Hatem M. El-Damanhoury
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Articles (10)
Year:
2023
Year:
2023
Objectives This study was conducted to investigate the microhardness, surface roughness (Ra), and wear behavior of thin occlusal veneers (TOV) fabricated from different injectable composite materials and compare them to a Computer-Aided Design (CAD)/Computer-Aided Manufacturing (CAM) resin-based material. Materials and Methods A 1-mm occusal veneer preparation was done in a mandibular right second molar typodont tooth. The prepared model was duplicated to fabricate 32 replicas and divided into four groups (n = 8). Standard TOV were fabricated either indirectly from Cerasmart blocks, Cerasmart, GC (CS), or directly from Beautifil Injectable X, Shofu (BF), G-ænial Universal injectable, GC (GU), or SonicFill 2, Kerr (SF) using the injection molding technique. All the specimens were subjected to both thermomechanical cyclic loading (TMC) in a chewing simulator. Wear measurement was conducted by three-dimensional (3D) scanning of the veneered models before and after TMC, and the difference in the volume of the sample was recorded as the volumetric material loss due to wear. Ra before and after TMC and Vickers microhardness (VHN) of the tested materials were measured using standardized samples (n = 8). Representative samples from each group were investigated under a stereomicroscope and a scanning electron microscope. Statistical Analysis One-way analysis of variance (ANOVA) was applied to detect the effect of material on VHN and wear. Two-way ANOVA was utilized to examine the impact of material and TMC on Ra. Multiple comparisons between the groups were conducted using Tukey's post hoc test (α = 0.05). The Pearson's correlation coefficient was used to determine the relationship between hardness and wear and between roughness and wear (α = 0.05). Results CS exhibited the highest mean VHN (p ≤ 0.001), followed by GU and SF which were statistically similar (p = 0.883) but significantly higher than BF (p < 0.001). After TMC, GU revealed the lowest Ra and volumetric wear (VW), followed by CS, BF, and SF (p < 0.5). A highly significant correlation existed between Ra and VW (p = 0.001, R 2 = 0.9803). Conclusion The effect of TMC on the surface properties and wear resistance of the investigated TOV is material-dependent. GU injectable TOV are less influenced by TMC than CS milled TOV. In contrast, BF and SF demonstrated significant VW and Ra which might limit their clinical use as TOV.
Year:
2022
Objectives To evaluate the fatigue and fracture resistance of ultra-thin laminate veneers (UTLV) with two different thicknesses and two different bonding protocols. Materials and Methods A total of 64 flat enamel surfaces were assigned to either 0.2 or 0.4 mm UTLV. The UTLV were further subdivided and assigned to one of two bonding techniques: adhesive resin cement(RC( or preheated restorative resin composite (HC) (n = 16). Eight samples were fatigued with 750,000 mechanical cycles and 8,000 thermal cycles between 5 and 55°C in a chewing simulator, and the failure mode was evaluated using a stereomicroscope and SEM. The other eight samples from each group were loaded to failure in a universal testing machine to test the fracture resistance. Fisher's exact Probability test was used to analyze the fatigue test results, and two-way analysis of variance and Bonferroni's test were used to analyze the fracture resistance test results. Results The difference in fatigue resistance between failure proportions in different groups was statistically different (p < 0.05). The 0.4-mm-thick UTLV had similar results regardless of the bonding technique, while 0.2-mm-thick UTLV only showed comparable results when cemented with preheated HC. No statistically significant difference was found in fracture resistance between the tested groups (p > 0.05). Conclusion The Bonding technique and the thickness of the UTLV had impacted fatigue resistance but had no significant effect on the fracture resistance. bonding of UTLV with preheated composite increases their fatigue resistance. Different testing approaches delivered different results.
Year:
2022
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2021
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2021
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2021
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2021
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2021
Year:
2021
Collaborators (1)
Abdul Rahman M. Saleh
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