A study on flexible bio-based conductive foam for pressure sensing and electromagnetic interference applications

Day by day gadgets are taking an irreplaceable role in our life. Humans are now depending on electronic gadgets. This increased usage and utility of these electronic gadgets increased the radiation; hence, it is important to research materials for better absorption of these radiations. In this research work, we are developing an electromagnetic interference shielding material. We used polyurethane foam (PU foam), an insulating material that has been filled with nanofillers Polyaniline (PANi), Zinc Oxide (ZnO), and MWCNT. The PU foam used was castor oil-based instead of going with petroleum-based. This work aims to achieve a potential material for electromagnetic interference shielding and sensing material that should be bio-degradable at a low price. The samples were fabricated using the taguchi method in the design of the experiment. This helps to reduce time consumption and provides more accurate results. Once the samples were fabricated, it was subjected to morphological study SEM and EDAX. EMI and conductivity were also carried out. The EMI experiment was done using setup model N5230A PNA-L. The conductivity test is done. GRA relational statistics was utilized to find the interrelation between the two output responses in the taguchi. The experiment concludes that the samples synthesized with 2 wt% of PANi, 300 rpm mixing rotation, and 10 min. Sonication time, provide the best conductivity of 900 S m−1 and EMI SE of 34.38 dB. The best result for conductivity is for sample 8. The maximum conductivity value is 900 S m−1. The lightweight flexible conductive foams can be used in the application of biosensors.

attraction.The most widely used cross-linking agents in the manufacture of polyurethanes are polyol, aromatic polyester polyol, polycarbonate polyol, acrylic polyol, and polybutadiene polyol.Two aromatic isocyanates that are frequently utilized in polyurethanes are toluene diisocyanate (TDI) and methylene diphenyl diisocyanate (MDI) [1][2][3][4][5][6][7][8][9][10].There are a few applications for aliphatic isocyanates, including isophorone diisocyanate (IPDI) and hexamethylene diisocyanate (HDI).Aromatic varieties are employed in a variety of applications, such as flexible and rigid foams, and are more reactive [11][12][13].Polyurethane foams are flexible, non-conductive, cheap, and lightweight, with lower water absorption, low density, and relatively good mechanical strength.As they are nonconductive, it is being used in cooling and freezing systems and automotive interiors, The drawback of normal polyurethane foam is that they are being synthesized from petrochemicals and are non-biodegradable and affect living beings adversely.These have to be hence replaced with naturally synthesized PU foam, which is affordable, eco-friendly, biodegradable, and easily available.Researchers developed a bio-based organic foam from natural vegetable oils like castor oil, soya bean oils, sunflower oils, etc to replace the petrochemical-based Polyol [14][15][16][17][18].As these foams are non-conductive, through many processes researchers approached to make these foams conductive.Experiments like Mixing and foaming, hydrothermal process, freeze-drying, In situ polymerization, dip coating, Spray pyrolysis, spin coating, sputtering, etc Were done to make the foam conductive using fillers.Here, intrinsically conducting polymers (ICPs) took great attention while selecting the fillers.Polyaniline (PANI) Polypyrrole (Ppy), Polythiophene.Intrinsically conducting polymers are also known as organic semiconductors, having delocalized pie electron systems with a wide band gap [19].Polyaniline is selected here as an intrinsically conducting polymer.The interactions of the PANI can be noted based on the change in conductivity.Introducing fillers to these foams while synthesizing them changes their whole property from the parent material.The electrical, mechanical, and structural properties of the material are altered drastically upon the addition of fillers.PANI, as a conductive polymer, demonstrates a propensity for modulating its electrical conductivity by external stimuli, while flexible polyurethane foam exhibits desirable mechanical properties and inherent flexibility.
To make the nonconductive foam, a conductive one; fillers were added.PANI, ZnO, and MWCNT are the combination of fillers we chose to make composite PU foam.Most elastomers, including rubbers, Polyurethanes, and silicon are natural insulators due to their chemical compositions.Electrical conductivity in these materials can be increased by introducing conductive additives (fillers) in the production process.Xiaozhou Lü et al fabricated a piezoresistive pressure sensor using porous foam filled with rGO, with ultra-wide pressure detection range and stability.followed by freeze-drying and dip coating.This is then used as a motion detector [20,21].Naimat Ullah et al Made a simple sensor for the application of sensing NH 3 at ambient temperature.The resistance of the material is reduced with an increase in the volume of NH 3 gas observed.Optimum sensing response was achieved with PANI in the presence of 50 wt% ZnO NP [22].Teklebrahan Gebrekrstos Weldemhret et al synthesized a sensor for Flame retardancy and oil leakage detection as a triboelectric nano-generator.The effect of IL was thoroughly investigated.In this the FR-TENG has been attached to the insole of a shoe, generating an output of 36 and 100 V upon walking and running, respectively.This output was sufficient to concomitantly glow 38 LEDs [23].Rigid PU foam with IL/MWCNT [24].The PANI/CNT composite was used as an active material because of its superior flexibility, high electrical conductivity, and thermoelectricity.The elastic PDMS porous structure was initially built as a 3D structure skeleton for pressure sensing.Contrary to SWCNTs, MWCNTs are more readily available and have relatively cheap production costs, making them more appealing [25].Gang Ge et al demonstrated a sensor for human motion detection by wrapping rGO/PANi into a flexible sponge.The synthesized flexible pressure sensor on the rGO flexible sensor and achieved a tunable sensitivity (0.042 to 0.152 kPa −1 ), wide working range (0-27 kPa), fast response (∼96 ms), high current output.The advantage of PANi is the larger surface-to-volume ratio [26], PANI/Mn3O4 [27], PANI/V2O5 for ammonia sensing, it can detect gaseous ammonia in the 0-54 ppm range of concentrations [28], PANi with natural rubber for chemical sensing [29], PANI the conductive polymer has been used in making chemiresistive sensing, layer by layer thin films with natural rubber for multiple applications [30][31][32].Muthukumar et al used PANI-coated PU foam for sensing applications [33].Some conductive polymeric materials exhibit the piezo resistivity effect; however, the effect of piezo resistivity for insulating polymeric materials is challenging to produce.Yanli Kang et al in their review paper mentioned ZnO in the sensing application and is an optically active semiconductor with a piezoelectric nature [34].Lee et al [35] did a wide-range pressure sensor using a CNT/TPU composite.The most conductive filler to incorporate into a porous structure like PU foam is MWCNT.When the conductive material is disseminated with the appropriate non-uniformity, the MWCNT impregnation inside the sensor has the benefit of increasing the sensor's sensitivity.The design of the porous structure affects the sensitivity of a sensor impregnated with MWCNT in PU foam.Vinoth et al used castor oil-based PU foam with nanofillers embedded in the EMI application [18].ZnO has a noncentrosymmetric structure, the wurtzite ZnO material displays excellent piezoelectric properties in the [0001]-direction [36].
Resistive, capacitive, triboelectric, and piezoelectric forms of flexible and stretchable tactile sensors with various pressure-detecting techniques have all been researched [37][38][39].the synthesis in a traditional way can take more time, waste precursors, etc Hence a statistical method can be selected to experiment more qualitatively in less time reducing the trials.The Taguchi approach, which is based on an orthogonal array (OA) for the Design of Experiment (DOE), involves examining a given system through several independent elements on a certain Level [40][41][42].The Taguchi method makes it possible to accomplish this goal by minimizing the number of tests.Effects of individual components organizing the relationship between variables and operating circumstances, eventually establish performance at the optimum levels, acquired by a few experimental sets [43].the multiple output responses in taguchi cannot be assessed normally.The Deng-proposed grey system theory includes grey relational analysis (GRA), which is appropriate for resolving issues involving intricate interactions between several components and variables [44].only one performance characteristic can be optimized using the Taguchi approach.The GRA technique combines all the performance characteristics that are considered into a single value that can subsequently be employed as the sole characteristic in optimization issues [45].GRA analysis provides the interrelation between 2 or more multi-responses [46].The novelty of the work is that the synthesized PU foam was castor oil-based.The advantage of castor oil-based PU foam is that it is bio-based and the normal one is petroleum-based.In the literature survey, it's being analyzed that bio-based PU foam has less thermal conductivity than petroleum-based PU foam implying that it is more heat resistant.
This research project presents a novel and innovative approach to addressing the increasing dependence on electronic gadgets and the resulting surge in electromagnetic radiation.By developing an electromagnetic interference (EMI) shielding material using polyurethane foam (PU foam) enriched with nanofillers such as Polyaniline (PANi), Zinc Oxide (ZnO), and Multi-Walled Carbon Nanotubes (MWCNT), this study pioneers an advanced solution for radiation absorption.Notably, the use of eco-friendly, castor oil-based PU foam underscores a commitment to sustainability.Employing the Taguchi method for material fabrication streamlines the experimentation process, optimizing parameters to achieve the best conductivity and EMI shielding effectiveness.The project's ingenuity extends to comprehensive characterization through SEM, EDAX, and conductivity tests, reinforcing the credibility of the results.Furthermore, the versatile material serves a dual purpose, exhibiting potential for both EMI shielding and biosensing applications due to its lightweight, flexible nature.The incorporation of Grey Relational Analysis (GRA) statistics enhances the robustness of findings.Altogether, this research introduces an original and impactful contribution to the field of electromagnetic interference shielding materials, poised to shape more efficient and sustainable electronic applications.
The Polyol was measured in a measuring jar and poured into a beaker.To this using a micro pipette 3.5 μl of surfactant is added and mixed thoroughly using a magnetic stirrer at 200 rpm.The surfactant is crucial in managing the cell size.Surfactants can reduce the surface tension of the reactants.0.10 μl of tin catalyst and 0.75 μl of Amine catalyst were added to this and uniformly mixed well without changing the rotation rate.The addition of catalytic systems in the preparation of PU foam influences the final material.The catalysts also control the reaction rate and gelling time.Gas formation during side chain reactions and foaming is also assisted by these catalysts.Upon mixing 1 g of distilled water is also added as a CO 2 blowing agent.To this 12 ml of castor oil-based isocyanate is added.Upon stirring this for 5 s the solution is poured into a mould of diameter 110 mm and allowed to free rise at room temperature.The mentioned above is for the synthesis of normal PU foam.This is an exothermic reaction.Following the same procedure before adding the iso, the fillers Polyaniline, Zinc-Oxide, and MWCNT are weighed and added according to the DOE Taguchi table.The synthesized foam is conductive and used for EMI shielding.A Schematic representation of the preparation of foam is shown below in figure 1.

Experimental setup for conductivity
The synthesized samples are subjected to conductivity testing.The conductivity of the foam was calculated by finding the resistance using RIGOL digital multi-meter model number DM3068, 3000 series (figure 2(a)).As a tiny current is introduced into the circuit, the voltage drop across those locations is then measured by multimeter to determine the resistance.The specifications of the digital multi-meter are 1000 V DC voltage, AC 750 V, DC 10 A, resistance 1000 MΩ, and Frequency 30 MHz. Figure 2(b) is a digital oscilloscope DS1054 model with which we can measure the V rms and V pp values of the samples to be tested.
The setup consists of two and four probes.The resistance is being measured, with which using the equation (1), the conductivity σ is calculated Where, R = resistance ρ = resistivity L = length A = area From this equation (2), resistivity can be found and the reciprocal of it gives the conductivity.The obtained conductivity values are in the units of S/m.
The sensing capacity of the foam when pressure is applied is measured using a digital oscilloscope.The DSO is of 4 channels with 50 MHz bandwidth.The sensitivity range is 1 mv div −1 −10 V div −1 .The frequency change and changes in V rms Values, period, Vmax, V min , and V pp were noted upon pressure exerting.DSO captures, displays, and analyzes the waveforms and bandwidth of the electronic signals.The digital Oscilloscope has 4 channels with a maximum input voltage of 400 V.The resolution rate is 8-bit with a time base of 2 ns div −1 -50 s div −1 .

Experimental setup for electromagnetic interference shielding
The EMI experiment was carried out in an open field or free space method.Here, the transmitter transmits the EM waves which are passed through the sample placed 60 cm apart from it and are detected by the receiver and processed by the network analyzer.Both the transmitter and the receiver are equidistant from the specimen.The setup includes, a network analyzer of model number N5230A PNA-L having a dynamic range of 138db with an operating range of 10 MHz to 50 GHz.Due to this high dynamic range, the accuracy in the measurement and detection of the small changes in the signal is very high.The experiment was conducted from +5 V to +15 V.The distance between the antennas is half the wavelength of the electromagnetic interference setup.The power supply is 240VAC.N5230-PNA-L The transmitter and the receiver connected with the vector analyzer provide the graph and values showing the effective EMI shielding of the sample.Figure 3(a) below shows the experimental setup of effective EMI shielding of the specimen.Figure 3(a) below shows the experimental setup of effective EMI shielding of the specimen.Figure 3(b) represents the schematic illustration of EMI testing where a power source network analyzer is connected.On one end transmitter and the other end, a receiver is placed which is equidistantly placed apart from the sample (60 cm).

Statistical design of experiment using taguchi approach
The experiment was conducted using a statistical approach called DOE (Design of Experiments) Taguchi optimization, developed by Japanese engineer Dr Genichi Taguchi, to reduce trials and improve product quality.This aids in comprehending how input values influence output responses.It is possible to study numerous factors affecting the final product.Industries employ DOE to streamline the process, yielding more accurate results in less time.The Taguchi method consists of eight steps: defining the primary function, side effects, and failure mode; determining testing conditions, noise factors, and quality parameters; identifying desired optimization outcomes; establishing levels for controlling factors; selecting an orthogonal array matrix experiment; and conducting the matrix experiment.A unique feature of the Taguchi method is the use of orthogonal arrays (OA) and signal-to-noise ratios (S/N ratio).The levels in the Taguchi method pertain to the factors considered in sample synthesis.In this study, we selected an L9 (3 × 3) orthogonal array and prepared 9 samples with varying proportions.The factors examined are the concentration of filler PANi, sonication time, and mixing rotation.Constructing an orthogonal array involves multiple factors, factor levels, and response values.Orthogonal arrays aid in demonstrating statistical independence in experimental results.The signal-tonoise ratio gauges the quality of product or process performance, indicating deviations from the target value due to uncontrollable factors.The aim of using this statistical method for sample synthesis is to achieve maximum quality during commercialization.Statistical methods offer advantages such as reduced time consumption, material usage, lower costs, and more accurate design, compared to traditional methods.Taguchi orthogonal arrays can encompass multiple factors and levels.However, the Taguchi method alone may not sufficiently assess individual results, leading to the utilization of ANOVA.When using the Taguchi approach and considering multiple output responses, Grey Relational Analysis (GRA) is employed.Scholars have used GRA to optimize control settings with multi-response by evaluating grey relational grades.Through this method, insights into the interactions between output responses, such as EMI and conductivity, were obtained.The Taguchi optimization procedure efficiently enhances product or process performance by systematically varying factors using orthogonal arrays, minimizing experiments and resource use.Its robustness against noise ensures reliable real-world outcomes, and its focus on interactions yields deeper insights.Unlike traditional methods, Taguchi's signal-to-noise ratio manages noise, making it practical.It excels in multi-objective optimization, balancing diverse goals through desirability functions.Taguchi's structured simplicity appeals to various industries, offering a potent alternative to response surface methodology and factorial experiments, delivering enhanced performance and competitiveness.

Grey relational analysis (GRA)
GRA is one of the commonly used grey system theory.GRA was done to compare the output variables (conductivity and EMI shielding interference) to explain the complex or more than 1 data.It helps in analyzing complex data and their relationships including binary data, continuous data, and categorical data.Here, we can understand how each factor is dependent on each other.The basis for the Grey relation analysis approach is the similar or disparate relationship between different process variables.Information for forecasting and making decisions is also provided by the method.The approach is also applicable to decision-making using multiple factors.It also helps in the decision-making process while evaluating alternatives.The grade obtained in the grey relational analysis is to estimate the multivariate responses.GRA grades show the quality of the synthesized samples in their order.The optimization itself suggests the best combination to get the best output.The GRA includes six steps: defining the problems and response variables, Data collection, Normalizing the data ('Larger the better', 'Smaller the better', 'Normal the best'), Grey relation coefficient for the normalized data, Finding the grade, understanding the best quality outcome and its correlation).The correlation between the outputs is referred to as the Grey correlation degree.Using the GRA, we can understand how each output is affecting the main system.In GRA we can give our preference in calculating, to get the best sample.

Scanning electron microscopy (SEM)
The morphology of the castor oil-based polyurethane form with conductive fillers is examined using scanning electron microscopy, as depicted below in figure 5. Before subjecting the samples to testing, specimens are goldplated.The thickness of the foam is about 20 mm.The pore size distribution of the foam is around 100 μm.The SEM image of the foam after coating PANi/ZnO/MWCNT is shown in figure 5.In the SEM images below, we can identify the agglomeration of fillers at a 20 μm scale in figure 5(a).In figures 5(b) and (c) with 100 μm scale, pores can be seen with fillers embedded in them.The porous structure makes the bio-based foam flexible and can be hence considered for sensing application.Upon the compression and relaxation, the particles get near and far thus affecting the distance of separation and conductivity of the foam.In figure 5, the SEM images show the nodular growth of fillers.The size of the pores decreased after coating the fillers.When applying the pressure, the pore size again gets reduced, and the clusters of the fillers get in touch with each other leading to conductivity changes.

Energy dispersive x-ray analysis for biocomposites foam
An energy-dispersive x-ray analyzer (EDX or EDA) is used to provide elemental identification and quantitative compositional information.Figure 6 shows the peaks obtained for Polyurethane foam with fillers Polyaniline (PANi), Zinc Oxide (ZnO), and Multi-Walled Carbon Nanotube (MWCNT).From the data, we can identify major peaks of Carbon (C), Oxygen (O), and a minor peak for Zinc (Zn).Table 4 below shows the weight percent of the respective elements.
The elemental composition of the polyaniline sample, including the presence and relative proportions of elements like carbon (C), Nitrogen (N), and oxygen (O) has been detected in EDAX result through which we confirmed the presence of Polyaniline.The EDAX of ZnO typically reveals the presence of two primary elements   i.e., zinc (Zn) and oxygen (O).The result we obtained shows the presence of zinc and oxygen thus we confirmed the presence of zinc oxide.In the case of MWCNTs, the peaks of carbon indicate the presence of MWCNT.MWCNTs consist primarily of carbon atoms, and the carbon peaks in the EDAX spectrum are notably prominent, highlighting their significance in the elemental composition of MWCNTs.

Experimental analysis of electrical conductivity
The insulative polyurethane foams are made conductive upon adding ICP (Intrinsically conductive polymer) PANI, Zinc Oxide (ZnO), and Multi-Walled Carbon Nano Tube (MWCNT).The electrical conductivity is measured using a multi-meter and DSO (Digital Oscilloscope).The Voltage and Current were measured and plotted using the origin.The resistance of the polyurethane foam measured was 22 MΩ.Upon addition of the fillers, the resistance was reduced to kΩ.According to the change in filler ratio, Sonication time, and mixing rotation, the value of resistance varied from 9.19 kΩ to 0.21 kΩ.The lower the resistance higher the conductance of the material.Table 5 below shows the resistance value of the samples.The above-mentioned values are obtained from a 4-probe digital multi-meter.From this conductance of the material was obtained.From the table, we concluded that the sample with 2 wt% of PANI at 300 rpm for 10 min.Sonication provides the lowest resistance, which means that it is highly conductive from the rest of the samples.The IV curve of the best sample (S8) is shown in figure 7.In the graph, the X-axis notates the Current and the Yaxis is the Voltage.The resistance value can be calculated using this data.Ohms law: V = IR, upon obtaining the R, we can find the conductivity of the foam, using the equation (3), Where, R = resistance  1/R = Conductance -unit (mho, Siemens) L = length between the probes (m) A = Area (m 2 ) Note that all the values are in SI units before calculating.The unit of conductivity is Siemens/meter i.e., Sm −1 .
From the graph, we can analyze the resistance of the sample using Ohm's law.

Statistical analysis of conductivity
The conductivity of the samples was calculated using the resistance value obtained.The higher value of conductivity gives the best sample.Taguchi's design for conductivity analysis using Minitab is given in table 6.
Taguchi analysis for conductivity versus composition of fillers, mixing rotation, and composition of fillers was analyzed.Signal-to-noise ratio and main effect plots of individual factors affecting the samples were obtained.The response table value was set as 'larger the better'.Through this, we can analyze how the conductivity is affected by the factors taken.The graphs for the S/N ratio, contour plot, and optimization plots are as below which indicate how the factors are affecting the output response conductivity.
According to the main effects plot generated by the Minitab, as shown in figure 8, the variation in the output is observed concerning the three factors :(a) Composition of PANi, (b) Mixing rotation, and (c) sonication time.In this analysis, larger values of the signal-to-noise (SN) ratio indicate the production of most conductive samples.The results indicate that the best sample is obtained when synthesizing with a composition of 2 wt% of PANi, a sonication time of 10 min, and a mixing rotation speed of 300 rotations per minute.
The contour plot assists in visually identifying the regions or combinations of the factor levels that lead to favorable or optimal response values.It provides a clear visualization of how the adjustments in the factor settings influence the response, aiding in the determination of the most effective combination of factor levels to achieve the desired outcome.Hence, we did a contour plot analysis for the biocomposite foam.
Figure 9 shows the contour plot for conductivity with (a) mixing rotation (rpm) with the composition of PANi (%), (b) sonication time(min) v/s composition of PANI (wt%), (c) sonication time (min) v/s mixing rotation (rpm).From the plot, we can say that the darker green region gives the best conductivity value.In (a) it is  From the contour plot of mixing versus sonication; the maximum conductivity can be obtained in the range where mixing rotation is below 350 rpm till 200 rpm where the composition of PANI ranges from 1.78 wt% to 2wt%.When sonication time is less than 15 min with a composition of PANI ranging from 1.86 wt% to 2 wt%, conductivity is greater than 500 S m −1 .Mixing rotation between 200 rpm to 260 rpm with a sonication time of less than 15 min also provides conductivity greater than 500 S m −1 .Table 7 presents the utilization of analysis of variance (ANOVA) to assess the reliability of the model.The F-value indicates the statistical significance of each outcome, demonstrating that the model statistically aligns with the experimental results.Based on the findings, the model can predict results with a 90% level of confidence.

Experimental Analysis of EMI
The samples were placed 30 cm apart from the transmitter and the receiver to keep the antenna in range.As shown in figure 3(b), both the transmitter and receiver were connected to the power supply and the network analyzer.To understand the experimental results of EMI SE, the below equations (4)-( 9) is used.

= ( ) | | ( )
Where S 11 = Spectrum Parameter in free air medium Where S 12 = Spectrum parameter of castor oil-based PU foam with conductive fillers Figure 10 and table 8 below show the EMI result of the 9 different castor oil-based PU foam with nanofillers and 1 castor oil-based PU foam without nanofillers by varying parameters.The highest EMI value shows that the sample is the best among the others.It was observed that the foam with filler is more advantageous for EMI shielding than the foam without fillers.The graphs below show the EMI shielding effectiveness of the castor oilbased foam with and without fillers.Figure 10(a) shows the difference between the S0, S1, S2, and S3, figure 10(b) shows S0, S4, S5, and S6, and figure 10(c) shows S0, S7, S8, and S9 shows the differences in the EMI values concerning castor oil-based PU foam with and without nanofillers.The frequency range was from 8 to 12 GHz.The sample S0 has an EMI value ranging from 3.281 dB to 7.71 dB.The samples with nanofillers S1 to S9 show an EMI value in the range of 25.10 dB to 34.47 dB.Table 9 shows the calculation of the reflection coefficient and shielding effectiveness reflection.

EMI statistical analysis
The composition of PANi, Sonication time, and mixing rotation are the factors considered in the synthesis of conductive castor oil-based PU foam.The DOE tool used is Taguchi.Using Minitab software, regression was utilized to find the effect of the factors on the conductivity and EMI shielding effect of the synthesized foam.The   Figure 11(c) shows the influence of mixing rotation and sonication time affecting the EMI.The main effect plot as shown in figure 11 shows the factors affecting the output EMI SE.From the below data, we can conclude that if the composition of PANi is 1 wt%, with mixing rotation of 300 rpm and 20 min sonication gives us the sample with higher EMI SE.The EMI value is greater when the composition of PANI is in between 1 wt% − 1.1 wt% and 200 rpm to 325 rpm upon considering mixing rotation.When sonication time and composition are the factors, then from 17 min to 30 min of sonication for 1wt% to 1.1 wt% shows a good result in terms of EMI.When sonication time and mixing rotation were taken into account as factors, 200 rpm rotation speed and 30 min of sonication provides the best outcome according to Taguchi counterplots.Table 10 presents the utilization of analysis of variance (ANOVA) to assess the reliability of the model.The F-value indicates the statistical significance of each outcome, demonstrating that the model statistically aligns with the experimental results.Based on the findings, the model can predict results with a 90% level of confidence.

GRA relational analysis of conductivity and EMI
Table 11 below shows the GRA analysis of the output responses in the experiment conducted.The highest and best values were given by the sample with the composition of filler 2 wt% at 300 rpm for 10 min.GRA relational analysis for the multi-response included the following steps.
Step 1: Calculate the SN ratio using the desired available formulas.For the values which give smaller is the best result we use this equation.
(iii) Nominal the best The medium in between the value of any experiment shows the best character in the ultimate product we must use the nominal as the best equation.
The second step in GRA relational analysis for EMI and conductivity is 'Normalization'.To get the raw data ready for analysis, the original sequence is converted to a comparable sequence using normalization of the S/N ratio.Here, again we have equation (10) to use, according to our experimental results.
The above equation (10) is used in normalizing the output response if the S/N ratio is good in larger the better constraint.
The equation ( 11) is for smaller the better being the best output response.

Zij yij Target yij
The equation ( 12) is used in the case of nominal the best produces the best data.
In this work, we focused on what larger values are the best.Step four is to generate the grade using the existing values by utilizing the equation ( 14) below.
Where Υ j is the grade of jth experiment; k = number of performance characteristics.Following this, the grade of the experiment conducted can be accessed.According to the grades, the samples can also be ranked.Conductivity and electromagnetic interference (EMI) share an interconnection rooted in material properties.Higher conductivity can enhance EMI shielding by facilitating wave absorption or reflection, influencing a material's effectiveness in managing interference.Conversely, lower conductivity may impact a material's EMI shielding efficiency, allowing greater wave penetration.This interplay is pivotal in applications like electronics and communication systems, shaping material selection for optimal EMI control.

Sensing application of the conductive bio composite polyurethane foam
The synthesized samples were subjected to sensing the pressure applied.Using a digital multimeter the rise and fall in the resistance were measured over time.Figure 13 shows the sensing of the biocomposite foam towards the applied pressure concerning time.

Conclusion
In this research, we have developed a conductive polyurethane foam using castor oil as the base material.To achieve conductivity and piezoelectric properties, we incorporated ICP PANi, ZnO nano-powder, and MWCNT into the foam.The foam's suitability for EMI shielding application was successfully demonstrated, and its conductivity was thoroughly investigated.Our study revealed that the most favorable conductivity was observed in sample 8, which utilized 2 wt% of PANI,10 min of sonication, and a mixing rotation of 300 rpm.Additionally, for effective EMI shielding, the sample containing 1 wt% PANi, 20 min' sonication, and 300 rpm mixing rotation showed promising results experimentally as well as statistically.After applying Taguchi analysis with DOE, we obtained multiple results and observed variations in the performance based on factor concentration.To assess the combined output performance, we employed Gray relational analysis, which involved normalizing the data and determining the ranking of the samples.According to this analysis, sample 8  demonstrated the highest combined performance in both EMI (Electro Magnetic Interference) and conductivity, receiving a rank of 1. Specifically, sample 8 exhibited a conductivity value of 900 S m −1 and an EMI value of 34.26 dB.However, when considering each output factor independently, sample 8 had the highest conductivity value of 900 S m −1 , while sample 2 achieved the best performance in terms of EMI with a value of 34.40 dB.These findings suggest that both sample 8 and sample 2 are optimal for their respective output factors when analyzed separately.Given their favorable characteristics, the synthesized samples can be effectively utilized in sensing applications.

Figure 1 .
Figure 1.Schematic illustration of the synthesis of conductive polyurethane foam.

Figure 3 .
Figure 3. (a) Experimental setup of effective EMI shielding.(b) Schematic illustration of electromagnetic interference shielding.

Figure 5 .
Figure 5. SEM images of castor oil-based polyurethane form with conductive fillers.

Figure 7 .
Figure 7. Graph showing the IV graph of the best sample (S8) which shows the highest conductivity.

Figure 8 .
Figure 8. Graph showing the influence of factors on signal-to-noise ratio in conductivity of the PU foam.

Figure 9 .
Figure 9. Counter plots showing the effect of nano-fillers on the conductivity of the PU foam.

Figure 11 .
Figure 11.Counter plots showing the effect of nano-fillers on the EMI of the PU foam.

=
Where, n = number of replications, Y ij is the observed response, where I = 1, 2K. n; j = 1, 2KK.k.This can be applied to the values where higher is better.(ii)Smaller the best y

Figure 12 .
Figure 12.The signal-to-noise ratio of factors affecting EMI SE.

Figure 13 .
Figure 13.(a) Deformation of conductive foam, (b) Elongation of conductive foam, and (c) Graph showing the change in resistance with applied pressure.

Table 1 .
Details of Bio-based polyurethane foam precursors.
was carried out first and with 1:2 ratios of Polyol and castor oil-based isocyanate flexible desired foam was obtained by adding enough surfactant, tin catalyst, and amine catalyst as shown in table

Table 2 .
Details of Nanofillers used in the synthesis of conductive polyurethane foam.

Table 3 .
Chemical composition of castor oil-based polyurethane foam.

Table 4 .
Elements and their weight percent of EDAX analysis.

Table 5 .
Resistance measurement values of the synthesized PU foams.

Table 6 .
Statistical values for conductivity using taguchi analysis.

Table 9 .
Calculation of reflection coefficient and shielding effectiveness reflection.

Table 10 .
Analysis of variance for EMI (dB).

Table 11 .
Conductivity and EMI values using GRA.

Table 12 .
Normalized values of PU foams with 3 factors.

Table 13 .
Shows the GRA relational coefficient values.