Influence of Different Surface Treatments and Type of luting Cement on the Retention of Glass Fiber Posts
DOI:
https://doi.org/10.51984/joms.v17i1.2321Keywords:
Glass Fiber Posts, Surface treatments, Sandblasting, resin cements, push out bond strength.Abstract
Background and objectives. The restoration of endodontically treated anterior teeth with excessive coronal destruction often requires a post and core system. One of the common treatments of these teeth is by using the glass-fiber posts. Although it offers better retention and stress distribution when used, debonding is still the most common mode of failure for glass fiber posts. Objectives: This study aimed to evaluate the bond strength of different surface treatments of glass fiber post systems using different adhesive systems via push out test. Methods. Forty-two extracted human incisors teeth were selected. The coronal aspect of each tooth was sectioned at 2 mm above the level of cement-enamel junction, and the remaining root received root canal therapy. Post spaces were prepared in all specimens to a depth of 10 mm. The teeth were divided randomly into three main groups, each of 14 specimens according to the glass-fiber post surface treatments: group I: control group (untreated glass-fiber posts surfaces), group II: glass-fiber posts subjected to sandblasting surface treatment with50μm aluminum oxide particles, group III: glass-fiber posts subjected to treated with 10% hydrofluoric acid. Each group has been subdivided into two subgroups, each of 7 specimens according to type of cement used as following: subgroup a: posts cemented by rely X Unicem, subgroup b: posts cemented by multilink N cement. Roots were then cut into three sections coronal, middle, and apical. Push-out test was performed in a universal testing machine at a crosshead speed of 1 mm/minute, until the post segment was dis- lodged from the root section. The data were collected and analyzed with three-factorial ANOVA followed by pair-wise Tukey’s post-hoc tests (p ≤ 0.05) were performed to detect significance between subgroups. Statistical analysis was performed using SPSS IBM V.22.
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