## 4 Discussion
### LSTM
#### 4.3.2 Sensitivity  
RSM  
Table 12 presents the ANOVA results regarding the sensitivity of the flexible sensing materials. The regression model's total F-value is 55.80, with the affecting components ranked by importance as CNT \(>\) Fe\({}_{3}\)O\({}_{4}\)\(>\) MLG \(>\) MT. For quadratic terms, the ordering is \(x_{2}^{2}>x_{1}x_{3}>x_{4}^{2}>x_{1}x_{4}\). The \(p\)-value for the model term is below 0.0001, signifying its significance; conversely, the \(p\)-value for the lack of fit term is 0.6262, over 0.05, indicating its insignificance. The R\({}^{2}\) value of the RSM model is 0.8955, signifying a strong fit of the model. The regression model's coefficient of variation (C.V.%) is 1.28%, below 5%, meaning that the model demonstrates strong reliability and accuracy. Figure 15a,b illustrate the influence of Fe\({}_{3}\)O\({}_{4}\) particles and carbon conductive filler on sensor sensitivity. The sensitivity response exhibits considerable fluctuations due to the interaction between the three types of nanoparticles, underscoring the significant role of carbon particles in enhancing sensitivity. The carbon material possesses excellent electrical conductivity and flexibility, and their combination facilitates the formation of a carbon conductive network, which further improves dispersion, increases the contact area between the filler and substrate, enhances the conductivity of the flexible sensing material, and aligns with the conclusion of Equation (16). Data analysis reveals that as the concentrations of Fe\({}_{3}\)O\({}_{4}\), CNT, and MLG increase, the sensitivity rises before declining. This phenomenon occurs due to the densification of the conductive network with the addition of conductive fillers, resulting in a reduction in the equivalent micro-resistance within the flexible sensing material. Consequently, the sensitivity diminishes, highlighting the significant influence of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & **\(p\)-Value** & **Status** \\ \hline Model & 0.5096 & 14 & 0.0364 & 55.80 & \(<\)0.0001 & Significant \\ \(x_{1}\)-Fe\({}_{3}\)O\({}_{4}\) & 0.0224 & 1 & 0.0224 & 15.85 & 0.0014 & \\ \(  
optimal filler content proportions on sensitivity performance. Figure 15c,e,f illustrate the interaction between the filler and the MT, demonstrating an initial increase in sensitivity with rising temperature, followed by a slow decline. Increased temperature results in the softening of the matrix, thereby reducing the average spacing between the conductive fillers during material formation, which amplifies the synergistic effect among the fillers. As the temperature rises, the matrix becomes increasingly softened, leading to a continual decrease in the spacing between the conductive fillers. This ultimately causes the agglomeration of conductive particles, disrupting the internal conductive network (a three-dimensional system of conductive pathways formed by dispersed conductive particles through physical contact or tunnelling effect, which allows current to flow throughout the material and endows the composite with its electrical conductivity) of the composite material and consequently diminishing the sensitivity of the sensing material. The filler concentration, curing, and MT are crucial for improving the sensor sensitivity performance. According to the ANOVA regression coefficient of BBD, the response relationship between the sensitivity, filler content, and moulding temperature can be expressed as follows:  
\[ \begin{split} y_{2}=3.05940-0.735600x_{1}-2.94273x_{2}-3.33888x_{3 }-0.004671x_{4}\ldots\\ +0.127500x_{1}x_{2}+0.820000x_{1}x_{3}+0.000713x_{1}x_{4}\ldots\\ +1.69750x_{2}x_{3}+0.003813x_{2}x_{4}-0.006500x_{3}x_{4}\ldots\\ -0.148167x_{1}^{2}+0.753083x_{2}^{2}+1.05058x_{3}^{2}+0.000071x_{4 }^{2}\end{split}  \tag{44}\]  
Figure 15: Three-dimensional representations of sensitivity response surfaces.