## 3 Results
### Shore-D Hardness  
As illustrated in Figure 4 and supported by the regression model, SSP exhibits the most significant influence on Shore D hardness, showing a strong increasing trend due to its high density and stiffening effects. EPB also contributes positively, with a greater effect than EPA, while EPA produces a modest increase that plateaus at higher concentrations. The interaction between SSP and EPB suggests additive benefits, though SSP remains the dominant factor. Optimal hardness occurs at balanced EPA and EPB levels, while excessive amounts reduce effectiveness. These results emphasize that a well-balanced formulation of SSP, EPA, and EPB is essential for maximizing Shore D hardness in the bio-composite system. The quadratic model developed using the RSM design for Shore D hardness prediction is presented in Equation (6). The increase in hardness is linked to rigid particulate reinforcement that limits polymer chain mobility, while balanced resin ratios help to disperse filler evenly and reduce defects.  
\[\begin{array}{c}\text{Shore D hardness}=56.59648+2.38443\times\text{ SSP}+0.204284\times\text{ EPA}+2.18738\\ \times\text{EPB}-0.040401\times\text{SSP}\times\text{EPA}-0.081486\times\text{ SSP}\times\text{EPB}-0.071215\\ \times\text{EPA}\times\text{EPB}+0.181823\times(\text{SSP})^{2}+0.014239\times( \text{EPA})^{2}+0.006093\times(\text{EPB})^{2}\end{array} \tag{6}\]  
Table 5 demonstrates that SSP, EPA, and EPB contents have statistically significant effects on Shore D hardness. The overall model significance is very high (\(p<0.0001\)), confirming that all three variables exert a strong influence on hardness. Among them,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Source** & **Sum of Squares** & **Mean Square** & **F-Value** & _p-Value_ \\ \hline Model & 458.82 & 50.98 & 139.52 & \textless{}0.0001 \\ A-SSP (g) & 422.43 & 422.43 & 1156.07 & \textless{}0.0001 \\ B-EPA (g) & 9.46 & 9.46 & 25.88 & 0.0014 \\ C-EPB (g) & 3.97 & 3.97 & 10.87 & 0.0132 \\ AB & 5.684 \(\times\)\(10^{-14}\) & 5.684 \(\times\)\(10^{-14}\) & 1.556 \(\times\)\(10^{-13}\) & 1.0000 \\ AC & 5.684 \(\times\)\(10^{-14}\) & 5.684 \(\times\)\(10^{-14}\) & 1.556 \(\times\)\(10^{-13}\) & 1.0000 \\ BC & 5.684 \(\times\)\(10^{-14}\) & 5.684 \(\times\)\(10^{-14}\) & 1.556 \(\times\)\(10^{-13}\) & 1.0000 \\ A\({}^{2}\) & 21.53 & 21.53 & 58.93 & 0.0001 \\ B\({}^{2}\) & 0.2420 & 0.2420 & 0.6622 & 0.4426 \\ C\({}^{2}\) & 0.2323 & 0.2323 & 0.6358 & 0.4514 \\ Residual & 2.56 & 0.3654 & & \\ Lack of Fit & 2.53 & 0.5062 & 37.97 & 0.0259 \\ Pure Error & 0.0267 & 0.0133 & & \\ Cor Total & 461.38 & & & \\ \hline \end{tabular}
\end{table}
Table 4: ANOVA analysis for bulk density of the bio-composites.  
Figure 4: (**a**) Effects of EPA and SSP contents, (**b**) effects of EPB and SSP contents, and (**c**) effects of EPA and EPB contents on Shore D hardness.  
SSP has the most pronounced effect (F = 1045.62, \(p\) < 0.0001) and plays a critical role in increasing Shore D hardness. EPA also has a statistically significant but comparatively lower effect (F = 25.63, \(p\) = 0.0015), while EPB shows a weaker but still significant contribution (F = 8.17, \(p\) = 0.0244). The two-way interaction terms (AB, AC, and BC) were not significant (\(p\) > 0.05), indicating that the variables do not exhibit synergistic interactions and instead act independently. The quadratic effect of SSP was significant (\(p\) < 0.0001), suggesting a non-linear relationship between SSP content and Shore D hardness, but its effect may change beyond a certain concentration. By contrast, the quadratic effects of EPA and EPB were not significant (\(p\) > 0.05). The model exhibits good agreement with the data, as indicated by the non-significant lack of fit (\(p\) = 0.3279), confirming the reliability of the predictions. In conclusion, SSP is the dominant factor in optimizing Shore D hardness, while EPA and EPB have more limited but statistically meaningful effects.