Studies on electrical and thermal properties of dental glass ionomer cement
A commercially available dental Glass Ionomer Cement (GIC) was studied after setting at room temperature (300 K) to understand its DC electrical conductivity, dielectric and thermal properties. The dental GIC’s are supposed to have free mobile charge carriers like F- ions. Interestingly this material loses its conductivity above 80°C and behaves like a non-polar substance. The frequency dependent dielectric studies also indicate the loss of mobile charge carriers in the samples annealed at 80°C. The DSC and TGA studies indicate that the material loses H2O exothermically at 100°C. This is attributed to the onset of a secondary setting reaction.
Glass-ionomer dental restorative: Part I: a structural study
A structural study of glass-ionomer cement (GIC) dental restoratives has been completed. Transmission electron microscopy, selected area electron diffraction, and X-ray diffraction studies indicate domain-like microstructure in a new experimental material, whereas a featureless amorphous gel-like microstructure exists in the conventional GIC. Nuclear magnetic resonance studies were also conducted. The new experimental GIC contains domains of (i) bonelike material (apatite), (ii) mesoporous material and (iii) other framework structures (aluminium phosphate in the high cristobalite structure), with its setting chemistry a restructuring of the aluminosilicate glass around the template of poly(acrylic acid). Conventional glass-ionomer cement may set by a similar but slower process. Leaching properties of glass-ionomer cements are also explained.
Regression techniques for the detection of analytical bias
Regression techniques are commonly applied to compare two analytical methods at several concentrations and to test the biases of one method relative to another. However, regression is strictly applicable only when one method is without error, for example in comparisons with reference materials. A regression-like technique, maximum-likelihood fitting of a functional relationship (MLFR), is explained and is demonstrated to work well. Under some conditions weighted regression provides a good approximation to MLFR, and so can be used if more convenient.