Restor Dent Endod.  2021 Nov;46(4):e57. 10.5395/rde.2021.46.e57.

Porosity and pore size distribution in high-viscosity and conventional glass ionomer cements: a micro-computed tomography study

• Neves AB
• Lopes LIG
• Bergstrom TG
• Silva ASSd
• Lopes RT
• Neves AdA

• KMID: 2548101
• DOI: http://doi.org/10.5395/rde.2021.46.e57

Abstract

Objectives
This study aimed to compare and evaluate the porosity and pore size distribution of high-viscosity glass ionomer cements (HVGICs) and conventional glass ionomer cements (GICs) using micro-computed tomography (micro-CT).
Materials and Methods
Forty cylindrical specimens (n = 10) were produced in standardized molds using HVGICs and conventional GICs (Ketac Molar Easymix, Vitro Molar, MaxxionR, and Riva Self-Cure). The specimens were prepared according to ISO 9917-1 standards, scanned in a high-energy micro-CT device, and reconstructed using specific parameters. After reconstruction, segmentation procedures, and image analysis, total porosity and pore size distribution were obtained for specimens in each group. After checking the normality of the data distribution, the Kruskal-Wallis test followed by the Student-Newman-Keuls test was used to detect differences in porosity among the experimental groups with a 5% significance level.
Results
Ketac Molar Easymix showed statistically significantly lower total porosity (0.15%) than MaxxionR (0.62%), Riva (0.42%), and Vitro Molar (0.57%). The pore size in all experimental cements was within the small-size range (< 0.01 mm³ ), but Vitro Molar showed statistically significantly more pores/defects with a larger size (> 0.01 mm³ ).
Conclusions
Major differences in porosity and pore size were identified among the evaluated GICs. Among these, the Ketac Molar Easymix HVGIC showed the lowest porosity and void size.

Keyword

Glass ionomer cements; Porosity; X-ray microtomography

Figure

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  Figure 1 Glass ionomer cement specimens and porosity. (A) Original specimen. (B) Specimen after definition of the volume of interest. (C) Cross-sectional slice of the specimen after normalization and filtering. (D) Application of the iterative threshold. (E) Three-dimensional renderization of pore segmentation.

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  Figure 2 Three-dimensional volume rendering obtained after image acquisition and reconstruction of a representative specimen of each studied material. (A) MaxxionR; (B) Riva Self-Cure; (C) Vitro Molar; (D) Ketac Molar Easymix.

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