DESIGNING SILICONE PSAs - A NEW LOOK AT RESIN

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1 DESIGNING SILICONE PSAs - A NEW LOOK AT RESIN Roy M. Griswold, Sr. Chemist, GE Silicones, Waterford, NY Mary Krenceski, Materials Technologist, GE Silicones, Waterford, NY Jeffery Wengrovius, Principal Chemist, GE Silicones, Waterford, NY Abstract Silicone pressure sensitive adhesives (PSAs) were introduced into the market in the mid-1950's and have seen enhanced properties developed to meet increasing performance demands. The subject of this paper is the use of the Six-Sigma approach, focused on the role of low molecular weight, highly condensed silicone resin in determining PSA performance. Statistical software is used to design a PSA with improved adhesive tack and peel adhesion properties. Supporting data from dynamic mechanical thermal analysis and a Texture Analyzer are used to understand the silicone PSA behavior. Introduction Silicone pressure sensitive adhesives are similar to their organic counterparts and consist of a silicone MQ resin, polysiloxane polymer and solvent. MQ resins are low molecular weight, highly condensed silicone structures with multiple quatrifunctional sites (abbreviated: Q) and silicon-oxygenmethyl group species (abbreviated: M) in the structure. Though the basic silicone PSA composition has remained constant since introduction in the mid-1950s, several studies and product improvements were made to meet increasing performance demands. Adhesive performance responses included tack, peel adhesion, shear resistance, and clean removal properties with changes in the MQ resin, polymer or process of adhesive manufacture. As markets are becoming increasingly competitive, the importance is heightened for speed in introduction of products with the associated reduction in costs and increased product consistency. Implementation of Six, Sigma methodology along with computer aided statistical software are key to achieving these goals. Silicone adhesive performance properties are a balance that is controlled by the MQ resin and polysiloxane molecular weights, their ratio and the process of manufacture. As the ratio of MQ resin to polymer increases the peel adhesion increases but the tack reaches a maximum that is dependent on the above-mentioned factors. In the past this has limited adhesives to either providing high tack with low peel or high peel with low tack. Work in our labs using the Six-Sigma methodology and statistical Design of Experiments (DOE) software with the capability to functionally extrapolate has yielded new adhesives that provide both high tack and high peel adhesion while extending high temperature lap shear performance. These are GE Silicones polydimethyl siloxane based Silgrip PSA910 and polydimethy diphenyl siloxane based Silgrip PSA950. The objectives of this paper are to give an overview for the use of the Six-Sigma process with focus on the use of statistical DoE to generate the complex design spaces for the polydimethyl siloxane PSA mentioned above. This PSA design space is key to the capability to functionally extrapolate designs of a silicone adhesive meeting specific property requirements. The benefits associated in using the Six- Sigma methodology with statistical based DoEs include decreased development time, a formulation

2 connected to customer requirements that are Critical to Quality (CTQ), robust manufacturing process and product consistency. The focus for this paper is to understand the role that the MQ resin plays in determining adhesive properties as defined by tack, peel adhesion and lap shear testing. Dynamic mechanical analysis (DMA) as a function of temperature as well as a Texture Analyzer were used to further understand the adhesive properties. Designing for Six-Sigma An in depth review of the Six-Sigma methodology is beyond the scope of this paper. However an overview is valuable to illustrate how the topic of this paper, the role that MQ resin plays in a silicone PSA, fits within the Six-Sigma process. Figure 1 gives an overview summarizing steps involved in a cross-functional team discussion(s) with customer(s) to identify the market or customer needs, selection of factors which are Critical to Quality (CTQs), selection of a team chartered with identifying the specific program objectives, determining quantitative performance standards (metrics) and measurement capabilities (Gage Repeatability & Reproducibility). In this process the use of a Quality Functional Deployment (QFD) analysis further defines the scope of the project prior to being launched. At this point the program is launched with the potential internal variables (x's) being identified along with associated factors that affect the responses (y's) required to satisfy customer CTQs (Y's). These internal variables (x's) are either screened for importance in a statistical DoE or if historical data and experience have already identified the key variables, one is justified to go to an optimization DoE to generate the design spaces. In our work the latter approach was used since prior analysis of historical data identified the internal variable (x's) and potential noise factors. Analysis of the experimental data fitting a surface to each response is followed by optimization and functional extrapolation of a design space. This capability is only as good as the validation runs for a specific formulation meeting the CTQs. Once validation trials confirm the design space is valid, it becomes a valuable tool to extrapolate formulations meeting CTQs and variances for given process tolerances. This information is essential for robustness in scale-up and product consistency.

3 i, i Define and Measure - Identify and Select CTQs - Develop Team - Define Performance Metrics - Validated Metrics System Analyze - Establish Process Capability - Define Performance Objectives - Identify Variation Sources Improve - DoE for Potential Variables - Define Variable Relationships - Tolerances Control - Validate Design - Define Process Capability - Implement Process Controls Figure 1. Designing for Six-Sigma

4 MQ Resin A silicone PSA composition consists of an MQ resin that acts as a tackifier, a gum that can be polydimethyl or polydimethyl-diphenyl siloxane based, a solvent and coupling agent to enhance cohesive strength. The MQ resin can compositionally be varied through changes in the M to Q ratio along with process parameters in either a continuous or batch process. These resins contain residual silanol functionality, which is condensed in the adhesive process to increase cohesive strength. Figure 2 gives the empirical structure and Figure 3 a computer-simulated model for a typical MQ resin. Since changes in resin molecular weight are related to the 60 wt.% solids solution viscosity, the solution viscosity (Ostwald Viscometer) will be used in this paper. In this study several variations in the MQ resin viscosity were prepared via M/Q ratio and process variable controls. These were then converted into adhesives described later. [(CH3)3Si]xSiO1/2 X = Figure 2. Empirical Formula for MQ Resin Figure 3. MQ Resin Molecular Structure

5 Adhesive Formulation An interest in extending PSA performance beyond current technology led to applying the Define/Measure and Analyze phases to define and internalize the CTQs. The objective for the program was to design an adhesive with both high tack and high peel adhesion while retaining high temperature lap shear strength. In Table 1 the customer CTQs (Y), internal y's and control variables (x's) are summarized. All noise factors such as catalyst type and concentration, reflux temperature and time, use of additives and agitation rates were held constant. Table 1. Listing of Customer CTQs and Internal Test Methods and Variables Custom~r.CTQs_.ff's):. ~. Increased Peel Adhesion Increased Tack Higher Temperature Static Lap Shear Customer Y's.t~!nterna!y,s):... lnternfly,s =t~variab!e x!s) PSTC-1 Peel Adhesion Polyken Probe Tack, lcm/sec, 1 sec Static Lap Shear, 500g, 550 F Resin/Gum Ratio, Gum Viscosity, Gum Functionality (Methyl vs. Phenyl) Resin/Gum Ratio, Resin Viscosity Resin/Gum Ratio, Resin Viscosity Before conducting any experiments, the metrics and test capabilities were defined. Table 2 summarizes the results from Gage Repeatability & Reproducibility (GR&R) studies of the test methods using two adhesives that covered the current extremes for either a high tack/low peel or high peel/low tack adhesive. As can be seen, the sigma-gage for peel adhesion was constant therefore the variance over the design space was treated as constant. However, the Polyken Probe tack sigma-gage was not uniform over the expected range. Greater variability was found at higher tack values. Therefore tack variance over the design space could not be treated as a constant. Knowing this prior to any experimental work permits the developer to map a response variance for later use in defining scale-up tolerances. i / P

6 Table 2. Summary of GR&R for Adhesive Testing Peel Adhesion 20 minute dwell l~gage 0.5 oz/in. (14 g/in.) Tack, 100g/cm2 29 g/cm 2 1 see., 1 cm/sec. 76 g/cm 2 for 500 g/cm 2 tack for g/cm 2 tack It is at this point that selection of a DoE software and setting up the experimental design becomes important. Today, several general-purpose statistical packages exist that include basic DoE functions. There are also specialized packages that can do much more for DoE practitioners. One of these is Design-Expert V6.0.4 from Stat-Ease in Minneapolis, MN. This software is comprehensive in handling the analysis, determining terms (y's) to be included in the model, computing all coefficient values, and checking the lack-of-fit statistic to assess the model fit. The capability to functionally extrapolate formulations when response balancing is needed, as in the case of silicone adhesive tack and peel adhesion, is of added value for using this software. Since curvature was expected and the factors defined from experience, a quadratic model, based upon a rotatable central composite design (CCD) DoE using Design Expert software was selected to define the PSA design space. Repetitions of center points were included to test for curvature and lack-offit. All adhesive preparations and testing conditions were controlled to minimize noise. Responses were test averages of several values that were within the sigma-gage for each test. The initial CCD was augmented to look at lower viscosity MQ resin after analysis identified its ability to increase tack. Figures 4 and 5 are from the augmented DoE and Figure 6 is from the initial DoE. In Figure 5 the response surface for tack as a function of resin/gum ratio and resin viscosity variation is shown. It is clear that tack has a maximum at a certain resin/gum ratio. It is interesting that as the resin viscosity decreases the curvature for the maximum broadens. The importance of this observation becomes clear by looking at the peel adhesion response surface, which is planar with respect to resin/gum ratio. This can be seen in Figure 6, which depicts peel adhesion as a function of resin/gum ratio and resin viscosity. Therefore the broadening at lower MQ resin viscosity from Figure 5 suggests that a lower viscosity MQ resin would permit higher MQ resin/gum ratios thus increasing peel adhesion without decreasing the tack property. However, Figure 7 reveals that as resin viscosity is decreased the lap shear for uncatalyzed adhesives decreased. While the DoE indicated tack and peel adhesion could be increased, it failed to specify how to optimize lap shear. This is where the technical expertise of the developer is required! Through a proprietary technology the decreased lap shear from the DoE design space was resolved without compromising the high tack and peel adhesion properties observed from the DoE. 10

7 DESIGN-EXPERT Plot Tack, g/c~a 11oo g/cm 2 Tack Broadened Curvature 275 ~~i:iiiii!i~iiii!ii:iii~ii 1.q Resin/Gum Ratio 1'~ Resin Viscosity, 60% Solids Figure 5. Resin Viscosity versus Polyken Probe Tack DESIGN-EXPERT Plot Peel Adhesion Peel Adhesion, oz/in Resin/Gum Ratio Resin Viscosity, Solids Figure 6. Resin Viscosity versus Peel Adhesion (PSTC-1) 11 ~..

8 DESIGN-EXPERT Plot Lap Lap Shear, minutes 70o 7 600,500" 400" 300' Resin/Gum Ratio 1~ Resin Viscosity, Solids Figure 7' Resin Viscosityversus Lap Shear for Uncatalyzed Adhesive Dynamic Mechanical Analysis Response to Resin Viscosity Changes Adhesives were selected from the DoE that varied the MQ resin viscosity over the design space while all other factors were held constant. They were studied using a temperature sweep from -140 C to 200 C, 3 C/min at 10 rad/sec, 1% strain in a dynamic mode with a Rheometrics Dynamic Analyzer (RDA I1). 2 The tan 5 response versus temperature, as shown in Figure 8, depicts a typical curve showing the thermal transitions of the silicone gum and the MQ resin rich phase. It is the resin rich phase transition that dominates the physical properties observed at room temperature. As Figure 9 shows, the transition increased as resin viscosity increased. This corresponds to a decrease in tack observed as the resin viscosity increased as shown previously in Figure 4. 12

9 1.0- Resin richiphase I 1 1 "=1 l= i ' i ~.~... ~... ~....f_ o.o so.o loo.o 15o.o Temp [ Cl Figure 8. Tan ~5 from dynamic mechanical analysis as a function of temperature Tan Delta Vs. Resin Viscosity y = x x R 2 = ~" i '! i ' 1 ' '!! I... ' ' i Resin Viscosity@60% Solids Figure 9. Resin-rich phase transition temperature versus Resin Viscosity Texture Analyzer Response to Resin Viscosity Changes in PSAs Adhesive bonding/debonding processes involve contact time, contact force, surface roughness, surface and interfacial tensions and viscoelastic properties. The TA-XT2i Texture Analyzer 3 offers a unique means to quantify tack by force-time plots, which was not available with the Polyken Probe tack 13

10 tester. Advantages include the ability to select probe geometry and material of construction, control the probe speed prior to bonding, adhesive surface contact force sensitivity, selection of applied force and duration, and probe speed during debonding. This made the TA-XTi2 ideally suited for understanding the effect of changing MQ resin viscosity in an adhesive formulation. To define test conditions a half-factorial DoE to define probe test speeds, force applied and duration using a 7 mm 3 radius hemispherical probe was conducted. The selected settings and a GR&R are summarized in Table 3. The sigma-gage was significantly less than for the Polyken probe tack tester for the high tack adhesive used indicating its value not only in development work but also in manufacturing to characterize PSAs. Table 3. Texture Analyzer Test Conditions Pre-Test Speed 0.50 mm/s Test Speed 0.04 mm/s Post Test Speed 0.10 mm/s Applied Force g Contact Time 1.0 sec. GR&R (3'gage = 26 The TA-XT2i Analyzer response to changes in the PSA MQ resin viscosity were determined by testing several PSAs prepared with all other variables (gum viscosity, resin/gum ratio, processing) held constant. Figure 10 illustrates the TA-XT2i profile obtained for one of the adhesives used in this study. In Figure 11 the post-peak area from the Texture Analyzer is plotted against resin viscosity. Though not shown, a plot of tack against resin viscosity follows a similar decrease with increased resin viscosity. When the post-peak is plotted versus the 100g/cm 2 Polyken Probe Tack (similar to "finger" tack) in Figure 12 a reasonable correlation existed. The increased post-peak area and tack is attributed to increased energy dissipation during the debonding process and formation of fibrils as reported by Zosel. 1 i Pr~'P~'ak II 'Post-Peak For ce I[.'~) 1 2 ~ O'O O--il ~ ~ ~ ~ ~ ~ ~ ~::il " 12 ~ ~ ~ ~ ~ 8 s i:: I _, -'- " ~ ' S ' "~:~ - I I.-- ~4~It~l i i i I I I I -"..~.~I III II HI li 5'.o I ilillli ll li~: l l l l l l l l l l l l l ~ i,,,,,s,,,,,,iii m l.. l l,,. l I l Time (sec.) o. 0 4 ~,,, ~ ~ ~il I&~,~ -2 ' i ~" """" i I ~'-~ o] ~~ -3OO O -J " Figure 10. Texture Analyzer TA-XT2i Profile 14

11 350.0 Resin Viscosity vs. Post Peak Area y = e ".~rnx R 2 = "~ 250.0, ~" < ~ o ~u I I I Resin Viscosity, Solids 30.0 Figure 11. Texture Analyzer Post-Peak Area versus Resin Viscosity Post-Peak Area Vs. Tack, 100 g/cm2 y = x R 2 = r~ ~ IN.<.~ ~ o ' I I I I Tack, 100 g/era2 Figure 12. Texture Analyzer Post-Peak Area versus Polyken Probe Tack, 100g/cm 2 15

12 Summary Six-Sigma methodology offers a clear means to identify critical factors and to use tools such as DoE to arrive at new products with well-defined performance and manufacturing robustness. The use of statistical DoE software permits the product developer to define and functionally extrapolate design spaces meeting new product introduction goals. In this paper we gave an overview of this process, defining the CTQs with corresponding metrics and use of DoEs. The role that the MQ resin viscosity plays in the adhesive properties of tack, peel adhesion and lap shear were shown. Dynamic mechanical analysis and the Texture Analyzer were further used to understand the adhesive properties. New adhesives from GE Silicones, Silgrip PSA910 and Silgrip PSA950 were introduced having high tack and high peel adhesion along with high temperature lap shear properties. They are improvements over traditional adhesives having either a high tack and low peel, or high peel and low tack. References 1. Zosel, A. (1998), "The effect of fibrillation on the tack of pressure sensitive adhesives", International Journal of Adhesion & Adhesives, 18, pp Rheometric Scientific, Piscataway, N.J. 3. Stable Micro Systems, Godalming, Surrey, England Acknowledgements The authors wish to acknowledge the contribution of Edwin Cua, Rheologist, GE Silicones, for useful discussions. 16