Mechanical, Physical and Thermal Properties of Waste Polypropylene/Polystyrene/Natural Rubber (wPP/PS/NR) Blends

doi:10.21203/rs.3.rs-2966999/v1

Download PDF

Research Article

Mechanical, Physical and Thermal Properties of Waste Polypropylene/Polystyrene/Natural Rubber (wPP/PS/NR) Blends

https://doi.org/10.21203/rs.3.rs-2966999/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 23 Feb, 2024

Read the published version in Journal of Polymer Research →

You are reading this latest preprint version

Combining two or more polymers as a blend is necessary rather than synthesizing new ones for the balance of polymer properties for end-use applications. This work introduces a sustainable polymer blend (matrix) for high-impact applications, as the accessibility and cost of production of these high-impact polymers keep souring. This was achieved by designing an experiment using design expert v10.0 to account for the number of experimental runs and optimise the polymer blend. Mechanical, dynamic mechanical, and temperature effects were considered on the blended polymer. Sixteen (16) experimental runs were generated with blend ratio as the independent variable and Impact Strength (IS) and Tensile Strength (TS) as the responses. The optimal blend ratio was obtained for wPP, PP and NR as 23, 30 and 47 % respectively. While, the optimum conditions for maximum impact and tensile strengths from the D.E was 241.62 J/m and 118.04 MPa respectively, with a desirability of 0.973. While the validated values obtained were 248.9 J/m; with errors of 2.93 % and 120.14 MPa; with an error of 1.75 % respectively. Thermogravimetry analysis revealed a single peak degradation for sp17, sp18, and sp19 (control samples) while multiple degradation peaks were observed for the blends. It was believed to be a result of different times for degradation of the individual components forming the polymer blend. Also, the T_d onset started around 300 ^oC and the T_d offset was around 500 ^oC for all tested samples. While, the Dynamic Mechanical Analysis (DMA) showed that there was an improvement in the T_g and damping factor for sp5 (109 ^oC, 0.171) as compared to sp17 and sp18 (81.1 ^oC, 0.089 and 104.3 ^oC, 0.061), but no improvement was recorded for storage and loss moduli. SEM micrographs showed a good surface morphology for the blend with optimal mechanical and thermal properties. The blend can be utilized for engineering applications, including matrices for body armour, helmets, car bumper, etc.

Polypropylene

Polystyrene

Natural Rubber

Polymer Blend

Design of Experiment

Matrix, which is also known as a base material in composite material is crucial to its development because it conducts loads within the structure [1–2]. In composite materials, matrices hold all reinforcing fibres and act as an agent in transferring/sharing any external stress within the constituents by protecting them against any degradative processes [3]. These processes, can either be mechanical (damage from impacts, delamination, extreme heat, creep, and water absorption) or chemical degradation. Metals, ceramics and polymers are used as matrices in composite materials development; however, polymers matrices have a wide range of applications due to their lightweight and easy processability compared to metals and ceramics [3–9].

Polymers are classified as thermoplastics, which include, acrylonitrile butadiene styrene (ABS), polyethylene (PP), polystyrene (PS), polyethylene terephthalate (PET), etc. or thermosets polyesters, polyurethanes, epoxies, phenolics, acrylics, etc. The processing methods for thermoplastic polymer items require that the products are formed in a single process (heating, shaping and cooling) or repeated cycle; i.e., in different shapes and sizes. The major processing methods are; extrusion, injection moulding, blow moulding, rotational moulding etc. [7]. However, ABS is expensive (engineering polymers) and have complex processing methods than utility polymers (PP, PS etc.) [8–10].

Polymers whose tensile strength is above 40 MPa and are processable at a temperature above 100 ^oC are termed engineering polymers [11]. Among the widely used engineering polymer matrices are the thermoplastics. These polymers have stable dimension control, as well as high performance at service temperature above 100 ^oC. Some of these polymers e.g., natural rubber has high impact properties, PS is dimensionally stable, while PP is tough and has good heart resistance. For the balance of properties, there is a need to combine two or more of these polymers termed blend-in matrices production [12–14].

There is the need to combine two or more utility polymers, to get properties same or close to the expensive polymer (engineering polymers). The process is by physically combining two or more polymers (blends) [15]. A polymer blend is a combination of two or more macromolecular polymers or copolymers with an ingredient content of more than two weight percent to produce a new material with various physical characteristics. [3, 14, 16].

However, due to time and resources, there are several techniques used to design experimental setups to minimize the number of experiments, reduce costs and optimize blend ratios. These include the design of experiments (DOE), Taguchi, Artificial Neural Networks and Statistical package social science techniques. The DOE technique is robust and user-friendly, thus the choice of selecting it over others in this work. Factorial design, response surface design, custom design and mixture design are also incorporated in the DOE package to provide more options depending on the experimental objective function [17].

Schneider et al. (1997) [18]According to research on the toughening of polystyrene using natural rubber-based composite particles, PS needed to contain roughly 18 wt% of the composite natural rubber particles and compatibilizer before the impact resistance sharply enhanced.

Azide cross-linking agents are utilized by Fernández-Garca et al. (2019) [19] to join the chains of PP and PS in a PP/PS blend. When heated, the azide group loses nitrogen, forming nitrene species that are extremely reactive and inserted into C-H bonds in polymer chains. Thermal examination revealed that PS and PP were chemically joined.

Moan et al. (2000), [20] reported random terpolymer composed of maleic anhydride (MA), acrylic ester, and ethylene (E) using an immiscible system based on polyethylene (PE) and polyamine (PA). The polyamide end group and anhydride react when combined, and the in-situ produced graft copolymer acts as a compatibilizer. The terpolymer can be regarded as compatible with PE due to its high ethylene content (89.5 per cent).

This work aimed at the determination of mechanical, physical and thermal properties of low cost ter-blend of wPP/PS/NR to mimic ter-copolymer (ABS). The number of runs were determined through the use of DoE and then it was compounded using two roll mill machines and a compression machine was used to produce the blended sheet. Mechanical, physical and thermal analyses of the produced blend were conducted using the standard ASTM methods. The optimal blend ratio and associated tensile and impact strengths were determined using Design Expert.

Materials

Design Expert version 10, Stat-Ease, Inc., Minneapolis, MN, USA. Waste Polypropylene (wPP) from Ahmadu Bello University, Zaria environs. Polystyrene (PS) General purpose polyester resin (standard grade) with melt flow index 1.9 g/10 min, from Solarene G-116 by Hyundai EP. Natural Rubber (NR) from Leopard Enterprises Ltd. Alausa, Ikeja Lagos Nigeria. Sodium hydroxide (NaOH) pellets (98% Alderich reagent grade). Maleic anhydride (MA) (Alderich). Zinc Oxide (ZnO) (Alderich). Stearic Acid (AS) (Alderich). Sulphur (S) (Alderich). Mercaptobenzothiazole disulfide (MBTS) (Alderich). 2,2,4-Trimethyl-1,2-Dihydroquinoline (TMQ) (Alderich). Silicon Oil (Alderich reagent grade).

Methods

Design and optimization of polymer matrix blend experiment

Ter blend of wPP, PS and NR constitute the independent variables considered in this work. The experimental runs were designed using the D-optimal (optimal design)- Mixture experimental method. The lower and upper blend ratio is shown in Table 1, these were derived from the usual combination of monomers use in co-polymerization to produce ABS. These boundary conditions were chosen in accordance with the information given for ABS, an amorphous thermoplastic mix made up of a composition of 40–60% styrene, 5–30% butadiene, and 15–35% acrylonitrile. Various qualities can be attained depending on the combination [24].

Table 1

Summary of the design of experiment boundary conditions input
Parameter	Lower boundary	Upper boundary
Polypropylene (%)	15	35
Polystyrene (%)	40	60
Natural Rubber (%)	5	30

The data in Table 1 were inputted into the design expert software and run to generate the sixteen (16) numbers of experimental runs for the study.

Matrix preparation

Waste polypropylene (wPP) was used as substitute for virgin PP due to its availability as waste in the environment. Waste PP packages were cleaned and shredded before use. Polystyrene was in pellet form and known for its lustrous nature and strength. While the Natural rubber in its condensed form serves as an impact modifier.

From the design of the experiment, the blend for the first run was prepared by weighing 350 g, 600 g and 50 g of wPP, PS and NR respectively in different containers. 4 g of Maleic anhydride was also weighed and kept for later use. The additives for the NR vulcanization (Table 2) were also weighed. The two-roll mill machine was switched on and set to a temperature of 150 ^oC, the NR was poured into with the additives after which the remaining two polymers were added and allowed to mix until a consistent mixture was obtained in 10 mins. After which the mixture was scraped off the machine and kept for further use. The same procedure was used for preparing the other 15 runs and the control samples (pure wPP, PS and NR).

Table 2

The composition of curatives used for NR dynamic vulcanization.
Material	Parts per hundred rubber (phr)	Use
Natural Rubber (NR)	100g	Binder
Zinc Oxide (ZnO)	5g	Activator
Stearic Acid (AS)	2g	Activator
Mercaptobenzothiazole disulfide (MBTS)	1.5g	Accelerator
2,2,4-Trimethyl-1,2-Dihydroquinoline (TMQ)	1g	Accelerator
Silicon Oil	2ml	Processing oil
Sulphur (S)	2g	Curing agent

The matrix sheets were produced by placing, 2 mm thick Teflon sheets on both sides of the constructed mild steel mould to aid the ease of removal of the blend from the mould after compression. The blend was then placed in the moulds and compressed simultaneously on a double panel hydraulic press at a temperature of 150 ^oC and pressure of 3.45 MPa for 5 min and further cooling under pressure at the same pressure for 3 min.

Materials Analysis and Characterization

Statistical analysis

Five (5) specimens from each of the material composites produced were tested. The mean values of the properties, standard deviation and standard error were calculated.

Mechanical properties analyses

Impact test

The impact test was conducted following ASTM D4812 standard using the CEAST Resil Impactor Instron 6957 Model. Rectangular specimens with dimensions of 64 x 12.7 x 3.2 mm and without notches were cut from the main sample. The ASTM was followed to conduct dynamic impact testing in the low-velocity range of 2 m/s.

The impact strength was calculated using Eq. 1.

$$Impact Strength=\frac{Energy Absorbed \left(J\right)}{Thickness \left(m\right)}$$

Tensile test

The Instron machine model 3369, system number 3369K1781, capacity 50 kN, weight 141 kg, maximum speed 50 mm/min, power need 100/120/220/240V, and maximum vertical test space 1193 mm was used for the tensile test. It was carried out in accordance with ASTM D638. For every sample of the composite created for the tensile test analysis, five (5) specimens were cut to the following measurements: 100 mm x 20 mm x 3 mm.

The stress-strain curve was generated from the machine and the required parameters were generated automatically. From the results, tensile strength result was extracted and validated (Equations 2), using data from the stress-strain curve [23, 24].

$$TS =\frac{Force}{Area} \left(\frac{N}{{m}^{2}}\right)$$

Morphology

SEM analysis

A PhenomTM (Pro X) Scanning Electron Microscopy (SEM) at an operating voltage of 10 kV was used to examine the morphologies of the test samples. The samples were inserted in a Quorum (Q150R ES) machine where the specimens were coated with a thin layer of gold. Thereafter specimens were viewed via a monitor connected to the machine, at different magnifications (500–2500).

Thermal properties

Thermogravimetric analysis (TGA)

The TA instrument SDT-Q600 was used to conduct tests on the thermal degradation of blends and control samples. The sample, which had a mass of about 10 mg, was heated in oxidizing air at a rate of 10 K/min from 23 to 800°C. Thermal degradation features such as the temperature at which degradation first occurs (T_on), the temperature at which it progresses at its fastest pace (T_max), and the temperatures at which different weight losses occur were obtained from the TG curves.

Dynamic mechanical analysis (DMA)

Based on ASTM D5023, DMA was performed using DMA 242E Artemis NETZSCH. The instrument set-up also includes a sample holder with a three-point bend, a furnace with a temperature range of 30 to 120°C, a thermocouple for the furnace, and a measuring mode with a dynamic load of 4 N, a frequency range of 0.25 to 10 Hz, a heating rate of 6 K/min, and an amplitude of 60 m. A three-point bending sample holder was used to put the sample specimens onto the apparatus, which were then secured into the furnace. The sample specimens had the following dimensions: 60 x 12 x 3.2 mm. The storage modulus (El), loss modulus (E^ll), and damping factor (Tan ɗ) of the materials were calculated.

The Experimental Blending of Polymers

Significance of impact and tensile test on the blend of wPP, PS and NR

The mechanical properties (impact and tensile strength) in the blend of experimental runs are presented in Table 3. Where, both impact and tensile strength of the blend showed significant improvement as compared to the control samples sp17, sp18 and sp19. As demonstrated in Table 3, it is a well-known fact that not all mechanical properties of a certain blend combination will yield high values in those attributes. But it turns out that sp5 (PP/PS/NR; 22.5/47.5/30) and sp11 (PP/PS/NR; 30/40/30) are different, with high impact and tensile strength values of 198.16 J/m, 158.71 J/m, 158.71 J/m, and 118.04 MPa, 114.97 MPa, and 26.93 MPa, respectively.

Table 3

Effects of wPP, PS and NR concentrations on the impact and tensile strength
Sample		Independent Variable			Responses
Runs	Sample code	PP	PS	NR	Impact strength (J/m)	Std. Error	Tensile strength (MPa)	Std. Error
1	sp1	35	60	5	108.78	± 11.56	105.44	± 2.67
2	sp2	15	60	25	126.00	± 11.00	78.11	± 7.02
3	sp3	25	60	15	96.89	± 3.86	50.22	± 3.11
4	sp4	35	40	25	98.67	± 2.00	11.63	± 3.69
5	sp5	22.5	47.5	30	251.67	± 15.57	118.04	± 7.07
6	sp6	26	51	23	80.11	± 5.06	109.41	± 4.52
7	sp7	30.5	45.5	24	106.11	± 13.11	102.30	± 4.03
8	sp8	35	50	15	92.11	± 2.61	51.07	± 7.18
9	sp9	20.5	55.5	24	91.33	± 10.60	35.52	± 2.32
10	sp10	35	50	15	93.24	± 4.44	27.30	± 0.71
11	sp11	30	40	30	201.56	± 12.40	26.93	± 1.18
12	sp12	25	60	15	96.78	± 1.93	49.95	± 2.80
13	sp13	22.5	47.5	30	227.56	± 7.18	114.97	± 4.12
14	sp14	15	60	25	119.11	± 1.22	12.11	± 0.45
15	sp15	35	60	5	116.78	± 9.89	96.44	± 6.44
16	sp16	30.5	55.5	14	66.22	± 2.98	11.63	± 0.30
Control Samples
17	sp17	100	0	0	72.89	± 4.78	20.19	± 2.49
18	sp18	0	100	0	121.33	± 9.33	93.89	± 2.51
19	sp19	0	0	100	72.22	± 4.84	38.71	± 1.76

Studying the mechanical properties' analysis of variance (ANOVA)

The significance of the models is to predict the experimental value, which is subject to validation. However, the models cannot be said to be reliable without checking for consistency and adequacy. Equations 4 and 5 present the models generated from the ANOVA of tensile and impact strengths of the polymer blend, where cubic and special quartic mixture models were used for the blend of wPP, PS and NR respectively. Through the use of probability values, ANOVA can help assess the importance of each linear, quadratic, cubic, quartic, and interaction factor in the models (P-value). P values less than 0.05 were used to establish the significance of the models produced in Tables 4 and 5 for all terms. The impact and tensile strengths had a considerable impact on all independent and interaction variables.

TS =	37.82A – 282.59B + 100.91C + 955.10AB – 92.65AC + 603.77BC – 1171.31ABC – 974.77AB (A – B) + 73.20AC (A – C) + 1251.93BC (B – C)	(4)
IS =	149.53A + 123.17B + 113.16C + 422.45AB – 126.44AC – 108.84BC – 3505.41A2BC – 1147.41 AB2C – 1861.57ABC²	(5)

Where; A = wPP, B = PS, C = NR

Given the Model F-value of 97.60, the model is most likely significant. Only 0.01 percent of cases might have an F-value this high due to noise. Model terms are regarded as significant when "Prob > F" is less than 0.05. A, B, C, AB, AC, BC, A²BC, and ABC² are significant model terms in this situation. In cases where the value exceeds 0.1, model terms are not significant. The "Lack of Fit F-value" of 0.57 indicates that, in comparison to the pure error, the lack of fit is not substantial. The probability that noise is to blame for a "Lack of Fit F-value" this high is 60 percent. A negligible lack of fit is advantageous since we want the model to fit. Predictability R-Squared (Pred R-Squared) of 0.9614 and Adjustment R-Squared (Adj R-Squared) of 0.9810 are reasonably in agreement; the difference is less than 0.2. Accurate measurements are made of the signal-to-noise ratio. Preferable ratios range from 4 and up. Due to the ratio of 31.278 showing a sufficient signal, the model can be used to investigate the design space.

Given the Model F-value of 98.26, the model is most likely significant. Only 0.01 percent of cases might have an F-value this high due to noise. Model terms are regarded as significant when "Prob > F" is less than 0.05. The model terms B, C, AB, BC, ABC, AB(A - B), and BC(B - C) are significant in this case. If the value is more than 0.10, the model terms are not important. If your model has a lot of extraneous terms, model reduction may improve it (except those needed to maintain hierarchy). According to the "Lack of Fit F-value" of 2.43, there is no distinction between the lack of fit and a simple error. A "Lack of Fit F-value" this large could be caused by noise with a 17.94% probability. The model should fit; hence a non-significant lack of fit is excellent. It is not defined (N/A) what the projected R² statistic is. Because a case with a leverage of 1.00 was achieved, that is why. While the adjusted R² (0.9832) is close to unity (1.0000) and so validates the model, the R² (0.9933) is not. The signal-to-noise ratio is calculated by "Adeq Precision". More than 4 is preferred as a ratio. The signal is strong enough when the ratio is 27.047. It is presented in Table 6 and can be utilized to move about the design space.

Blend optimization

The optimization process for the blend of wPP, PS, and NR was predicted by the software and three runs were proposed. The run with the highest desirability was chosen [25]. As indicated in Table 7, the anticipated ideal combination variables in this work were 21 percent, 49 percent, and 30 percent, with a desirability value close to unity.

Table 5: Analysis of variance (ANOVA) test of the developed cubic model for Tensile Strength on the blend of PP, PS and NR.

Source

Sum of

Squares

Mean

Square

Value

p-value

Prob > F

R²

Adj R²

Error
(%)

Pred R²

Adeq
pre.

Model

21724.93

2413.88

98.26

< 0.0001

0.9933

0.9832

1.02

1.00

27.047

Linear Mixture

60.49

30.24

1.23

0.3564

3602.25

146.63

< 0.0001

29.84

1.21

0.3127

4363.99

177.64

< 0.0001

ABC

756.76

30.80

0.0014

AB(A-B)

3544.89

144.30

< 0.0001

AC(A-C)

8.02

0.33

0.5885

BC(B-C)

8411.56

342.39

< 0.0001

Residual

147.40

24.57

Lack of Fit

48.27

2.43

0.1794

Pure Error

99.13

19.83

Cor Total

21872.33

Table 7

Optimum condition for a maximum impact and tensile strength
Parameter	Goal	Experimental region		Optimized conditions	Validated result	Error (%)
		Lower Limit	Upper Limit
Fraction of wPP	In range	15	35	21
Fraction of PP	In range	40	60	49
Fraction of NR	In range	5	30	30
Tensile Strength (MPa)	Maximize	11.63	118.04	118.04	120.14	1.75
Impact Strength (J/m)	Maximize	66.23	251.67	241.62	248.9	2.93
Desirability	0.973

Diagnostics Plots and the three-Dimensional Graph for Impact and Tensile Strengths

According to the data distributions along the regression line in Fig. 1, which exhibit no outliers and demonstrate a high level of significance and a model that fits well thanks to positive interactions, the experimental study's data are in good agreement with those of the anticipated variables.

The impact strength model graph is also shown in Fig. 2. The 3D graph's elliptical shape demonstrates the model's importance. Additionally, as shown in the graph as proof, the shape of the related contour plots typically shows the significance of the reciprocal interactions between the independent variables and their responses.

Furthermore, in terms of tensile strength, comparable interaction trends were seen in the parity and 3D graphs, respectively. The usefulness of the model was further demonstrated by the good interactions seen in the plots, which demonstrated that the data were evenly distributed along the regression line and the contour form of the 3D surface plots as shown in Figs. 3 and 4, respectively.

Thermogravimetric analysis (TGA)

Thermogravimetric analysis (TGA) and derivative thermogravimetric analysis were used to examine the thermal analysis and thermal stability of polymer blends (DTG) as shown in Figs. 7 and 8.

Tables 8 and 9 present the summarized results for thermogravimetry and derivative thermogravimetry analysis for the blend of wPP/PS/NR

Table 8

Summary of TGA on weight loss on the polymer of wPP, PS, and NR
	Initial Point		Mid-Point		Final Point (Max)		Residue
Sample	wt loss (m₁)	Temp (T₁)	wt loss (m₂)	Temp (T₂)	wt loss (m₃)	Temp (T₃)	(m₄)
sp4	0	352	50	402	90.8	448	9.2
sp5	0	360	50	406	94	444	6
sp8	0	369	50	405	98	440	2
sp11	0	360	50	407	93.2	447	6.8
sp17	0	332	50	381	97.7	424	2.3
sp18	0	360	50	387	95	420	5
sp19	0	341	50	382	92	532	8

Table 9

Summary of the effect of DTG on temperature for the blend of wPP, PS, and NR
DTG
Sample	T_d Onset	T_d Peak	T_d Offset
sp4	344	439	481
sp5	353	434	451
sp8	355	433	449
sp11	349	426	451
sp17	337	391	436
sp18	320	402	438
sp19	329	379	445

In some circumstances, this study can reveal details regarding the mechanisms of degradation that cause molecular weight reduction. Figures 7 and 8 show the weight loss versus temperature for the blends (wPP, PS, and NR), as well as the derivative of the weight over temperature (differential thermogravimetry) versus temperature for control samples (wPP, PS, and NR).

Intake of oxygen results in the creation of hydroperoxides and peroxy radicals, which are unstable at higher temperatures and soon change into other labile products before being evolved. Figure 8 shows this initial mass rise. For samples that have experienced degradation, the mass gain is larger because shorter chains react with oxygen more quickly. Since volatile chemicals evolved after the reaction with oxygen, the reprocessed samples begin to lose mass more quickly [24, 25]. However, all samples of the polymer mixes will remain stable up to about 350 ^oC. However, it was discovered that more than half of the polymer blend was lost when the material was heated to a temperature between 380 and 400 ^oC. Additionally, the bulk of the compounds decomposed at temperatures higher than 400 ^oC.

However, the polymer blends will be stable up to around 350 ^oC for all samples. But, when the material was subjected to a temperature between 380–400 ^oC, it was observed that over about 50% of the polymer blend was lost. Also, when the decomposition temperature was above 400 ^oC, the majority of the samples were decomposed leaving residue, as presented in Table 9. From the DTG curves, the loss was attributed to side group scission, which was for sp17, sp18 and sp19 having a single peak (Figure )(single stage degradation). While, sp4, sp5 sp8 and sp11 with double peaks degradation, indicate mass loss as a result of scissions and depolymerization [26, 27].

Furthermore, a detailed summary of the TGA (Table 8) gives an idea about the effect of temperatures corresponding to different mass losses at; the initial point (T₁₀₀), mid-point (T₅₀), and maximum degradation (T_max). While, Table 9 gives the summary of the DTG curve via the onset of degradation (onset T_d), peak degradation temperature (peak T_d), and offset temperature at maximum degradation (offset T_d).

Dynamic Mechanical Analysis (DMA)

Dynamic mechanical analysis (DMA) is a standard technique for defining a material's properties as a function of temperature, time, frequency, stress, atmosphere, or a combination of these elements [28, 29].

Storage modulus E^l

To investigate miscibility in polymer mixtures, DMA is frequently utilized. Temperature information is supplemented by data from DMA regarding the behaviour of mixes and phase morphology. Figure 9 shows how the temperature affects the mix of wPP, PS, and NR's storage modulus (E^l).

Theoretically, all samples should experience a general drop in storage modulus as the temperature rises. Chain mobility theory can be used to explain this [30]. As a result, a temperature rise will raise the enthalpy of the composite chain, which will release a lot of internal energy that can be used to weaken or dissolve the physical bonds and entanglements linked to the polymer chains. Again, if the molecular chains move more forcefully during heating, less energy will be stored, resulting in a decrease in storage modulus.

From Fig. 9, it can be observed that storage modulus (E^l) values for the control samples were higher than the blended samples, which was a result of the addition of NR in the blend making it more flexible as compared to the pure PS and PP. But generally, the storage modulus decreased with increasing temperature in all samples, with a notable drop in the regions between 80 and 100 ^oC, after which the storage moduli are relatively the same.

Loss modulus (E^ll)

When comparing several systems at the same strain amplitude, the E^ll is a measurement of the energy lost as heat per cycle of sinusoidal deformation. It is also known as the material's viscous reaction (viscoelasticity). The variation in loss modulus (E^ll) with a temperature of the blend is illustrated in Fig. 10 and summarized in Table 10. There was no improvement in the E^ll. The unbroken polymer chains and potential for stress transfer below the T_g value were represented in the more or less consistent values of E^ll. Energy dissipation occurred when the temperature reached the T_g, and a corresponding peak was seen in the values E^ll.

Damping (Tan ɗ)

Damping, which is measured as the tangent of the phase angle, is the energy dissipation in a material under cyclic load. A material's capability to absorb energy is determined by its Tan ɗ value; the higher Tan ɗ is, the better the capacity. The ratio of E^ll to E^l is provided. It also varies according to the frequency, temperature, and state of the material. The glass transition temperature T_g of the substance is taken to be the highest temperature value of Tan ɗ. Figure 11 and Table 10 provide convincing evidence that the blended component has better damping effect and higher T_g then the control samples. They tend to absorbed more energy when subjected to engineering application. These result from the addition of three polymer, which typically causes a shift in the T_g toward higher temperatures and a peak in the loss factor (damping) due to the restriction of the movements of the matrix (polymer) chain molecules, raises the storage modulus and dampens the effect [31–33].

According to Joseph et al. (2010) [28], the inclusion of macro/microfibers reduces the damping properties of polymer fibre composites and increases storage modulus because the additional fibres obstruct the macromolecular chains' free motion. The empty matrix, in comparison, has the largest damping ratio, indicating a high level of mobility. Additionally, Sreekala et al. (2005) [29] used a DMA technique to report on the dynamic mechanical characteristics of composites made of oil palm fibre and phenol-formaldehyde. The addition of oil palm fibre was found to increase the dynamic modulus and decrease the damping properties of the composite.

Table 10

Summary of DMA values for polymer blend.
Samples	Onset E^l of T_g (^oC)	E^l (MPa)	Peak T of E^ll (^oC)	E^ll (MPa)	Peak T of Tan ɗ	Tan ɗ
sp5	79.4	199.74	27.1	29	109	0.171
sp6	68	65.34	33.4	31	88	0.18
sp17	52.5	696.59	39.9	63	81.1	0.089
sp18	93.7	590.02	90.4	60	104.3	0.061

Scanning Electron Micrograph Image of the Polymer Blend

The surface micrograph of the blend and optimized blend are presented in Fig. 12. SEM examines the surface appearance of bulk specimens and gathers signals using detectors positioned above the sample. It is the most common method for characterizing polymer surfaces.

The morphologies of the blend (sp5) and optimized (sp_opt) blend of terpolymer of wPP/PS/NR, are shown in Fig. 12. sp_opt shows a fine surface morphology with little cracks which account for high mechanical properties observed from the thermal and mechanical properties analysis. While, sp5 shows the coarsest morphological structure, which was a result of improper mixing of the polymers, leading to low mechanical properties.

For the impact and tensile strengths, the residuals and plots of the residual’s vs the projected response, respectively, showed that the residuals fall in a straight line, indicating that the errors are distributed properly. According to the overall findings, the mixture (D-optimal) technique is a useful tool for optimizing impact and tensile strength parameters, and the models that were created fit the data well.

The cubic model proved to describe the independent variable interactions better in terms of tensile strength. While, Special Quartic model describes the impact strength, as evidenced by their R²-values of 0.9991 and 0.9832 respectively. The optimum conditions for maximum impact and tensile strengths from the D.E was 241.62 J/m and 118.04 MPa respectively, with a desirability of 0.973. While the validated values got were 248.9 J/m; with an error of 2.93% and 120.14 MPa; error of 1.75% respectively. With the obtained tensile strength, the blend is suitable for engineering applications.

The weight loss of the polymer blend was found to be lower than that of blended components. While the weight loss of the modified blend at various temperatures was found to increase with the addition of nanofiber. Furthermore, DMA revealed that the T_g of the modified blend was improved, as also the loss (damping) factor. While the storage and loss moduli remain unchanged.

Acknowledgements

The authors like to acknowledge Ahmadu Bello University for providing an enabling platform for the work and King Fahd University of Petroleum & Minerals, Saudi Arabia where the TGA was conducted. Also, Dr Tanimu Gazali of the Research Institute, King Fahd University of Petroleum & Minerals, Saudi Arabia for conducting the SEM analysis for this work.

Conflict of interest statement

The authors declare no conflict of interest

Nassar, A., Nassar, E.: Effect of fiber orientation on the mechanical properties of multi layers laminate nanocomposites. Heliyon. 6, e03167 (2020). https://doi.org/10.1016/j.heliyon.2020.e03167
Kumar, A.: Advanced Ceramics for Energy and Environmental Applications. CRC Press, Boca Raton (2021)
Utracki, L.A., Mukhopadhyay, P., Gupta, R.K.: Polymer Blends: Introduction. In: Utracki, L.A. and Wilkie, C.A. (eds.) Polymer Blends Handbook. pp. 3–170. Springer Netherlands, Dordrecht (2014)
Wang, B., Gao, H.: Fibre Reinforced Polymer Composites. In: Shyha, I. and Huo, D. (eds.) Advances in Machining of Composite Materials: Conventional and Non-conventional Processes. pp. 15–43. Springer International Publishing, Cham (2021)
Naik, T.P., Singh, I., Sharma, A.K.: Processing of polymer matrix composites using microwave energy: A review. Composites Part A: Applied Science and Manufacturing. 156, 106870 (2022). https://doi.org/10.1016/j.compositesa.2022.106870
Radzi, A.M., Zaki, S.A., Hassan, M.Z., Ilyas, R.A., Jamaludin, K.R., Daud, M.Y.M., Aziz, S.A.: Bamboo-Fiber-Reinforced Thermoset and Thermoplastic Polymer Composites: A Review of Properties, Fabrication, and Potential Applications. Polymers. 14, 1387 (2022). https://doi.org/10.3390/polym14071387
Alazzam, M.B., Hajjej, F., AlGhamdi, A.S., Ayouni, S., Rahman, M.A.: Mechanics of Materials Natural Fibers Technology on Thermal Properties of Polymer. Advances in Materials Science and Engineering. 2022, e7774180 (2022). https://doi.org/10.1155/2022/7774180
Ghosh, A.: Performance modifying techniques for recycled thermoplastics. Resources, Conservation and Recycling. 175, 105887 (2021). https://doi.org/10.1016/j.resconrec.2021.105887
Hu, C., Qin, Q.-H.: Advances in fused deposition modeling of discontinuous fiber/polymer composites. Current Opinion in Solid State and Materials Science. 24, 100867 (2020). https://doi.org/10.1016/j.cossms.2020.100867
Jagadeesh, P., Mavinkere Rangappa, S., Siengchin, S., Puttegowda, M., Thiagamani, S.M.K., G., R., Hemath Kumar, M., Oladijo, O.P., Fiore, V., Moure Cuadrado, M.M.: Sustainable recycling technologies for thermoplastic polymers and their composites: A review of the state of the art. Polymer Composites. 43, 5831–5862 (2022). https://doi.org/10.1002/pc.27000
Ashothaman, A., Sudha, J., Senthilkumar, N.: A comprehensive review on biodegradable polylactic acid polymer matrix composite material reinforced with synthetic and natural fibers. Materials Today: Proceedings. (2021). https://doi.org/10.1016/j.matpr.2021.07.047
Kulshreshtha, A.K., Vasile, C.: Handbook of Polymer Blends and Composites. iSmithers Rapra Publishing (2002)
Markovic, G., Visakh, P.M.: 1 - Polymer blends: State of art. In: Visakh, P.M., Markovic, G., and Pasquini, D. (eds.) Recent Developments in Polymer Macro, Micro and Nano Blends. pp. 1–15. Woodhead Publishing (2017)
Parameswaranpillai, J., Thomas, S., Grohens, Y.: Polymer Blends: State of the Art, New Challenges, and Opportunities. In: Characterization of Polymer Blends. pp. 1–6. John Wiley & Sons, Ltd (2014)
Silverstein, M.S.: Interpenetrating polymer networks: So happy together? Polymer. 207, 122929 (2020). https://doi.org/10.1016/j.polymer.2020.122929
Coba-Daza, S., Carmeli, E., Otaegi, I., Aranburu, N., Guerrica-Echevarria, G., Kahlen, S., Cavallo, D., Tranchida, D., Müller, A.J.: Effect of compatibilizer addition on the surface nucleation of dispersed polyethylene droplets in a self-nucleated polypropylene matrix. Polymer. 263, 125511 (2022). https://doi.org/10.1016/j.polymer.2022.125511
Leardi, R.: Experimental design in chemistry: A tutorial. Analytica Chimica Acta. 652, 161–172 (2009). https://doi.org/10.1016/j.aca.2009.06.015
Schneider, M., PITH, T., LAMBLA, M.: Toughening of polystyrene by natural rubber-based composite particles: Part III Fracture mechanisms. Journal of Materials Science. 32, 5191–5204 (1997). https://doi.org/10.1023/A:1018638021609
Fernández-García, M., Arrieta, M.P., Muñoz-Bonilla, A., Echeverría, C., Sonseca, A.: Polymeric Materials: Surfaces, Interfaces and Bioapplications. MDPI (2019)
Moan, M., Huitric, J., Médéric, P., Jarrin, J.: Rheological properties and reactive compatibilization of immiscible polymer blends. Journal of Rheology. 44, 1227–1245 (2000). https://doi.org/10.1122/1.1289281
Acrylonitrile Butadiene Styrene – ABS Plastic material science, manufacturing, and its applications., https://absplasticscience.wordpress.com/
Lenau, T.: ABS - acrylonitrile butadiene styrene, http://designinsite.dk/htmsider/m0007.htm
MacDermott, C.P., Shenoy, A.V.: Selecting Thermoplastics for Engineering Applications. CRC Press (2019)
Adamu, Y., Isa, M.T., Mohammad, J.A., Bello, T.K.: Influence of double chemical treatments on properties of luffa fibre reinforced waste low density polyethylene (wLDPE) composite. Science Forum (Journal of Pure and Applied Sciences). 17, 84 (2019). https://doi.org/10.5455/sf.38589
Kandar, M.I.M., Akil, H.M.: Application of Design of Experiment (DoE) for Parameters Optimization in Compression Moulding for Flax Reinforced Biocomposites. Procedia Chemistry. 19, 433–440 (2016). https://doi.org/10.1016/j.proche.2016.03.035
Asaletha, R., Kumaran, M.G., Thomas, S.: Thermal behaviour of natural rubber/polystyrene blends: thermogravimetric and differential scanning calorimetric analysis. Polymer Degradation and Stability. 61, 431–439 (1998). https://doi.org/10.1016/S0141-3910(97)00229-2
Farha, A.H., Al Naim, A.F., Mansour, S.A.: Thermal Degradation of Polystyrene (PS) Nanocomposites Loaded with Sol Gel-Synthesized ZnO Nanorods. Polymers. 12, 1935 (2020). https://doi.org/10.3390/polym12091935
Joseph, S., Appukuttan, S.P., Kenny, J.M., Puglia, D., Thomas, S., Joseph, K.: Dynamic mechanical properties of oil palm microfibril-reinforced natural rubber composites. Journal of Applied Polymer Science. 117, 1298–1308 (2010). https://doi.org/10.1002/app.30960
Sreekala, M. s., Thomas, S., Groeninckx, G.: Dynamic mechanical properties of oil palm fiber/phenol formaldehyde and oil palm fiber/glass hybrid phenol formaldehyde composites. Polymer Composites. 26, 388–400 (2005). https://doi.org/10.1002/pc.20095

Download PDF

Journal Publication

published 23 Feb, 2024

Read the published version in Journal of Polymer Research →

Reviewers agreed at journal
30 May, 2023
Reviewers invited by journal
29 May, 2023
Editor invited by journal
28 May, 2023
Editor assigned by journal
25 May, 2023
First submitted to journal
22 May, 2023

You are reading this latest preprint version

Mechanical, Physical and Thermal Properties of Waste Polypropylene/Polystyrene/Natural Rubber (wPP/PS/NR) Blends

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Experimental

Materials

Methods

Design and optimization of polymer matrix blend experiment

Matrix preparation

Materials Analysis and Characterization

Statistical analysis

Mechanical properties analyses

Morphology

Thermal properties

Results and Discussion

The Experimental Blending of Polymers

Studying the mechanical properties' analysis of variance (ANOVA)

Blend optimization

Diagnostics Plots and the three-Dimensional Graph for Impact and Tensile Strengths

Thermogravimetric analysis (TGA)

Dynamic Mechanical Analysis (DMA)

Storage modulus E^l

Loss modulus (E^ll)

Damping (Tan ɗ)

Scanning Electron Micrograph Image of the Polymer Blend

Conclusions

Declarations

References

Status:

Journal Publication

Version 1

Mechanical, Physical and Thermal Properties of Waste Polypropylene/Polystyrene/Natural Rubber (wPP/PS/NR) Blends

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Experimental

Materials

Methods

Design and optimization of polymer matrix blend experiment

Matrix preparation

Materials Analysis and Characterization

Statistical analysis

Mechanical properties analyses

Morphology

Thermal properties

Results and Discussion

The Experimental Blending of Polymers

Studying the mechanical properties' analysis of variance (ANOVA)

Blend optimization

Diagnostics Plots and the three-Dimensional Graph for Impact and Tensile Strengths

Thermogravimetric analysis (TGA)

Dynamic Mechanical Analysis (DMA)

Storage modulus El

Loss modulus (Ell)

Damping (Tan ɗ)

Scanning Electron Micrograph Image of the Polymer Blend

Conclusions

Declarations

References

Status:

Journal Publication

Version 1

Storage modulus E^l

Loss modulus (E^ll)