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Surface morphological and physical characterizations of glass ionomer cements after sterilization processes.

Rodolfo Xavier de Sousa-LimaJoyce F M de LimaLucas J Silva de AzevedoLetícia V de Freitas ChavesRoberta C B AlonsoBoniek Castillo Dutra Borges
Published in: Microscopy research and technique (2018)
The aim of this study was to investigate if sterilization methods would promote changes in the selected adhesion-related surface properties of glass ionomer cements (GICs). Riva self-cure (RSC) and Riva light-cure (RLC) GICs were tested. Thirty samples were prepared according to the type of material (RSC and RLC) and sterilization method: hydrogen peroxide gas plasma (HPGP), steam sterilization (SS), and no sterilization (n = 5 per group). A Teflon matrix (5 × 2 mm) was filled with one of the GICs to produce the samples. For the groups with the RLC material, the samples were light cured using a light curing unit for 20 s. After 24 hr, finishing and polishing were performed in all samples and then they were sterilized. Surface roughness, wettability, and micromorphology were accessed using a profilometer, a goniometer, and a scanning electron microscopy, respectively. Data were statistically analyzed through a two-way ANOVA and Tukey post-hoc test (p < .05). Both sterilization methods promoted similar roughness values to the nonsterilized samples (p > .05). HPGP decreased contact angle for RSC (p < .01), and SS increased contact angle for RLC (p < .01). Samples subjected to HPGP presented similar surface micromorphology to nonsterilized ones, regardless of the material. SS promoted exposition of smaller filler particles in both materials. Although sterilization methods did not alter surface roughness and wettability, the sterilization methods selectively altered the micromorphology of the materials tested. RESEARCH HIGHLIGHTS: This study's main finding suggests that each sterilization method altered the surface of glass ionomers in different ways. Thus, the choice of sterilization methods prior to bacterial adhesion can lead to a bias in antimicrobial studies.
Keyphrases
  • hydrogen peroxide
  • electron microscopy
  • high resolution
  • staphylococcus aureus
  • escherichia coli
  • machine learning
  • pseudomonas aeruginosa
  • artificial intelligence
  • mass spectrometry
  • biofilm formation