0: DISCUSSION OF FINDINGS - The Development of a model program for the effective management of

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Figure 4.11: Scattered plot of fracture energy against thermodynamic work of adhesion for cow skin pressure sensitive adhesive

4.5.2 Analysis of the Relationship between Dispersive and Polar Component

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(1) Microsoft Excel

Figure: 4.12Scatter Plot for Cow Skin Pressure Sensitive Adhesive to Derive Surface Free Energy for the Adhesive

Figure 4.12 shows a scatter plot graph. This scatter plot graph was obtained by plotting the polar and dispersive component of the 3 test liquids using the Fowkes equation. The graphical analysis resulted in a regression equation 𝑌 = −0.3599𝑥 + 7.3533. the sum of the squares of the intercept and slope was used to determine the surface free energy of Cow skin pressure sensitive adhesive. The coefficient of determination R²= 0.5206 or 52.1%_. This regression analysis shows that the dependent variable Y representing the surface free energy of cow skin adhesive can be predicted by the result of the values of the polar and dispersive component (independent variables). It shows a strong

y = -0.359x + 7.353 R² = 0.520 6.3

6.4 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2 7.3

0 1 2 3

Dispersive Compoent of the t

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅

Polar

Component of Cow Skin PSA

Dispersive Component for Cow Skin PSA

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relationship between the values of the surface free energy for the cow skin pressure sensitive adhesive and its component (adhesive and polar) surface free energy good regression.

Figure: 4.13Scattered Plot for Natural Rubber Pressure Sensitive Adhesive Figure 4.13 is the Fowkes Equation Scattered Plot for Natural Rubber Pressure Sensitive Adhesive. It has a linear regression equation 𝑦 = 3.8976𝑥 − 0.7286 and coefficient of determination of 0.8173 or 81.73%. The surface free energy was derived from the sum of the squares of the intercept and slope. The coefficient of determination suggests a strong relationship between the value of the surface free energy for the Natural rubber pressure sensitive adhesive and its surface free energy components. (Dispersive and polar). Good regression.

y = 3.897x - 0.728 R² = 0.817

0 2 4 6 8 10 12

0 1 2 3

Polar

Component

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅 Polar

Component of Natural Rubber PSA

Dispersive Component of Natural Rubber PSA

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Figure: 4.14 Scattered Plot for Acrylic Pressure Sensitive Adhesive

Figure 4.14 is a scattered plot representing the relationship between polar and dispersive components of acrylic pressure sensitive adhesive. The regression equation is as follows: 𝑦 = −5.012𝑥 + 11.757. The sum of the squares of both the intercept and slope give the surface free energy of acrylic adhesive. The regression value is 1. This indicates perfect harmony in the role of dispersive and polar components respectively in determining the value of the surface free energy for the adhesive. However the value of the regression must have been affected by the use of only two test liquids (ethylene glycol and glycerol) for the analysis. The third test liquid ethanol exhibited spontaneous wetting when dropped on the film of the acrylic. This stopped the image of the contact angle

y = -5.015x + 11.75 R² = 1

0 1 2 3 4 5 6 7

0 0.5 1 1.5 2

Polar

Component of the test liquid

Dispersive Component of the test liquid

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅

Polar

Component for Acrylic PSA

Dispersive Component for Acrylic PSA

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from being derived at snapshot and subsequent calculations were limited. Good regression.

Figure: 4.15Scattered Plot for Silicon Pressure Sensitive Adhesive

Figure 4.15 is a scattered plot representing the relationship between polar and dispersive components of silicone pressure sensitive adhesive, with a regression equation of𝑦 = 2.1735𝑥 + 1.6607. The sum of the squares of both the slope and intercept gives the surface energy of the silicone adhesive. The coefficient determination is 0.7475 or 74.75%. It indicates a strong relationship between the values of the dependent variable the surface free energy and the independent variables the polar and dispersive component. This is a good regression.

y = 2.173x + 1.660 R² = 0.747

0 1 2 3 4 5 6 7 8 9

0 1 2 3

Dispersive Component of the test liquid Polar

Component of the test liquid

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅

Polar

Component of Silicon PSA

Dispersive Component for Silicon PSA

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Figure: 4.16 Scattered Plot for Epoxy Pressure Sensitive Adhesive

Figure 4.16 is a scatteredplot representing the relationship between the polar and dispersive component of epoxy pressure sensitive adhesive. The regression equation is as follows: 𝑦 = 3.1133𝑥 + 0.6593. The sum of the squares of the slope and intercept give the surface free energy of the epoxy. The value of the coefficient of determination is 0.8968 or 89.68%. The regression shows a strong relationship between the dependent variable (surface free energy) and independent variable (polar and dispersive component). Thus the independent variable can predict the dependent variable by 89.682, good regression.

y = 3.113x + 0.659 R² = 0.896

0 1 2 3 4 5 6 7 8 9 10

0 0.5 1 1.5 2 2.5 3

Polar

Component of the test liquid

Dispersive Component of the test liquid

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅

Polar

Component of Epoxy PSA

Dispersive Component for Epoxy PSA

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Figure 4.17: Scattered Plot for Rubber Tile Substrate

Figure 4.17 is a scattered plot representing the relationship between polar and dispersive component of rubber tile substrate. The regression equation is𝑦 = −10.48𝑥 + 19.01 and with a regression of 1. The sum of the squares of the slope of the equation and its intercept gives the surface free energy of the rubber tile substrate. The coefficient determination is 1. The regression value at 1 indicates perfect harmony in the prediction of the outcome of the dependent variable (surface free energy) and independent variable (polar and dispersive components). However this regression must have been affected by the use of only 2 test liquid for deriving the contact angle and subsequent calculations.

y = -10.14x + 19.03 R² = 1

0 1 2 3 4 5 6 7 8 9

0 0.5 1 1.5 2

Polar

Component of the test liquid

Dispersive Component of the test liquid

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅

Polar

Component of Rubber Tile Substrate

Dispersive Component for Rubber Tile Substrate

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Ethanol and Glycerol were used while the third test liquid Ethylene glycol caused spontaneous wetting of the rubber tile. Good regression.

Figure 4.18: Scattered Plot for AluminuimSubstrate

Figure 4.18 is a scattered plot representing the relationship between polar and dispersive component of aluminum substrate. The regression equation is 𝑦 = −3.7858𝑥 + 14.313, with a regression of 0.7024. The sum of the squares of both the slope and intercept gives the value of the surface free energy for the aluminum. The coefficient of determination is 0.7024 or 70.24%_. This is a strong relationship as this regression of 0.7024 shows that the dependent variable surface free energy dependents on the independent variable components (dispersive and polar) by a factor of 70%.

y = -3.785x + 14.31 R² = 0.702

0 2 4 6 8 10 12

0 0.5 1 1.5 2 2.5 3

Dispersive Component of the test liquid Polar

Component of the test liquid

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅

Polar

Component of Aluminium Substrate

Dispersive Component of Aluminium Substrate

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Figure 4.19: Scattered Plot for High Density Fibre WoodSubstrate

Figure 4.19 is a scattered plot representing the relationship between polar and dispersive component of high density fibre wood. The regression equation is as follows: 𝑦 = −1.2449𝑥 + 10.609. The sum of the squares of both the slope and intercept of the regression equation gives the surface free energy of the high density fibre wood. The coefficient of determination is 0.6236 or 62.36%. It is a strong relationship between the values of the dependent variable the surface free energy and the independent variable polar and dispersive component. This is a good regression.

y = -1.244x + 10.60 R² = 0.623

0 2 4 6 8 10 12

0 0.5 1 1.5 2 2.5 3

Dispersive Component of the test liquid Polar

Component of the test liquid

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅

Polar

Component of High Density Fibre Wood

Dispersive Component of High Density Fibre Wood

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Figure 4.20: Scattered Plot for Mild SteelSubstrate

Figure 4.20 is a scattered plot representing the relationship between the polar and dispersive component of Mild steel. The linear regression is as follows:

𝑦 = −1.3385𝑥 + 5.2076 and R²=0.4945 (coefficient of determination). The sum of the square of both slope and intercept of the regression gives the surface free energy of the Mild steel. The value of the coefficient of determination of 0.4945 is a result of 49.45%_.The result gives a moderate regression. Thus the value of the polar and dispersive component predicts the value of the surface free energy by 49.45%_.

y = -1.338x + 5.207 R² = 0.494

0 1 2 3 4 5 6

0 0.5 1 1.5 2 2.5 3

Dispersive Component of the test liquid Polar

Component of the test liquid

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅

Polar

Component of Mild Steel

Dispersive Component of Mild Steel

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Figure4.21: Scattered Plot for Ceramic Wall TileSubstrate

Figure 4.21 is a scattered plot representing the relationship between the polar and dispersive component of Ceramic wall tile. The linear regression is 𝑦 = −1.2939𝑥 + 7.6726 and regression of 0.9746. The coefficient of determination is 0.9746 or 97.46%. The sum of the polar and dispersive component (each square) gives the surface free energy of the Ceramic wall tile.

This is a good regression. The surface free energy of Ceramic wall tile value is 97.46% determined by the polar and dispersive component. The Statistical Package for Social Science (SPSS) was used to analyze the relationship between

y = 1.293x + 7.672 R² = 0.974

0 2 4 6 8 10 12

0 0.5 1 1.5 2 2.5 3

Dispersive Component of the test liquid Polar

Component of the test liquid

𝜸_𝒍^𝒑

𝜸_𝒍^𝒅

Polar

Component of Ceramic Wall Tile

Dispersive Component of Ceramic Wall Tile

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polar and dispersive components, of both the substrates and the pressure sensitive adhesive. The outcome is expressed in Table 4.9.

Table 4.9: Result of SPSS Correlation and Regression Analysis between Dispersive components and Polar components of Surface Free Energy for both

In document The Development of a model program for the effective management of biomedical waste in the Caribbean. A Review of barriers within public hospitals in the Caribbean. (Page 60-65)