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Now showing 1 - 4 of 4
  • PublicationMetadata only
    Effect of bleaching agents and whitening dentifrices on the surface roughness of human teeth enamel
    (2013-05-01T00:00:00Z) Ozkan, Pelin; Kansu, Gulay; Ozak, ŞULE TUĞBA; Kurtulmus-Yilmaz, Sevcan; Kansu, Pelin; DENİZ, ŞULE TUĞBA
    Objective. The aim of this in vitro study was to evaluate the surface roughness of human enamel bleached with 10% carbamide peroxide or 10% hydrogen peroxide bleaching agents at different times and also subjected to different superficial cleaning treatments. Materials and methods. One hundred and forty flat enamel samples were divided into 14 groups, Group 1-Group 14 (G1-G14). G1-G7 were treated with 10% carbamide peroxide and different dentifrices, G8-G14 were treated with 10% hydrogen peroxide and different dentifrices (G1 and G8: not brushed as control groups; G2 and G9: brushed with Ipana (R) toothpaste; G3 and G10: brushed with Clinomyn (R) toothpaste; G4 and G11: brushed with Moos Dent (R) toothpaste; G5 and G12: brushed with Signal (R) toothpaste; G6 and G13: brushed with Colgate (R) toothpaste; G7 and G14: brushed without dentifrice). A profilometer was used to measure average roughness values of the initial surface roughness and at each 7-day-interval. The bleaching was performed for 6 h a day and the surface cleaning treatment was performed 3-times a day, 2 min each time, for 4 weeks. The samples were stored in distilled water during the test period. Results. Statistical analysis revealed significant differences in surface roughness values over time for all groups except G1 and G8 (not brushed). The results of the surface roughness of all groups were nearly the same. Conclusions. The bleaching with 10% hydrogen peroxide and 10% carbamide peroxide did not alter the enamel surface roughness, but when the bleaching treatment was performed combined with abrasive dentifrices, a significant increase in roughness values was observed.
  • PublicationMetadata only
    Effect of atmospheric plasma versus conventional surface treatments on the adhesion capability between self-adhesive resin cement and titanium surface
    (2015-06-01T00:00:00Z) Seker, Emre; Kilicarslan, Mehmet Ali; Deniz, ŞULE TUĞBA; Mumcu, Emre; Ozkan, Pelin; DENİZ, ŞULE TUĞBA
    PURPOSE. The aim of this study was to evaluate the effects of atmospheric plasma (APL) versus conventional surface treatments on the adhesion of self-adhesive resin cement to Ti-6Al-4V alloy. MATERIALS AND METHODS. Sixty plates of machined titanium (Ti) discs were divided into five groups (n=12): 1) Untreated (CNT); 2) Sandblasted (SAB); 3) Tribochemically treated (ROC); 4) Tungsten Carbide Bur (TCB); 5) APL treated (APL). SEM analysis and surface roughness (Ra) measurements were performed. Self-adhesive resin cement was bonded to the Ti surfaces and shear bond strength (SBS) tests, Ra and failure mode examinations were carried out. Data were analyzed by one-way analysis of variance and chi-squared test. RESULTS. The lowest SBS value was obtained with CNT and was significantly different from all other groups except for APL. The ROC showed the highest SBS and Ra values of all the groups. CONCLUSION. It was concluded that the effect of APL on SBS and Ra was not sufficient and it may not be a potential for promoting adhesion to titanium.
  • PublicationMetadata only
    Efficacy of uncommon surface treatment methods on titanium in order to improve bond strengths for adhesive cementation
    (2016-01-01T00:00:00Z) Kilicarslan, Mehmet Ali; Ozkan, Pelin; Mumcu, Emre; Deniz, ŞULE TUĞBA; DENİZ, ŞULE TUĞBA
    The aim of this study is to comparison of effects of uncommon surface treatments, especially new alternatives on the adhesive strength between resin and titanium surfaces. Fifty-five titanium disks were prepared and they were separated into 5 groups as follows:(1) Control group; (2) Tribochemical treatment group in the laboratory; (3) Tribochemical treatment group in the clinic; (4) Acid etch group; and (5) Nd:YAG Laser-irradiated group. Surface roughness of the specimens was measured using a profilometer, and the topographic patterns were observed by scanning electron microscope. After these tests, resin cement was applied to the titanium samples. Shear bond test was performed via a universal testing machine at a crosshead speed of 0.5 mm/minute. In addition, the correlation between the surface roughness and bond strength was checked using Spearman correlation test (0.01 level). The highest surface roughness value was observed for the acid etch group (1.53 mu m). The highest mean shear bond strength was recorded with the tribochemical procedure group in the laboratory (13.74 MPa) and the lowest with the control group (3.69 MPa). A positive correlation was found between the bond strength and surface roughness for all groups. All of the surface treatment methods that were used in present study increased the bond strength between resin and titanium except for the laser group.
  • PublicationOpen Access
    The accuracy of the prediction models for surface roughness and micro hardness of denture teeth
    (2019-12-01T00:00:00Z) DENİZ, ŞULE TUĞBA; Ozkan, Pelin; Ozkan, Gulay; DENİZ, ŞULE TUĞBA
    The paper aimed to compare the performance of artificial neural network (ANN) model with the results of in vitro experiments. For these experiments, maxillary molars of four different denture teeth were subjected to tea, coffee, cola, cherry juice, distilled water. Vickers microhardness and surface roughness values were measured. Subsequently, ANN model for the prediction of microhardness and surface roughness of different denture teeth were examined. A back-propagation ANN has been used to develop a model relating to the amount of microhardness and surface roughness. The independent variables of the model are distilled water, tea, filtered coffee, cola, cherry juice, time and denture teeth. Microhardness and surface roughness were chosen as the dependent variables. According to the results, a neural network architecture having one input layer with ten neurons, two hidden layers with six neurons, one output layer with two neurons and an epoch size of 48 gives better prediction. Prediction models for dental materials could also be supportive for in vitro studies.