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Long-term effect of firing protocols on surface roughness and flexural strength of lithium disilicate glass-ceramic.

Kaíssa da Cunha LimaRocio Geng VivancoPedro Roberto Braz RodriguesAna Lucia Caetano PereiraFernanda de Carvalho Panzeri Pires de Souza
Published in: Brazilian dental journal (2023)
This study evaluated the effect of different firing protocols on the surface roughness and flexural strength of CAD/CAM lithium disilicate glass-ceramic (LD) after aging methods. Forty-two LD bars of 16 x 4 x 2 mm (IPS e-max CAD, Ivoclar) were randomly separated into two groups according to firing protocols: Single firing-Staining, glazing, and crystallization in a single step; Multiple firings-Crystallization+First staining+Firing+Second staining+Firing+Glazing+Firing. After protocols, initial surface roughness readings were taken (Surfcorder SE1700, Kosakalab). Samples were then randomly separated into three groups (n=7) according to the aging methods they were submitted: Thermomechanical cycling (TMC, ER System, Erios, 1,200,000 cycles, 0.3 MPa, 2 Hz and 5°C/37°C/55°C, 30 s swell time); Simulated toothbrushing (STB, Pepsodent, MAVTEC, 73,000 cycles), and Control (no aging). Final surface roughness readings were done, and samples were submitted to a three-point bending test (OM100, Odeme Dental Research) and fractographic analysis by scanning electron microscopy (EVO-MA10, ZEISS). Data were analyzed (2-way ANOVA, (α=.05). There was no difference (p>.05) in the flexural strength between the firing protocols, regardless of the aging method. STB decreased the flexural strength of samples submitted to multiple firings, different from control (p<.05). Without aging (Control), before TMC, and after STB, LD had lower surface roughness when submitted to multiple firings than to single firing (p<.05). The firing protocols did not affect the flexural strength or the surface roughness of the lithium disilicate glass-ceramic, even after aging. However, toothbrushing negatively affected the flexural strength and smoothed the surface of the ceramic submitted to multiple firings.
Keyphrases
  • electron microscopy
  • high resolution
  • machine learning
  • big data
  • endoplasmic reticulum