Influence of curing duration and mixing techniques of bulk fill resin composites on bi-axial flexural strength and degree of conversion

Abstract

Objectives: The aim was to assess the influence of polymerization duration, method and resin manipulation techniques on the biaxial flexural strength (BFS) and degree of conversion (DC) of bulk fill resin composites (BFRC). Methods: One hundred and eighty disc specimens were fabricated using MultiCore (MC) and Core-It (CI) bulk fill resin composite. Each material group, specimens were divided into nine subgroups based on curing methods (Light cure for 10 and 20 s; and auto-cure) and mixing techniques (first auto-mix, second automix, and hand mix). BFS was tested with a ball indenter at a crosshead speed of 0.50 mm/min. DC was assessed for MC and CI materials for 10 s and 20 s light cure; and auto cure specimens using Fourier Transform-Infrared Spectroscopy (FTIR). Statistical data comparisons were performed using ANOVA, Bonferroni and Tukey-Kramer tests. Results: For MC and CI, BFS was highest in 10 s light cure specimens, however comparable to specimens cured for 20 s ( p > 0.05). Auto cure specimens showed lower BFS than light cured samples for both materials ( p < 0.05). Hand mixed specimens showed significantly compromised BFS compared to automix technique for MC and CI. DC % was comparable for 10 s and 20 s light cure methods for both materials ( p > 0.05), which was higher than DC % of auto cure bulk fill resins ( p < 0.05). CI showed higher DC % and BFS compared to MC bulk fill resin composite. Conclusion: Photo-polymerization duration of 10 and 20 s showed similar outcomes for BFS and DC %; and BFS for auto-mixed resins (MC and CI) was significantly higher than hand mixed resin. BFS and DC was higher in photopolymerized groups as compared to auto-cured resin regardless of the manipulation technique for both materials (MultiCore and Core it).

Comparison of the effect of preheating on the flexural strength of giomer and nanohybrid composite resin

“…It has been reported that an increase in the monomer’s degree of conversion improves the physical and mechanical properties of composite resins. 21 , 22 Preheating resin-based restorative materials can increase the monomer’s degree of conversion and increase the polymerization rate. An increase in the material’s temperature decreases its viscosity, increasing the motility of free radicals and propagation of polymer chains, which result in the completion of the polymerization reaction, formation of more double bonds, and increased cross-linking.…”

“…It has been reported that an increase in temperature might increase the monomer’s degree of conversion, 23 improving composite resins’ physical and mechanical properties. 21 , 22 …”

Fracture Resistance of Maxillary Premolars With Complex Class Ii Cavities Restored With Recent Types of Posterior Composites and Biaxial Flexural Strength Assessment

“…Group IV (n=10): Harmonize composite discs. Composite was packed in the mold with a spatula and the surface was covered with acetate strip and pressed by a glass slab to extrude the excess and achieve consistent surface finish (16,17). The specimens were light cured using (Wood pecker LED-B) light-curing device for 40 seconds following manufacturer's recommended curing time.…”

“…Specimens were immersed in distilled water at 37 ±1°C for 1 week prior to testing to simulate the clinical intraoral conditions. Measurements of diameter (2r3) and thickness (d) of the discs were taken (16,17). II.b) Biaxial Flexural Strength Test Ball-on-3-balls biaxial flexural strength test was applied in a universal testing machine (Instron 3345, England) (Figure 2).…”

Multi-Objective Optimisation of Curing Cycle of Thick Aramid Fibre/Epoxy Composite Laminates

“…It emphasises optimisation through design and not through many experiments and introduces the influence of uncontrollable factors into the experimental design [ 28 , 29 , 30 , 31 , 32 , 33 ]. The Taguchi method combines orthogonal experiments and analysis of variance (ANOVA) [ 34 , 35 , 36 , 37 , 38 , 39 ] to determine the best combination of controllable factors to reduce the impact of uncontrollable factors on system quality fluctuations. Furthermore, the signal-to-noise ratio (SNR) method is an interdisciplinary evaluation and prediction approach [ 40 , 41 ].…”