Hydrophobic silica is a form of silicon dioxide (commonly known as silica) that has hydrophobic groups chemically bonded to the surface. The hydrophobic groups are normally alkyl or polydimethylsiloxane chains. Hydrophobic silica can be processed in different ways; such as fumed silica, precipitated silica, and aerosol assisted self assembly, all existing in the form of nanoparticles.
Structure
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Hydrophobic silica has an orthorhombic crystal structure (its space group name is Pmna under the bipyramidal point group).[1] Orthorhombic structures are the product of stretching a cubic lattice along two of its orthogonal pairs, resulting in a rectangular prism shaped crystal structure.
Properties
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Hydrophobic silica displays water resistant properties because of its nanostructure and chemical properties. When applied to a surface of a material, the nanoparticles adhere to the host material and prevent liquids from permeating the rough texture. The water only comes into contact with the tips of the nanoparticles coating the outside of the material. Due to lack of attraction, the water is repelled from the hydrophobic silica.[3]
Processing
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Initially, silica is hydrophilic due to the presence of the silanol (Si-OH) groups on the surface of the particle. These silanol groups can chemically react with various reagents to render the silica hydrophobic. There are many different methods of processing silica to become hydrophobic, mainly by adding hydrocarbon groups.
Hydrophobic fumed silica
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Fumed silica can react with chlorosilanes in a fluidized bed reactor at 400 °C[4]
Hydrophobic precipitated silica
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Precipitated silica can be hydrophobized with e.g. alkylchlorosilanes or trimethylsilanol in the precipitated solution. The hydrophobised silica is filtered, washed, dried, and tempered to 300400 °C to finish the reaction.[5]
Hydrophobic plasma polymer coated silica
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Silica particles can become hydrophobic through plasma polymerization. In this process, plasma polymerized 1,7-octadiene (ppOD) (related to the diene hydrocarbons) is used to deposit polymer films onto the silica particles. The ppOD films are deposited through the use of radio frequencies, along with a reactor containing a rotating chamber. Using low specific energy plasma conditions, the ppOD films chemically render the silica particles hydrophobic.[6]
By using the ppOD films, the hydrophilic polar Si-OH groups in the polymer itself are concealed by non-polar CxHy hydrocarbon groups, so when it's applied as a film to the silica particles, they become hydrophobic as well.[7]
Aerosol assisted self-assembly
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The goal of this process is to quickly and continuously create nanostructured particles deriving from a colloid precursor containing a solvent and silica particles. Aerosol assisted self assembly is a one step process with a high production rate. The process takes a few seconds in terms of reaction time, and there's no requirement for heating and chemically treating the particles after development.
The first part of the process is to create the colloid precursor which consists of the silica nanoparticles and the solvent. The initial silica nanoparticles are in an amorphous crystalline phase and the solvent is composed of trimethylsilyl chloride (TMCS) and ethyl alcohol. To synthesize hydrophobic nanostructured silica using this method, the colloid precursor containing the solvent and silica particles is sprayed by an aerosol generator. The droplets are then transported by a carrier gas to a furnace where they are heated. Upon entry into the furnace, the ethyl alcohol evaporates from the colloid precursor, allowing self-assembly to occur between the silica particles and the surface treating agent, TMCS.
The results of this process causes the silica particles to group together to combine into spherical nanostructured particles. By grouping these silica nanoparticles into a nanostructured particle, a certain percentage of porosity develops within the nanostructure related to the amount of TMCS concentration. Increasing the amount of TMCS concentration reduces the specific surface area of the silica nanostructured particles. The exhibited hydrophobicity is a result of the chemical reaction occurring between the silica particles and the TMCS. When the original SiO2-OH groups are replaced with hydrolytically stable Si(CH3) groups, this hydrophobicity occurs due to the prevention of the silica particles from interacting with water.[8]
Applications
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Hydrophobic silica is used to solve technical problems in a number of products including, but not limited to, paints, inks, adhesives, plastics, coatings, toners, defoamers, silicone rubber, sealants, cosmetics, food additives, polyester resins, cable gels, and greases. It's often manufactured as both single and multiphase composites in order to enhance properties such as dispersion, stability behavior, resistance to water, and functionality. Hydrophobic silica has been added to aerogels to increase their ability to adsorb organic pollutants without disrupting the network structure.[9]
Treated aggregated fumed silica
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Hydrophobic silica can be used to treat other surfaces to become hydrophobic, this is due to the morphology of the silica particles once they adhere to their host. The silica particles then alter the surface of its host material resulting in a hydrophobic surface.
Aggregated fumed silica can be applied to large surfaces to render them hydrophobic. Micro and nanoscale structures, resembling ball and block like forms, are attributed to the hydrophobic characteristics. Due to the change in the original surface's texture, the roughness of the surface causes its hydrophobicity to increase. This is because when water comes into contact with the rough surface, it only touches the tips of the rough texture and doesn't permeate any deeper through the rest of the air occupied structure. The water can't spread through the surface, thus yielding hydrophobic properties.[3]
Additional applications
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References
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There are two factors for making a super-waterproof coating: rough surface and rough surface correction. The StÖber method is one of the methods for synthesizing silica nanoparticles to make a rough surface. The purpose of designing the experiment is to optimize the parameters of the StÖber method to create a suitable rough surface and a surface modifying agent for the superhydrophobic coating. Finally, TEOS was investigated as a fixed precursor and the effect of deionized water (A), ethanol (B), ammonia (C), and PDMS (D). The central composite design (CCD) method was used for the design.
Hebei Silicon Research Electronic Materials Co., L Product Page
In this method, the experimental design is performed by specifying the actual levels and coding levels for each parameter (i.e. for high levels+1, central levels zero, and low levels 1). Actual and coded levels are shown in Table 1. The experimental design matrix with the levels encoded by the software is shown in Table 2. The obtained contact angle was entered into the software as a response. The actual and coded performance levels were used to conduct practical tests. Surface ( ) (experiment no. 10) with a contact angle of 110.4° was the lowest and surface () (experiment no. 6) with a contact angle of 166.5° was the highest.
Table 1 Actual levels and coded reaction parameters.Full size table
Table 2 Experimental design matrix with coded surfaces.Full size table
According to the obtained statistical data and the ANOVA table, it is a good model that has the following two conditions:
1.
p-value<0/05
2.
In the selected model, R2 should be closer to one (Table 3).
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R2 checks the quality of the experimental data with the model and the best value is one.
Adj- R2 is the modified value of R2, which also takes into account the degree of freedom (number of factors).
The Predicted R2 of 0. agrees with the adjusted R2 of 0.; That is, the difference is less than 0.2.
Adeq Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 71.932 indicates an adequate signal. This model can be used to navigate the design space.
Statistical data were analyzed using the response level method and regression equation:
Regression equation in encrypted units:
$$\begin{gathered} {\text{CA }} = { 165}.{683 } + { 2}.{942}0{8}*{\text{A }} + \, - {1}.{}*{\text{B }} + \, 0.{}*{\text{C }} + { 1}.{557}0{8}*{\text{D }} + { 6}.{}*{\text{AB }} + \, - {2}.{44}0{63}*{\text{AC }} + \, 0.{}*{\text{AD }} \hfill \\ + - 0.{}*{\text{BC }} + \, - {8}.{}*{\text{BD }} + \, - {4}.{}*{\text{CD }} + \, - { 1}0.{}*{\text{A}}^{{2}} + \, - {1}0.{}*{\text{B}}^{{2}} + \, - {8}.{}*{\text{C}}^{{2}} + \, - {6}.{ }*{\text{ D}}^{{2}} \hfill \\ \end{gathered}$$
The quadratic response level model is used to evaluate the effectiveness of the parameters and the accuracy of the model.
The model F value of 528.44 indicates that the model is acceptable. There is only a 0.01% chance that an F value of this magnitude will occur due to the disturbance.
P-values less than 0. indicate that the model parameters are significant. In this case, A, B, C, D, AB, AC, AD, BD, CD, A2, B2, C2, and D2 are acceptable parameters. Values that have p values greater than 0., such as the BC parameter, indicate that this parameter does not affect the test conditions. Changing the model may improve your model if many model parameters are not affected.
The Lack of Fit F-value of 3.16 indicates that a mismatch to a pure error is not acceptable. There is an 10.78% chance that a mismatch of the F-value of this magnitude will occur due to a disturbance. Non-significant disproportion is good because we want the model to fit Table 4.
Table 4 Analysis of variance.Full size table
To troubleshoot the results obtained from the software the four graphs of normal probability, residuals vs. predicted, predicted vs. actual and Box-Cox plot for power transforms are used. In Fig. 2a, the normal probability diagram shows that the residuals follow a normal distribution, therefore they follow a straight line. Even with normal data, expect some scatter. Figure 2b (residuals vs. predicted) indicates that the residuals are bullish against the predicted response values. This plot tests the assumption of constant variance. The graph should have a random scatter and according to the graph the data follows a random scatter. Figure 2c shows predicted vs actual. A graph of the predicted response values versus the actual response values. The purpose is to detect a value, or group of values, that are not easily predicted by the model. The Box-Cox chart is used to determine the strength of metamorphism consistent with experimental data (Fig. 2d). The blue line in the diagram shows the model change and the green line shows the best lambda value. The red line indicates the 95% confidence interval associated with the best amount of lambda. It is said that a model is qualified for the blue conversion line between the red lines and the green line on the conversion curve to form a black and white curve. The graph shows that the blue transition line between the green line and the red line shows that the model matches the experimental results.
Figure 2Diagnostics diagrams (a) normal probability, (b) residuals vs. predicted, (c) predicted vs. actual and (d) Box-Cox plot for power transforms.
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Figure 3 shows the effect of the selected parameters on the contact angle. The graph follows a certain pattern for all parameters. With increasing water volume from 8 to 10, the contact angle gradually increased from 152.34° to 165.83°. Subsequently, with an increase from 10 to 12, the contact angle decreased from 165.83° to 158.45°. For the parameters of ethanol, ammonium hydroxide, and PDMS, the same changes occurred, i.e. the contact angle increased from 1 to zero and decreased from zero to+1.
Figure 3Influence of single variables on contact angle.
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In Fig. 4, the interaction and three-dimensional diagrams of the model are obtained to estimate the interaction between the variables and the contact angle, while the other variables are kept at their zero levels and the others change in the experimental range. In Fig. 4, as can be seen, the interaction of the binary parameters with each other is like the effect of the parameters individually. The difference between the interaction of the binary parameters with each other is in the angle between the two diagrams. The higher the angle of the two graphs, the greater the interaction, and if they are parallel, they have less or no interaction. According to the ANOVA table, the BC parameter is unacceptable, and the angle between the parameter BC is very small and they are almost parallel.
Figure 4The effect of binary variables on the contact angle.
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One of the purposes of test design is to optimize process parameters to obtain the highest contact angle. The size of the contact angle was determined to be 162°. According to the CCD design, the optimal conditions for the preparation of SiO2 sol are shown in Fig. 5. The experimental contact angle size for the optimized sample is compared with the predicted contact angle in Fig. 5. The results showed that the size of the experimental contact angle corresponds well with the predicted contact angle and shows that the CCD surface response surface method is an efficient method for preparing ultra-waterproof coatings with a contact angle above 160°.
Figure 5Optimal values of process parameters.
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Optimized sample according to statistical data analysis, as mentioned earlier, was prepared by StÖber method with TEOS as precursor, deionized water of hydrolyzing agent, solvent ethanol, ammonium hydroxide catalyst, and PDMS as a hydrophobic agent. As a result, the effect of experimental parameters on superhydrophobic coatings was investigated.
The contact angle is the angle between the surface on which the liquid is located or the point of connection of the liquid on the surface. Static and dynamic contact angles are of its types. The method of measuring the contact angle is called the baseless droplet method. Can be used. The contact angle (CA) of the sliding angle (SA) obtained for the optimized sample is 162° and 5°, respectively (Fig. 6).
Figure 6Contact angle between the drop and the glass surface.
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Figure 7 shows the X-ray diffraction pattern of nanoparticles prepared by the StÖber method. As shown in the figure, no diffraction peaks are observed except for broadband with a 24-degree center (JCPDS No. -29) which represents a completely amorphous structure. But using Highscore plus software, it is shown that a small part of the sample has a crystalline structure. The marked peaks are related to hexagonal (JCPDS number -080-01) and quadrilateral (JCPDS number -079-01) and (JCPDS number -082-01) crystal structures.
Figure 7XRD pattern of the optimized sample.
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Infrared spectroscopy (FT-IR) was performed at room temperature to investigate the chemical bonds created in the optimized sample. As shown in Fig. 8, the peaks of cm1 and cm1 are symmetrical tensile vibration and flexural vibration of the OH bond, respectively, due to the incomplete density of the silanol group35,36. The range of 400 cm1, known as the fingerprint area, indicates silicon bonds. The peak cm1 and 808 cm1 represent the symmetric and asymmetric tensile vibrations of the SiOSi bond and the peak 466 cm1 represents the flexural vibrations of the SiOSi37,38. The peak of 947 cm1 is due to the flexural vibrations of the SiOH bond39. As can be seen, the aging period causes the formation of a SiOSi tensile and bending bond in the sample. This creates a resistant coating on the surface of the glass.
Figure 8FT-IR spectrum of the optimized sample.
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Scanning electron microscope images of the optimized sample at different magnifications are shown in Fig. 9. As seen in Fig. 9a, the nanostructures have a spherical morphology with a size of about 250 nm. The reason for the growth of nanoparticles is due to the use of the hydrothermal method to create a suitable uneven surface on the glass surface. The shape and size of the nanostructure have an essential effect on creating a super hydrophobic coating. As can be seen, the spherical nanostructures have made a rough surface on the glass. Figure 9b,c show the scanning electron microscope image of the surface and cross-section. The roughness of the glass surface indicates a rough surface for the super-hydrophobic coating. The growth process of this superhydrophobic coating is island-layer, which is a state between layer-by-layer growth and island growth, one or more monolayers are formed and then the islands are completed. Another name for this growth process is Stransky-Kristanov. In this growth mode, a mismatched network may be formed between the coated layer and the substrate. The grain size of the thin layer that is formed on the substrate depends on the speed and temperature of the layer. The thickness of this superhydrophobic coating is reported to be 1.06 µm.
Figure 9(a) SEM images of the optimized sample, (b) SEM images of the glass surface, and (c) cross-sectional SEM images of silica-coated glass.
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Figure 10a shows the X-ray energy diffraction spectrum for the optimal sample. As shown in the figure, the purely synthesized optimized sample is composed of silicon and oxygen elements. Figure 10b shows the X-ray diffraction spectrum from the glass surface. Due to the use of PDMS as a hydrophobic agent on the glass surface, in addition to silicon and oxygen, carbon is also seen. The gold peak seen in the figure is due to the conductivity of the surface for SEM analysis.
Figure 10EDS for the (a) optimized sample and (b) glass surface.
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Atomic force microscopy images of the mean square root roughness for the optimized sample are estimated in Fig. 11. As can be seen in the figure, rough surface roughness is created on the glass surface to create a super-hydrophobic coating. The maximum and minimum of these surface roughnesses were measured at 2.6 and 1.2 μm, respectively. The root means square roughness for the sample optimized by Gwyddion software was calculated to be 0.121 μm.
Figure 11AFM images of the optimized sample.
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As can be seen in the SEM images, the optimized sample has the same particle size distribution and DLS analysis was performed to determine the particle size distribution range. The particle size range is between 255 and 396.1 nm and, as shown in Fig. 12, the particle size distribution diagram is very narrow. The average particle size is 291.456 nm. Coating thickness and surface roughness depend not only on particle size but also on size distribution. Therefore, as much as the particle size is properly distributed, the surface roughness increases and becomes uniform.
Figure 12Size distribution of the optimized sample.
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The stability of superhydrophobic coating was investigated in three media: acidic, neutral, and alkaline. 1 M KOH and 1 M HCl solutions were prepared for the resistance of the super-hydrophobic coating in alkaline and acidic environments, respectively. Figure 13 shows the effect of different environments on the contact angle. The superhydrophobic coating was immersed in 10 ml for 24 h. It was then dried at room temperature. As can be seen in the figure, the play environment (pH: 13.5) has a great impact on the coating so that the drop contact angle of the super-hydrophobic range reaches the boundary between hydrophobic and hydrophilic, in which case the result can be the game environment causes corrosion of the coating and the use of this coating in play environments is not recommended. The contact angle obtained from the acidic environment (pH: 1) indicates that the coating has good resistance and this feature can be very effective in coating against acid rain. The use of deionized water is used to accurately assess the strength of the coating when in contact with water. Therefore, to prevent the effect of temperature on the coating, this test was performed at a temperature of 25°. The contact angle resulting from the immersion of the coating in deionized water indicates the very good resistance of the coating in this environment.
Figure 13Effect of pH on the contact angle of the optimized sample.
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