Correlating in vitro scratch test with in vivo contact free occlusal area wear of contemporary dental composites

Abstract

Objectives

The aims of this study are to determine the extent to which the ranking order for clinical Contact-Free-Occlusal-Area (CFOA) wear performance of composites correlates with the ranking based on in vitro scratch hardness, and to analyze the extent to which the microstructure influences the overall trend.

Materials and methods

The patient data and CFOA wear measurements of 16 Tetric-C, 17 Tetric-EC, 16 Gradia-DP, 18 Filtek Supreme, 19 Z100 restorations in 31 subjects (8 males, 23 females) of two randomized clinical trials were fitted in a mixed-effect model. The in vivo performance of the restoratives was summarized by ranking the estimated material-related coefficients in the model. Scratch tests on two specimens per composite were run at a constant speed of 0.05 mm/s under indenter with normal loads of 15, 25, and 35 mN. Scratch width, depth and hardness calculated by imaging the scratch tracks were summarized in a model, the material-related coefficients were ranked and correlated with that of in vivo ranking order.

Results

The best in vivo model included as significant factors ( p < 0.0001) the variables material, time/month, cavity type, and jaw type. The CFOA wear ranking order – Filtek Supreme, Z100 > Tetric-C, Tetric-EC > Gradia-DP-correlated closely ( R 2 = 0.991) with the order of scratch hardness – Z100 > Filtek Supreme > Tetric-C, Tetric-EC > Gradia-DP.

Significance

Scratch tests could roughly categorize a new material as to whether it will probably exhibit a high or low in vitro scratch resistance and/or clinical CFOA wear rate.

Introduction

Abrasive wear can be defined as the unwanted loss of solid material from solid surfaces due to mechanical interaction at asperity contact. In the oral cavity, Contact free occlusal area/CFOA is the region with scratches , pits and striations where abrasive wear occurs due to food particles, tooth brushing and other physical objects. Though not mechanically destructive, these scratches may act as particle traps during mastication and accumulate plaque, food debris, stains and calculus, deteriorating the esthetics and biological properties of the restorations.

Scratching of surfaces by abrasion is closely related to the field of tribology. From a tribological standpoint, sharp probes of scratch testers allow simulation of single asperity contacts involved in material removal by abrasion. The nanoscratch technique is based on the deformation of materials in a controlled, reproducible way. It induces scratches and allows comparing the scratch resistance of materials with different compositions and surface textures. Scratch resistance has been represented quantitatively in terms of scratch width, depth, and hardness, and qualitatively by surface damage patterns.

Earlier, single-pass scratch tests of two dental composite resins were performed to simulate the abrasive wear resistance at CFOA and to investigate the material removal mechanisms that occur during abrasion . A good correlation between the quantitative outcome of scratch tests and abrasive wear rates have demonstrated the predictive power of scratch resistance in estimating the abrasion resistance of twenty three dental acrylic resins . Recently, nanoscratch depth curves generated by subjecting specimens of resin composites, silver amalgam, enamel, and dentine to nanoscratch tests have been used to compare the wear resistance . Scratch resistance of obturation materials, dental ceramics, and dental stone models have also been successfully investigated by nanoscratch tests . Nanoscratch tests were employed to assess the nano-wear depth of fluoride treated enamel .

This paper describes the application of an in vitro scratch test to simulate the in vivo CFOA wear performance of five dental composites. The aims of this study are to determine the extent to which the ranking order for clinical CFOA wear performance of candidate composites correlates with the ranking based on scratch hardness, and to analyze the extent to which the microstructure influences the overall trend.

Materials and methods

The composition of the 5 composites investigated is summarized in Table 1 . Fig. 1 depicts the study set-up.

Table 1
Description of materials used in this study.
Material Type Polymer a Fillers a Filler size a Filler content (% by volume) a
Range Mean
Z100 Micro-
hybrid
Bis-GMA,
TEGDMA
Zirconia,
silica
0.01–3.5 μm 0.6 μm 66
Filtek Supreme
(Translucent)
Nano Bis-GMA,
UDMA,
Bis-EMA,
TEGDMA
SiO 2 Nanoclusters
and Nanomers
0.6–1.4 μm 75 nm
75 nm
57.7
Filtek
Supreme
(D/E/B)
Nano Bis-GMA,
UDMA,
Bis-EMA,
TEGDMA
ZrO 2 /SiO 2 Nanoclusters
SiO 2 Nanomers
0.6–1.4 μm ZrO 2 -5 nm
SiO 2 -20 nm
20 nm
59.5
Tetric Ceram Traditional Micro-hybrid Bis-GMA, UDMA, TEGDMA Ba glass, Ba-Al fluorsilicate glass, ytterbium trifluoride, dispersed SiO 2 and spheroid mixed oxide 1–3 μm 1 μm 58
Tetric EvoCeram Nano-hybrid Dimethacry-late Ba glass, ytterbium trifluoride, mixed oxide, prepolymers b 0.6 μm 68
Gradia Direct Posterior Micro-
filled
hybrid
Urethane dimethacryl-ate co-monomer matrix Silica, pre-polymerised fillers, fluoro-alumino-silicate glass b 0.85 μm 65%

a Data taken from the manufacturer’s technical information sheet.

b Data not available in the manufacturer’s technical information sheet.

Fig. 1
Overview of study set-up.

CFOA wear quantification in RCT studies

Gypsum replicas of 16 Tetric-C, 17 Tetric-EC, 16 Gradia-DP, 18 Filtek Supreme, 19 Z100 were taken from two previously published randomized clinical trial studies that evaluated the occlusal contact area/OCA wear resistance of the five composites, but did not report on the CFOA wear. The data presented in this paper focuses on CFOA. A 3D laser scanning technique determined the in vivo CFOA wear by quantifying the vertical loss of substance along the CFOA of gypsum replicas. Match 3D (Willytec, GmbH), a specially developed image analysis software was used to superimpose follow-up images recorded after 6, 12, 24, 36, 48 and 60 months on baseline images, by aligning the three user-defined references. If the standard deviation is less than 20 μm, the match was accepted , following which a digital subtraction of the follow-up image from the baseline was performed to arrive at a differential image to quantify the vertical wear along the CFOA in μm (see for details on matching procedure).

Statistical analyses of clinical CFOA wear magnitude

The CFOA wear data of five materials were compared with each other at six time points (at month 6 up to month 60) by means of pair wise contrasts ( F -test) to which Bonferroni correction for multiple testing was applied. The repeated measurements were summarized by a wear-rate, ρ , for each material, considering a non-linear relationship of wear, W , with time, t ( Fig. 2 ) where

ρ i = W i − W j t i − t j

ρ is the wear rate (μm/month), W is wear (μm), t is time (month), i is 1 for month 6, 2 for month 36, and 3 for month 60, j is i − 1.

Fig. 2
Non-linear wear model. Wear rate ( ρ ) prediction in a non-linear wear model with three time slopes. Time is expressed in months during period1/ running-in-wear from BL-baseline (1 month following P -placement of restorations) to t 1 -6 months through period2/ early-stage wear from t 1 to t 2 -36 months and period3/ steady-state wear from t 2 to t 3 -60 months and their corresponding wear ( W ) is quantified in μm. Wear rate is estimated by the slope of the line fitting the wear data in period1, period2 and period3.

For example, ρ 1 is estimated from the slope of the line fitting the wear data recorded in period1/ running-in wear (from baseline to month 6), from ρ 2 being the slope in period2/ early-stage wear (between months 6 and 36), and ρ 3 the slope in period3/ steady-state wear (between months 36 and 60)

Statistical modelling of the clinical CFOA wear rate and patient data

The patient data available for each of the 31 subjects (8 males, 23 females) include age, gender, cavity type, surface area, type of restorative, and CFOA wear measurements done at six time points. In order to describe the relationship between the response variable (CFOA wear rate) and some of the covariates (material, time, patient) a mixed effect model was fitted to the data set. The goal was to summarize the in vivo performance of the different materials as an in vivo index by ranking the estimated material-related coefficients in the linear mixed model.

Estimation of material-related coefficients

The data set comprising the categorical covariates such as material, time, patient, and CFOA wear magnitude were inserted in a data frame of PROC MXED as a table with columns corresponding to variables and rows to observations. The following formula was applied to the data set to obtain the results to which various extractor functions are applied to obtain values for the estimated covariates:

Y i j k = M i + T j + P k + ε i j k

where Y ijk is an in vivo index and is the sum of the material effect, M i with operator as a covariate, and time effect, T j , as fixed effects plus a patient effect, P k , as a random effect with covariates – cavity type, surface area, tooth (upper/lower), and ε ijk which is an error term measured by R -squared (close to 1) for the observation of the procedural and material effects M i and T j in a patient group P k assuming to be independent and normally distributed.

The four major sections of the results are: a descriptive part, statistics characterizing the model, summary of parameter estimates of the random effects and that of the fixed-effects. Based on the model-fit statistics such as Akaike’s Information Criteria (AIC), the best fitting model (lowest AIC) is selected for each material, among the different models fit to the same data. A statistical significance was accepted at p < 0.05. Generally, the parameter/covariate estimates in a statistical model represent the “best guess” at the unknown values of the model parameters. Such estimates are provided by the third part of the results as a table of estimates and standard errors of random and fixed-effects parameters. Since the material-related effect is of primary interest in this study, estimates of material-related effects M i adjusted for patient covariates were then used as a summarizing in vivo ranking to be correlated later with other in vitro variables. This estimate, M i is basically a numerical value mentioned in the results table labeled as intercept.

Ranking of the material-related coefficients

With the predicted estimate M i for each material referred to as ‘ x ’, a relative rank was defined as 1 + ( n − 1) ( x m )/( M m ) in order to take into account the relative differences between materials, where m is the minimum (mean) of the estimates, M the maximum (mean) of the estimates, and n ( n = 5) the number of materials.

In vitro scratch tests

Two specimens per composite measuring 2 mm × 5 mm were fabricated by inserting the material into a silicone mold in one increment, covering the surfaces with acetate strips and light cured with the Elipar Freelight SN-939800005306 (3 M ESPE) at ≥400 mW/cm 2 for 40 s. The specimens were removed from their molds, embedded in a self-curing acrylic resin, treated in a grinding machine rotating at 200 rpm on SiC grinding papers of decreasing abrasiveness from 320 down to 1200-grit under continuous water cooling, polished using felt cloths with 3, 1 and 0.25 μm embedded diamond, Al 2 O 3 and SiO 2 grains and put in an ultrasonic water bath for 5 min to remove any remaining debris.

The polished specimens with the wide face mounted in epoxy were subjected to scratching tests performed in a MUST micro-tribometer (Falex Tribology N.V., Belgium) with a stainless steel cantilever (stiffness – 1.137 N/m) and diamond tip (radius – 5 μm) in the edge-forward direction. During the tests, the specimens moved under the loaded indenter of normal loads, F n , of 15, 25, and 35 mN at a constant speed of 0.05 mm/s for a total length of 1 mm. On each test sample ( n = 2/composite), five scratches were made. The data collected included applied normal force and tangential force with respect to the position along the scratch.

3D interferometry and SEM imaging of scratches

Scratches were imaged with a non-contact white light interferometric profilometer (WYKO NT 2000) equipped with a WYKO Vision-32 analysis software. The hardness, H s , of the scratched material was calculated from the scratch width, w , at a given normal load, F n from the formula :

H s = 4 F n π w 2

After the analysis by 3D interferometry, the specimens were gold sputtered and investigated in SEM.

Statistical modeling of the scratch performance data

All analyses were performed using SAS software (version 9.2 Windows). Scratch width, w , depth, d and hardness, H s calculated by imaging the scratch tracks were used as data set. Average values obtained from 10 measurements done on 2 samples per composite ( n = 5) for these three variables were investigated. The presence of regression relationships between the dependent variable – scratch hardness and other predictors – scratch width and depth were tested at a statistical significance of 0.05. In order to indicate the existence of a linear relationship between the variables, the coefficient of determination, R 2 , was calculated, and eventually a relative rank was assigned to the tested material.

Correlative ranking

Concerning the ranking order, rank 1 in the case of in vivo wear measurements corresponds to the least wear/better wear resistance, while rank 1 for hardness measurements refers to the lowest hardness. Pearson’s correlation coefficient between clinical/in vivo and in vitro ranking order was calculated.

Materials and methods

The composition of the 5 composites investigated is summarized in Table 1 . Fig. 1 depicts the study set-up.

Table 1
Description of materials used in this study.
Material Type Polymer a Fillers a Filler size a Filler content (% by volume) a
Range Mean
Z100 Micro-
hybrid
Bis-GMA,
TEGDMA
Zirconia,
silica
0.01–3.5 μm 0.6 μm 66
Filtek Supreme
(Translucent)
Nano Bis-GMA,
UDMA,
Bis-EMA,
TEGDMA
SiO 2 Nanoclusters
and Nanomers
0.6–1.4 μm 75 nm
75 nm
57.7
Filtek
Supreme
(D/E/B)
Nano Bis-GMA,
UDMA,
Bis-EMA,
TEGDMA
ZrO 2 /SiO 2 Nanoclusters
SiO 2 Nanomers
0.6–1.4 μm ZrO 2 -5 nm
SiO 2 -20 nm
20 nm
59.5
Tetric Ceram Traditional Micro-hybrid Bis-GMA, UDMA, TEGDMA Ba glass, Ba-Al fluorsilicate glass, ytterbium trifluoride, dispersed SiO 2 and spheroid mixed oxide 1–3 μm 1 μm 58
Tetric EvoCeram Nano-hybrid Dimethacry-late Ba glass, ytterbium trifluoride, mixed oxide, prepolymers b 0.6 μm 68
Gradia Direct Posterior Micro-
filled
hybrid
Urethane dimethacryl-ate co-monomer matrix Silica, pre-polymerised fillers, fluoro-alumino-silicate glass b 0.85 μm 65%

a Data taken from the manufacturer’s technical information sheet.

b Data not available in the manufacturer’s technical information sheet.

Fig. 1
Overview of study set-up.

CFOA wear quantification in RCT studies

Gypsum replicas of 16 Tetric-C, 17 Tetric-EC, 16 Gradia-DP, 18 Filtek Supreme, 19 Z100 were taken from two previously published randomized clinical trial studies that evaluated the occlusal contact area/OCA wear resistance of the five composites, but did not report on the CFOA wear. The data presented in this paper focuses on CFOA. A 3D laser scanning technique determined the in vivo CFOA wear by quantifying the vertical loss of substance along the CFOA of gypsum replicas. Match 3D (Willytec, GmbH), a specially developed image analysis software was used to superimpose follow-up images recorded after 6, 12, 24, 36, 48 and 60 months on baseline images, by aligning the three user-defined references. If the standard deviation is less than 20 μm, the match was accepted , following which a digital subtraction of the follow-up image from the baseline was performed to arrive at a differential image to quantify the vertical wear along the CFOA in μm (see for details on matching procedure).

Statistical analyses of clinical CFOA wear magnitude

The CFOA wear data of five materials were compared with each other at six time points (at month 6 up to month 60) by means of pair wise contrasts ( F -test) to which Bonferroni correction for multiple testing was applied. The repeated measurements were summarized by a wear-rate, ρ , for each material, considering a non-linear relationship of wear, W , with time, t ( Fig. 2 ) where

ρ i = W i − W j t i − t j
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Nov 28, 2017 | Posted by in Dental Materials | Comments Off on Correlating in vitro scratch test with in vivo contact free occlusal area wear of contemporary dental composites
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