Objective: To evaluate the changes in the surface quality of irreversible hydrocolloid impression material
hydrogum following disinfection with 0.525% sodium hypochlorite, 0.2% Chlorehexidine Gluconate, and 4%
Povidone Iodine.
Methodology: Forty specimens of alginate impression materials hydrogum were fabricated according to the
ISO 1563 and were divided into four groups according to the method of solution dipping: group 1: Dip in
0.525% sodium hypochlorite, group 2: Dip in 0.2% chlorhexidine gluconate, Group 3: Dip in 4% Povidone Iodine,
Group 4: No treatment with any solution (control group). Then the specimens were poured in type II stone.
Surface detail was determined using a stainless steel block in accordance with ISO 1563.
Results: The results were subjected to Kruskal – Wallis non parametric test showed no statistical significant
difference in the effect of the disinfection solution on the surface detail quality (P – value 0.392, at p> 0.05).
However, the surface quality of the specimens disinfected by sodium hypochlorite showed some degree of
erosion or damage to the surface quality of the resultant cast, but the difference was not statistically
significant.
Recommendations: make microbiological study to evaluate the effect of disinfectant solutions on different
microorganisms present in the oral cavity and evaluate the hardness of impression material following the use
of disinfectant solutions.
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Carbon dioxide geo-sequestration (CGS) into sediments in the form of (gas) hydrates is one proposed method for reducing anthropogenic carbon dioxide emissions to the atmosphere and, thus reducing global warming and climate change. However, there is a serious lack of understanding of how such CO2 hydrate forms and exists in sediments. We thus imaged CO2 hydrate distribution in sandstone, and investigated the hydrate morphology and cluster characteristics via x-ray micro-computed tomography in 3D in-situ. A substantial amount of gas hydrate (∼17% saturation) was observed, and the stochastically distributed hydrate clusters followed power-law relations with respect to their size distributions and surface area-volume relationships. The layer-
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Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health
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In current article an easy and selective method is proposed for spectrophotometric estimation of metoclopramide (MCP) in pharmaceutical preparations using cloud point extraction (CPE) procedure. The method involved reaction between MCP with 1-Naphthol in alkali conditions using Triton X-114 to form a stable dark purple dye. The Beer’s law limit in the range 0.34-9 μg mL-1 of MCP with r =0.9959 (n=3) after optimization. The relative standard deviation (RSD) and percentage recoveries were 0.89 %, and (96.99–104.11%) respectively. As well, using surfactant cloud point extraction as a method to extract MCP was reinforced the extinction coefficient(ε) to 1.7333×105L/mol.cm in surfactant-rich phase. The small volume of organi
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