Startseite Influence of various class cleaning agents for prosthesis on Co–Cr alloy surface
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Influence of various class cleaning agents for prosthesis on Co–Cr alloy surface

  • Joanna Nowak EMAIL logo , Klaudia Steinberg , Jerzy Sokołowski und Kinga Bociong
Veröffentlicht/Copyright: 28. September 2022
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Open Chemistry
Aus der Zeitschrift Open Chemistry Band 20 Heft 1

Abstract

Introduction

Denture cleaners should not be harmful to dental prostheses elements, but immersions in cleaner solutions during a long time of using prosthesis may cause changes on Co–Cr alloy surfaces. There are five classes of denture cleaners: alkaline hypochlorites, alkaline peroxides, disinfectants, diluted acids, and enzymes. The aim of this work was to evaluate the influence of denture cleaners solutions on the surface properties of Co–Cr alloy.

Materials and method

Specimens cast from cobalt–chromium alloy were divided into eight groups: 1 – dry; 2 – ultrapure water; 3 – 20% wt/wt sodium; 4 – 20% chlorhexidine digluconate; 5 – Correga Tabs BioFormula; 6 – 20% wt/wt citric acid; 7 – 0.5% NaOCl; and 8 – 5.0% NaOCl. After immersion in 200 mL of cleaning agent solution at 45°C in 3 years, simulation of use, surface roughness, contact angle, surface free energy (SFE), and microscopic observation was performed.

Results

For citric acid and NaOCl, roughness (R a) raised clearly. These cleaning agents also caused R q, R v, R p, and R Sm to increase the most. The observed water contact angle after using denture cleaners, especially citric acid, and NaOCl decreases, and the values of SFE increase. Under a digital microscope, the harmful effect of citric acid and solutions of NaOCl was visible.

Conclusions

Diluted acids and alkaline hypochlorites in presented concentrations influence Co–Cr surface parameters like roughness and wettability. Other classes of denture cleaning agents do not affect surface roughness parameters which make them safer for the metallic components of removable partial dentures.

Keywords: Co–Cr alloys; denture cleaners; roughness; surface free energy; surface topography

1 Introduction

Dental prostheses are made of various metal alloys. Precious metal alloys such as platinum, gold, and silver have good chemical resistance and biocompatibility; unfortunately, they have a high price and lower strength. Base metal alloys such as chromium and cobalt characterized with higher hardness have better mechanical strength and other properties. Easy-to-process materials with better melting and ductility properties are also preferable for making prostheses. In the oral cavity environment, they must be highly resistant to corrosion. Resistance to oxidation and corrosion makes Co–Cr alloys suitable for use in the oral environment [1]. One of the best ways to clean dentures is a combination of mechanical and chemical cleaning like brushing or using ultrasonic cleaners, or soaking in dental cleaners solutions [2]. One challenge for these alloys is the chemical resistance to cleaning agents used for a prosthesis. Regular contact with harmful substances from prosthesis cleaners may affect alloy surface properties. The removable partial denture (RPD) frameworks can corrode or stain in contact with oxide or chlorine present in cleaners [3]. The ideal denture cleaner should not cause harmful effects to the cobalt–chromium alloy and other parts of the denture like surface pitting or surface discoloration (tarnish) [4]. It should also act quickly, be non-toxic, easy to use, cheap, have antimicrobial properties, reduce odors and discoloration [5]. There are five classes of denture cleaners: alkaline hypochlorites, alkaline peroxides, diluted acids, enzymes, and disinfectants [3]. They are available in different forms: creams, pastes, gels, solutions, tabs, or soluble powders. Denture users prefer the chemical method for cleaning because it is easier and more comfortable than brushing [6,7]. The most common denture cleaners are alkaline peroxides such as sodium perborate, potassium monopersulfate, sodium bicarbonate, and others. Due to the alkaline pH of over 8.5 and generation of active oxide in contact with water, contaminations dissolve easily [2,5,8]. Alkaline hypochlorites are very effective against biofilm compared to peroxides or enzymes. It is worth remembering that hypochlorite concentration should be below 0.5% because higher concentrations adversely affect materials such as acrylics and alloys used in the manufacture of prostheses [9]. NaOCl directly reduces biofilm and removes discoloration, food debris, or tartar [2,10]. Diluted acid denture cleaners contain at least one acid and have a pH below 7. These can be organic or inorganic acids or substances that generate acid during use. They are a good disinfectant and also good at reducing mineral contamination. The most common acid used in this cleaning agent class is citric acid. The second most popular compound is acetic acid. The decrease in the pH of the solution during cleaning kills or stops the growth of microorganisms [11]. To disinfectants as denture cleaning agents class belong to chlorhexidine acetate or digluconate, salicylates, chloroform, ethanol, isopropyl alcohol, or 2% glutaric aldehyde [2,8]. The fifth class of denture cleaning agents is enzymes. They reduce food residues, bacteria plaque, and fungi. In example mutanase or protease act on glycoproteins, polysaccharides, or mucoproteins present in biofilm and have higher effectiveness than alkaline peroxides. One of the advantages of this group is that they are mild to the denture materials [7,12,13].

The average time of use of an RPD is 5 years. During this time, improper cleaning of the RPD can lead to degradation or corrosion of the prosthetic materials, especially in contact with chlorine or oxygen ions. Besides corrosion or color change, cleaners also affect the mechanical strength of Co–Cr, surface roughness and reflectance, lower hardness, loss of retention, and undesirable ion release. Side effects of this harmful acting of immersion-type cleaners may cause biofilm growth and better adhesion to prosthesis [3,14,15,16,17,18,19,20,21,22].

The study aims to compare the effect of five classes of denture cleaning agents on selected properties of the Co–Cr alloy used for RPDs. The tests assessed changes in surface roughness, wettability, and microscopic evaluation of the surface.

2 Methods

Samples of Co–Cr alloy (Wironit extrahart, BEGO, Germany) were cast according to manufacturer instructions. In the next step, they were wet ground with SiC papers (grit P180, P360, P1200, and P2400) and polished with diamond paste (grit, 6 μm). Ultrasonically cleaned isopropyl alcohol samples were divided into eight groups (Table 1).

Table 1

Description of the solution for Co–Cr immersion

Group number Class of denture cleaning solution Solution Manufacturer
1 — Negative control – dry; storage in air —
2 — Ultrapure water (Millipore Direct Q3) Merck, Poland
3 Alkaline peroxides 20% Sodium percarbonate p.a. Biomus, Poland
4 Disinfectants 20% Chlorhexidine digluconate Amara, Poland
5 Enzymes Correga Tabs BioFormula gsk, United Kingdom
6 Diluted acids 20% Citric acid p.a. Stanlab, Poland
7 Alkaline hypochlorites 0.5% Sodium hypochlorite p.a. Chempur, Poland
8 Alkaline hypochlorites Positive control −5.0% sodium hypochlorite p.a. Chempur, Poland

Dry samples were chosen as a negative control group in order to eliminate water which may cause a corrosive environment for cobalt–chromium alloy. Ultrapure water does not act as (strong as) an electrolyte solution. 20% Chlorhexidine digluconate is a commercially used disinfectant. Corega Tabs BioFormula is a commercial denture cleaner that contains the enzyme – subtilisin (protease); 20% concentration of citric acid and sodium perborate corresponds to concentrations of these substances in commercially used denture cleaners. However, we wanted to eliminate possible reactions of other substances added to market products. Commonly used denture cleaner’s concentration is 0.5% sodium hypochlorite but it is worth knowing that minimal antibacterial concentration is 0.05% NaOCl; 5.0% concentration of sodium hypochlorite was used as a positive group because it tends to be corrosive. After preparing cleaning solutions, samples were stored in 200 mL of each cleaning agent at a temperature of 45°C for 92 h. Immersion time corresponds to 1,095 cycles of cleaning, assuming the patient put a denture into a cleaner solution once a day for 5 min during 3 years period. A five roughness parameters (μm): R a – arithmetic average height defined as the arithmetic mean of the absolute values of the evaluation profile deviations from the mean line, R q – root mean square roughness, which is the square root of the arithmetic mean of the squares of the deviations from the mean line to the evaluation profile, R v – maximum depth of valleys, when dividing the evaluation profile into segments based on the sampling length. Then for each segment, the distance of the lowest point (R vi) from the mean lines obtained. R is the mean of the R vi values that we obtained from the segments. R v (ANSI) is defined as the maximum floor depth over the evaluation length, and R p is the maximum height of peaks calculated as divided by the evaluation profile into segments based on the sampling length. Then, for each segment, the distance of the highest point (R pi) from the mean line is obtained. R p is the mean of the R pi values that were obtained from the segments, and R Sm is the mean spacing at a mean line. Portions of the evaluation profile that project upward are called profile element mountains, and portions of the profile that project downwards are called profile element valleys. A mountain followed by a valley is called a “profile element.” The value of this parameter is the arithmetic mean of the width (Xs) of each profile element. Each parameter was measured with a contact profilometer (Surftest SJ- 410, Mitutoyo, Japan). On each sample, nine measurements were made. The Owens-Wendt method was used for calculating surface free energy (SFE). According to the theory, SFE is a work needed to separate two phases from the equilibrium state to create a new surface. SFE γ is a sum of dispersive γ D and polar component γ P (equation (1))

(1) γ = γ D + γ P .

Dispersive component is calculated according to equation (2):

(2) γ D = ( γ 1 P ⁎ γ 2 ⁎ ( cos θ 2 + 1 ) ) − ( γ 2 P ⁎γ 1 ⁎ ( cos θ 1 + 1 ) ) 2 ⁎ ( γ 2 D ⁎ γ 1 P − γ 2 P ⁎ γ 1 D ) 2 ,

and polar component, as taken below (equation (3)):

(3) γ P = ( γ 1 ⁎ ( cos θ 1 + 1 ) ) − ( 2 ⁎ γ 1 D ⁎ γ D ) 2 ⁎ ( γ 1 P ) 2 ,

where γ 1 P is the polar component surface energy of water, 21.6 mN/m; γ 1 D is the dispersive component surface energy of water, 51.0 mN/m; γ 1 is the overall surface energy of water, 72.6 mN/m; γ 2 P is the polar component surface energy of diiodomethane, 2.3 mN/m; γ 2 D is the dispersive component surface energy of diiodomethane, 48.5 mN/m; γ 2 is the overall surface energy of water, 50.8 mN/m; θ 1 is the water contact angle (°); θ 2 is the diiodomethane contact angle (°).

The sessile drop method was used to measure water and diiodomethane (Sigma Aldrich, USA) contact angle. Captured images of drops were analyzed by ImageJ software with Contact Angle plug-in (National Institutes of Health, Bethesda, USA). A surface topography (2D and 3D analyses) was assessed by using a digital microscope (VHX, Keyence, Belgium) under ×1,500 magnification. To make more detailed surface observations, samples were observed under scanning electron microscope-energy-dispersive spectroscopy (SEM–EDS) microscope Hitachi S-4700. The images were made at a magnification of 2,000×, 5,000×, and 10,000×.

Analyzed numerical traits were depicted by their arithmetical weighted mean, standard deviation, 95% confidence interval, and minimum-to-maximum values. The normality of distribution was appraised by using the W Shapiro–Wilk test. The homogeneity of variances was tested by using Levene’s test. The Kruskal–Wallis one-way analysis of variance was fitted in order to assess the statistical significance of differences in the roughness of investigated surfaces treated with selected agents. Multiple comparisons were carried out a posteriori in search of specific pairs showing statistically meaningful dissimilarities.

A level of p < 0.05 was deemed statistically significant. All the statistical procedures were performed by using IBM® SPSS® Statistics, version 28 (Armonk, New York, USA).

3 Results

3.1 Roughness

Nine measurements per sample were made with a contact profilometer. A Gaussian filter was used to separate roughness from waviness and surface errors. Six roughness parameters were calculated: R a, R q, R p, R v, R Sm, and R mr (Tables 2–7). After immersion in different denture cleaners, all samples have a smooth surface with R a under 0.5 µm [23]. In the case of immersion in citric acid or sodium hypochlorite, the environment increases the surface roughness. The R a parameter is around twice higher compared with other groups (Table 2). R q parameter changes also show the highest value after storage Co–Cr alloy in a citric acid or NaOCl environment, and the difference is about twice higher (Table 3). Using citric acid or sodium hypochlorite causes a slight increase in R v value (Table 5). Also, about three times higher peaks R p can be observed on sample surfaces after immersion in the solutions mentioned earlier (Table 4). In 0.05% NaOCl solution, R Sm increased more than three times, compared with the negative control group (Table 6). We do not observe any significant changes in parameters R Lo and R mr (25%) (Table 7).

Table 2

Descriptive statistics for R a measurements outcome

No. R a (µm) Statistical parameter p-Value
M SD 95% CI Min. – max.
1 Dry 0.016 0.001 0.015–0.017 0.014–0.018 <0.001
2 Ultrapure water 0.015 0.001 0.013–0.016 0.013–0.016
3 20% Sodium percarbonate 0.014 0.001 0.013–0.015 0.012–0.016
4 20% Chlorhexidine digluconate 0.017 0.001 0.016–0.018 0.015–0.019
5 Correga Tabs BioFormula 0.019 0.001 0.018–0.019 0.017–0.020
6 20% Citric acid 0.024 0.001 0.016–0.032 0.017–0.054
7 0.5% Sodium hypochlorite 0.029 0.003 0.027–0.031 0.023–0.031
8 5.0% Sodium hypochlorite 0.037 0.009 0.030–0.040 0.028–0.057

Post-hoc multiple comparisons: (1) versus (6)–(8) at p < 0.001; (2) versus (6)–(8) at p < 0.001; (3) versus (6)–(8) at p < 0.001; (4) versus (6)–(8) at p < 0.001; (5) versus (6)–(8) at p < 0.001; (6) versus (1)–(5) at p < 0.001; (7) versus (1)–(5) at p < 0.001; (8) versus (1)–(5) at p < 0.001.

Table 3

Descriptive statistics for R q measurements outcome

No. R q (µm) Statistical parameter p-Value
M SD 95% CI Min. – max.
1 Dry 0.020 0.002 0.019–0.021 0.017–0.023 <0.001
2 Ultrapure water 0.020 0.002 0.018–0.021 0.017–0.023
3 20% Sodium percarbonate 0.018 0.001 0.016–0.019 0.016–0.020
4 20% Chlorhexidine digluconate 0.022 0.002 0.020–0.023 0.019–0.024
5 Correga Tabs BioFormula 0.025 0.003 0.022–0.027 0.021–0.032
6 20% Citric acid 0.051 0.024 0.032–0.070 0.024–0.091
7 0.5% Sodium hypochlorite 0.045 0.004 0.042–0.048 0.036–0.050
8 5.0% sodium hypochlorite 0.054 0.012 0.044–0.064 0.041–0.079

Post-hoc multiple comparisons: (1) versus (6)–(8) at p < 0.001; (2) versus (6)–(8) at p < 0.001; (3) versus (6)–(8) at p < 0.001; (4) versus (6)–(8) at p < 0.001; (5) versus (6)–(8) at p < 0.001; (6) versus (1)–(5) at p < 0.001; (7) versus (1)–(5) at p < 0.001; and (8) versus (1)–(5) at p < 0.001.

Table 4

Descriptive statistics for R p measurements outcome

No. R p (µm) Statistical parameter p-Value
M SD 95% CI Min. – max.
1 Dry 0.070 0.008 0.064–0.077 0.060–0.082 <0.001
2 Ultrapure water 0.077 0.011 0.069–0.086 0.063–0.088
3 20% Sodium percarbonate 0.068 0.007 0.062–0.074 0.060–0.081
4 20% Chlorhexidine digluconate 0.077 0.021 0.061–0.093 0.024–0.091
5 Correga Tabs BioFormula 0.088 0.006 0.084–0.093 0.078–0.095
6 20% Citric acid 0.164 0.050 0.123–0.206 0.095–0.267
7 0.5% Sodium hypochlorite 0.250 0.043 0.218–0.283 0.183–0.317
8 5.0% Sodium hypochlorite 0.272 0.057 0.229–0.317 0.213–0.359

Post-hoc multiple comparisons: (1) versus (6)–(8) at p < 0.001; (2) versus (6)–(8) at p < 0.001; (3) versus (6)–(8) at p < 0.001; (4) versus (6)–(8) at p < 0.001; (5) versus (6)–(8) at p < 0.001; (6) versus (1), (2), (3), (4), (5), (6), and (7) at p < 0.001; (7) versus (1), (2), (3), (4, (5), and (6) at p < 0.001; and (8) versus (1), (2), (3), (4), (5), and (6) at p < 0.001.

Table 5

Descriptive statistics for R v measurements outcome

No. R v (µm) Statistical parameter p-Value
M SD 95% CI Min. – max.
1 Dry 0.063 0.010 0.055–0.070 0.050–0.085 <0.001
2 Ultrapure water 0.083 0.016 0.066–0.100 0.062–0.110
3 20% Sodium percarbonate 0.067 0.011 0.058–0.075 0.049–0.082
4 20% Chlorhexidine digluconate 0.067 0.013 0.057-0.076 0.042–0.085
5 Correga Tabs BioFormula 0.091 0.037 0.062–0.120 0.067–0.189
6 20% Citric acid 0.122 0.055 0.076–0.169 0.070–0.225
7 0.5% Sodium hypochlorite 0.084 0.021 0.068–0.101 0.064–0.121
8 5.0% Sodium hypochlorite 0.096 0.023 0.078–0114 0.072–0.143

Post-hoc multiple comparisons: (1) versus (5) and (6) at p < 0.001; (2) versus (6) at p < 0.001; (3) versus (6) at p < 0.001; (4) versus (6) at p < 0.001; (5) versus (1) and (6) at p = 0.030; (6) versus (1)–(5) and (7) at p < 0.001; (7) versus (6) at p = 0.006; and (8) versus (1), (3), and (4) at p = 0.027.

Table 6

Descriptive statistics for R Sm measurements outcome

No. R Sm (µm) Statistical parameter p-Value
M SD 95% CI Min. – max.
1 Dry 27.4 9.0 20.5–34.3 20.6–50.0 <0.001
2 Ultrapure water 26.1 5.3 21.2–31.0 19.3–35.7
3 20% Sodium percarbonate 29.1 10.9 20.7–37.5 19.5–49.7
4 20% Chlorhexidine digluconate 22.5 3.1 20.1–24.9 18.3–28.6
5 Correga Tabs BioFormula 33.2 22.0 16.2–50.1 21.4–90.4
6 20% Citric acid 58.2 29.0 27.7–88.7 28.2–100.9
7 0.5% Sodium hypochlorite 99.3 34.5 72.8–125.9 59.3–178.7
8 5.0% Sodium hypochlorite 62.6 14.5 51.5073.8 49.1–90.9

Post-hoc multiple comparisons: (1) versus (6)–(8) at p < 0.001; (2) versus (6)–(8) at p < 0.001; (3) versus (6)–(8) at p < 0.001; (4) versus (6)–(8) at p = 0.015; (5) versus (6)–(8) at p = 0.015; (6) versus (1), (2), (3), (4), (5) and (7) at p = 0.005; (7) versus (1)–(6) and (8) at p < 0.001; and (8) versus (1)–(5) and (7) at p < 0.001.

Table 7

Descriptive statistics for R mr (25%) measurements outcome

No. R mr (25%) Statistical parameter p-Value
M SD 95% CI Min. – max.
1 Dry 99.9 0.1 99.8–100.0 99.7–100.0 =0.002
2 Ultrapure water 99.3 1.1 98.4–100.1 97.2–100.0
3 20% Sodium percarbonate 99.9 0.1 99.9–100.0 99.8–100.0
4 20% Chlorhexidine digluconate 100.0 0.04 99.9–100.0 99.9–100.0
5 Correga Tabs BioFormula 99.8 0.3 99.6–100.0 99.3–100.0
6 20% Citric acid 97.0 2.8 94.8–99.1 91.9–100.0
7 0.5% Sodium hypochlorite 99.8 0.2 99.6–100.0 99.4–100.0
8 5.0% Sodium hypochlorite 99.6 0.4 99.3–99.9 99.0–100.0

Post-hoc multiple comparisons: (6) versus all the remaining compounds at p < 0.001.

3.1.1 Contact angle and SFE analysis

Table 8 shows the influence of different cleaning solutions on contact angle changes on Co–Cr samples. In both cases, when using water or diiodomethane, the contact angle between the solid and fluid phases decreases. For water, it drops from 75.5° for the dry group to 13.5° for the group stored in 5.0% sodium hypochlorite. As with changes in surface roughness, the use of a weak acid such as, for example, citric acid or a hypochlorite solution causes greater changes in the amount of SFE than with the other groups of cleaning agents. The same relationship can be observed to diiodomethane contact angle changes – it varies from 68.0° for the dry group to 14.6° for the positive control group. According to measured values, all samples have hydrophilic surfaces.

Table 8

Contact angle of water and diodomethane (mean with standard deviation) on Co–Cr surface after conditioning in dental cleaners

Group Water contact angle, θ (°) Diiodomethane contact angle, θ (°)
Dry (negative control) 75.7 ± 6.7 68.0 ± 9.4
Water 63.3 ± 6.7 56.7 ± 3.3
20% Sodium percarbonate 48.5 ± 10.8 40.1 ± 3.3
20% Chlorhexidine digluconate 40.0 ± 1.8 38.3 ± 3.6
Corega Tabs Bio Formula 28.5 ± 6.2 42.4 ± 8.2
20% Citric acid 28.9 ± 2.9 40.0 ± 9.6
0.5% Sodium hypochlorite 18.1 ± 2.8 24.5 ± 3.8
5.0% Sodium hypochlorite (positive control) 13.5 ± 2.4 14.6 ± 2.4

Calculated SFE values for each group are shown in Table 9. The SFE is the highest for the group cleaned with 5.0% NaOCl. Every denture cleaning solution causes an increase in dispersive and polar components alike. However, in the case of Corega Tabs Bio Formula (enzyme), 20% citric acid (diluted acid) and 0.5% NaOCl (alkaline hypochlorite) polar components rise more than three times. This tendency also corresponds with the increased value of roughness parameters (especially R Sm) in the same groups.

Table 9

SFE and its components of Co–Cr alloy after immersion in different denture cleaners

Group SFE (mJ/m2) γ d (mJ/m2) γ p (mJ/m2)
Dry (negative control) 30.775 17.948 12.827
Water 40.732 18.525 22.206
20% Sodium percarbonate 53.371 28.675 24.696
20% Chlorhexidine digluconate 58.651 28.174 30.477
Corega Tabs Bio Formula 64.580 24.654 39.926
20% citric acid 64.621 25.870 38.752
0.5% Sodium hypochlorite 70.742 31.359 39.384
5.0% Sodium hypochlorite (positive control) 72.781 38.937 33.844

The increasing value of SFE testifies to the increasing wettability of Co–Cr alloys after cleaning dentures, especially in weak acids and alkaline hypochlorites.

3.1.2 Microstructure

The Co–Cr alloy surface topography after storing in different denture cleaners is shown in Figure 1. Except for samples stored in NaOCl solutions, microstructures look similar. Some stains and surface discoloration on Co–Cr alloy stored in sodium hypochlorite solutions can be observed (Figure 1(7)). With increasing concentrations of NaOCl, dark structures occur in bigger areas (Figure 1(8)).

Figure 1 Digital microscopy images of Co–Cr alloy after storage: (1) dry, (2) in water, (3) 20% sodium percarbonate, (4) 20% chlorhexidine digluconate, (5) Corega Tabs Bio Formula, (6) 20% citric acid, (7) 0.5% NaOCl, and (8) 5.0% NaOCl. Magnification 1,500×.
Figure 1

Digital microscopy images of Co–Cr alloy after storage: (1) dry, (2) in water, (3) 20% sodium percarbonate, (4) 20% chlorhexidine digluconate, (5) Corega Tabs Bio Formula, (6) 20% citric acid, (7) 0.5% NaOCl, and (8) 5.0% NaOCl. Magnification 1,500×.

It is worth mentioning that SEM–EDS analysis (Table 10) shows that samples’ surfaces are without visible signs of the corrosive effect of denture cleaners. The exception is for samples stored in the 5.0% sodium hypochlorite solution. However, this is a positive control because of the high concentration of NaOCl which should act harmful for Co–Cr alloy. Some stains are visible in samples stored in 20% sodium percarbonate and 20% citric acid solution; however, EDS analysis did not show any changes in elements composition. Only in the positive control group, oxygen and chlorine occurred at EDS spectrums (Figure 2). We did not observe these elements even in a group with 0.5% NaOCl (Figure 3) or in the negative control group stored in a dry environment (Figure 4). This observation suggests that all classes of denture cleaning agents are safe for Co–Cr alloy during the simulation of 3 years using RPDs.

Table 10

SEM analysis of Co–Cr samples stored in different solutions at magnifications 2,000×, 5,000×, 10,000×

Magnification 2,000× Magnification 5,000× Magnification 10,000×
Dry
Water
20% Sodium percarbonate
20% Chlorhexidine digluconate
Corega Tabs Bio Formula
20% Citric acid
0.5% NaOCl
5.0% NaOCl
Figure 2 EDS analysis for a positive control group stored in 5.0% NaOCl.
Figure 2

EDS analysis for a positive control group stored in 5.0% NaOCl.

Figure 3 EDS analysis for the samples stored in 0.5% NaOCl.
Figure 3

EDS analysis for the samples stored in 0.5% NaOCl.

Figure 4 EDS analysis for the negative control group (dry).
Figure 4

EDS analysis for the negative control group (dry).

4 Discussion

Based on the obtained test results, it can be concluded that cleaning agents like water, alkaline peroxides, or disinfectants and their mixtures with enzymes and weak acids do not significantly affect denture surfaces cast from Co–Cr. After conditioning in the solutions of cleaning agents, the surface of the alloy did not develop to any significant degree during the time corresponding to the use of the prosthesis for 3 years. According to Curylofo et al. [18], using 20% citric acid solution or solutions of sodium hypochlorite in a concentration higher than 0.5% is harmful to Co–Cr alloy because R a increases over 0.2 μm, which cannot be clinically acceptable, due to promoting a bacteria adhesion. According to research carried out by Felipucci et al. [3] the citric acid solution and NaOCl solutions with concentrations higher than 0.05% may lead to a corrosion effect. The negative effect of using these cleaners is also discoloration and remaining stains. pH Changes (decreased value) also affect the corrosion resistance of the alloy, which may cause a roughness increase. The results from our tests for citric acid and NaOCl confirm that a more aggressive environment is harmful to metallic denture elements. According to Puscar et al.’s report [24] with increasing acidity of solution, corrosion resistance of cobalt–chromium alloy decreases. Morphological changes on the surface may be affected by Co-release. Rylska noted that in solutions with low pH values, the chemical passivation decreases susceptibility to electrochemical corrosion of Co–Cr dental alloy [25]. It is worth noting that strong acids inactivate surfaces stronger than weak acids by forming a passive layer on Co–Cr alloy surface with an oxide layer [26]. Many studies are focused only on R a measurements [27]. Arithmetic average roughness height is a widely used parameter, but as we see R a value do not fully characterize features of the surface like sharp or soft peak and valleys. Also, Zecchino [28] stated that R a is a parameter that characterizes only in general a surface topography. Different surfaces with the same R a range can have different surface morphologies, so it is necessary to use other R parameters to characterize differences in surface roughness. It should be emphasized that diluted acids develop Co–Cr surface more than other cleaning agents. The valleys (R v) on the surface become deeper. Its values rise mostly in citric acid and both NaOCl solutions to around 0.09 μm compared to 0.07 μm for dry storage samples. R v is an important parameter in the corrosive behavior of cleaning agents and Co–Cr alloy, because of its influence on the diffusion of active ions during corrosion [29]. On the corroded surface, a predominance of deep valleys is visible, and as Zecchino stated it is a correlation between a bigger R v value and greater corrosion [28]. We see that citric acid or NaOCl differs from other cleaning solutions and the negative control group and their R v values increase significantly. These classes of denture cleaning agents may affect more aggressively Co–Cr surface, than others. Surface picks (R p) are significantly higher, they rise from around 0.07 μm for dry storage Co–Cr to 0.273 μm for alloy immersed in 5.0% NaOCl, and it may affect occurring pitting on Co–Cr surfaces. Not only vertical parameters characterize surface character. Also, horizontal and hybrid parameters are important to determine surface properties like wettability, corrosion, friction, wear, and others [30]. The mean width of the profile elements R Sm also raises three times after storage in 0.5% sodium hypochlorite. Change of surface roughness influence is also on the wettability of materials. As per Uhorchuk et al.’s statement [31] surface roughness influence is on wetting characteristics, which correlate with changes in static contact angle. This is coincident with the Cassie-Wenzel theory and Nishioka et al.’s [32] results. Also, our investigation confirmed this thesis. Contact angle decreases as Co–Cr alloy surface roughness increases. The high value of SFE is undesirable due to higher bacteria adhesion. Bollen et al. [33] note that plaque accumulation increases the risk of periodontal inflammation and caries, so it is needed to keep the surface of prosthesis materials as smooth as possible. The side effect of increasing surface energy makes citric acid and sodium hypochlorite to unfavorable denture cleaners [32]. Also, Sobolewska reports that increased wettability of Co–Cr alloy may be a reason why bacteria adherer stronger. One of the factors that modulate the adhesive behaviors of bacteria proteins is SFE. Bacteria adhere stronger to surfaces with higher surface energy. On the other hand, water contact angle under 40° (highly hydrophilic surface) or over 130° (highly hydrophobic surface) seems to inhibit bacterial adhesion. Because of inconclusive results for modifying wettability and surface roughness [34], it was necessary to carry out an investigation, like SEM-EDS, but despite the positive control group, any other changes was observed.

Researchers show surface changes under microscopic observation in the case of using sodium hypochlorite solutions [35]. Dong observed also yellowish brown changes on the surface of Co–Cr alloy after immersion in weak acid [26]. There are also publications which show that other cleaning agents are safe for metal elements of the prosthesis. Curylofo et al. [18] state that the use of disinfectants like cetylpyridinium chloride is safe for metallic elements of RPDs for a period of 5 years, and in SEM–EDS analysis, any deleterious effect or changes in surfaces chemical composition was observed. The visual analysis [3] eliminates sodium hypochlorite and citric acid as safe for Co–Cr denture cleaners because of occurring tarnishes and presented stains. These observations also correspond with our results obtained from the SEM images. These side effects suggest that corrosion occurred, like pitting, but EDS analysis has shown changes in elements composition only for samples stored in 5.0% NaOCl, where chlorine and oxygen occurred.

5 Conclusions

On the basis of our measurements and their limitations (used solution of active substances not commercial cleaners with the selected concentration of the active substance), it can be concluded that as a class of denture cleaning agents, alkaline hypochlorites and diluted acids could cause deleterious effects on Co–Cr alloy surfaces in RPDs. Immersion in alkaline hypochlorites and diluted acids leads to an increased all roughness parameters compared to dry storage samples (negative control group) and growth of SFE of Co–Cr especially polar compound of SFE, which may cause better biofilm adhesion even in 3 years of simulation of use. Also, some stains and discoloration are remaining on the Co–Cr alloy surfaces, when alkaline hypochlorite or diluted acid was used. Other classes of denture cleaning agents like alkaline peroxides, enzymes, and disinfectants do not change significantly the values of the surface parameters and the SFE value. Hence, it can be concluded that the above-mentioned cleaning agents do not have harmful effects on the metallic components of RPDs, so they are suitable for RPD cleaners.

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  1. Funding information: The authors state no funding is involved.

  2. Author contributions: Joanna Nowak – conceptualization, data curation, formal analysis, investigation, methodology, resources, writing- original draft, writing – review and editing, Klaudia Steinberg – investigation formal analysis, visualization, writing original draft, Jerzy Sokołowski – project administration, funding acquisition, Kinga Bociong – conceptualization, investigation, methodology, project administration, supervision, validation, writing – review and editing

  3. Conflict of interest: The authors state no conflict of interest.

  4. Ethical approval: The conducted research is not related to either human or animal use.

  5. Data availability statement: The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-03-21
Revised: 2022-07-12
Accepted: 2022-08-11
Published Online: 2022-09-28

© 2022 Joanna Nowak et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  155. Special Issue on Pharmacology and metabolomics of ethnobotanical and herbal medicine
  156. Cynarin inhibits PDGF-BB-induced proliferation and activation in hepatic stellate cells through PPARγ
  157. Special Issue on The 1st Malaysia International Conference on Nanotechnology & Catalysis (MICNC2021)
  158. Surfactant evaluation for enhanced oil recovery: Phase behavior and interfacial tension
  159. Topical Issue on phytochemicals, biological and toxicological analysis of aromatic medicinal plants
  160. Phytochemical analysis of leaves and stems of Physalis alkekengi L. (Solanaceae)
  161. Phytochemical and pharmacological profiling of Trewia nudiflora Linn. leaf extract deciphers therapeutic potentials against thrombosis, arthritis, helminths, and insects
  162. Pergularia tomentosa coupled with selenium nanoparticles salvaged lead acetate-induced redox imbalance, inflammation, apoptosis, and disruption of neurotransmission in rats’ brain
  163. Protective effect of Allium atroviolaceum-synthesized SeNPs on aluminum-induced brain damage in mice
  164. Mechanism study of Cordyceps sinensis alleviates renal ischemia–reperfusion injury
  165. Plant-derived bisbenzylisoquinoline alkaloid tetrandrine prevents human podocyte injury by regulating the miR-150-5p/NPHS1 axis
  166. Network pharmacology combined with molecular docking to explore the anti-osteoporosis mechanisms of β-ecdysone derived from medicinal plants
  167. Chinese medicinal plant Polygonum cuspidatum ameliorates silicosis via suppressing the Wnt/β-catenin pathway
  168. Special Issue on Advanced Nanomaterials for Energy, Environmental and Biological Applications - Part I
  169. Investigation of improved optical and conductivity properties of poly(methyl methacrylate)–MXenes (PMMA–MXenes) nanocomposite thin films for optoelectronic applications
  170. Special Issue on Applied Biochemistry and Biotechnology (ABB 2022)
  171. Model predictive control for precision irrigation of a Quinoa crop
https://doi.org/10.1515/chem-2022-0201
Schlagwörter für diesen Artikel
Co–Cr alloys; denture cleaners; roughness; surface free energy; surface topography
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Artikel in diesem Heft

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  5. Simulation of gamma-ray shielding properties for materials of medical interest
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  9. Design of a Robust sliding mode controller for bioreactor cultures in overflow metabolism via an interdisciplinary approach
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  116. Corrigendum to “Statistical analysis on the radiological assessment and geochemical studies of granite rocks in the north of Um Taghir area, Eastern Desert, Egypt”
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  118. Corrigendum to “Mechanical properties, elastic moduli, transmission factors, and gamma-ray-shielding performances of Bi2O3–P2O5–B2O3–V2O5 quaternary glass system”
  119. Erratum
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  154. Hydrocracking optimization of palm oil over NiMoO4/activated carbon catalyst to produce biogasoline and kerosine
  155. Special Issue on Pharmacology and metabolomics of ethnobotanical and herbal medicine
  156. Cynarin inhibits PDGF-BB-induced proliferation and activation in hepatic stellate cells through PPARγ
  157. Special Issue on The 1st Malaysia International Conference on Nanotechnology & Catalysis (MICNC2021)
  158. Surfactant evaluation for enhanced oil recovery: Phase behavior and interfacial tension
  159. Topical Issue on phytochemicals, biological and toxicological analysis of aromatic medicinal plants
  160. Phytochemical analysis of leaves and stems of Physalis alkekengi L. (Solanaceae)
  161. Phytochemical and pharmacological profiling of Trewia nudiflora Linn. leaf extract deciphers therapeutic potentials against thrombosis, arthritis, helminths, and insects
  162. Pergularia tomentosa coupled with selenium nanoparticles salvaged lead acetate-induced redox imbalance, inflammation, apoptosis, and disruption of neurotransmission in rats’ brain
  163. Protective effect of Allium atroviolaceum-synthesized SeNPs on aluminum-induced brain damage in mice
  164. Mechanism study of Cordyceps sinensis alleviates renal ischemia–reperfusion injury
  165. Plant-derived bisbenzylisoquinoline alkaloid tetrandrine prevents human podocyte injury by regulating the miR-150-5p/NPHS1 axis
  166. Network pharmacology combined with molecular docking to explore the anti-osteoporosis mechanisms of β-ecdysone derived from medicinal plants
  167. Chinese medicinal plant Polygonum cuspidatum ameliorates silicosis via suppressing the Wnt/β-catenin pathway
  168. Special Issue on Advanced Nanomaterials for Energy, Environmental and Biological Applications - Part I
  169. Investigation of improved optical and conductivity properties of poly(methyl methacrylate)–MXenes (PMMA–MXenes) nanocomposite thin films for optoelectronic applications
  170. Special Issue on Applied Biochemistry and Biotechnology (ABB 2022)
  171. Model predictive control for precision irrigation of a Quinoa crop
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Heruntergeladen am 20.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/chem-2022-0201/html
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