix under various storage temperatures correlated with Reynolds number and Washburn analyses. These observations have direct implications for the design of mixing and delivery systems for acrylic bone cements used in orthopaedic surgery.

Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.