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Article Type: Research Paper
Date of acceptance: October 2024
Date of publication: November 2024
DoI: 10.5772/geet.20240012
copyright: ©2024 The Author(s), Licensee IntechOpen, License: CC BY 4.0
Bioplastics are of great importance and are viable in domestic and industrial applications. The eco-friendly polymers derived from agricultural wastes mitigate and substitute the use of their petro-plastic counterparts because they are safe, inexpensive to produce, and biodegradable. This work focused on the synthesis and optimization of bioplastic from waste corn. The experimental design is a requisite to the main experiment in order to reduce the number of experimental runs while minimizing errors. The experiment was designed using the Box–Behnken response surface methodology of central composite design of Minitab 19.0, in which low and high bounds are set for the process variables with 6 centered points and 46 runs. The statistical analysis gave
bioplastics
eco-friendly synthesis
response surface methodology
optimal conditions
biodegradable polymer
Author information
Eco-friendly polymers from plants substitute their petro-plastic counterparts.
Corn is selected due to its starch abundance and apt amylose/amylopectin proportion.
The statistical analysis shows good model strength of the response variables.
Optimal energy consumed, the weight of bioplastic, tensile strength, and elongation are obtained.
Waste corn can be used as bioplastic for electrical insulation and packaging.
A staggering estimate of 400 million metric tons of plastic waste is generated around the world annually, most of which are non-biodegradable [1–5]. This represents a 45% increase when compared to the estimate presented in 2018 by the United States Environmental Protection Agency [6]. Despite reported environmental concerns, non-biodegradable plastics are widely used for packaging due to their high barrier characteristics, stiffness, tensile strength, and tear strength. The continued use of these plastics has resulted in a growing proportion of waste piles constituting plastic waste. In addition to non-biodegradability, petroleum-based plastics have a low water vapor transmission rate owing to their large carbon footprint and thus recalcitrant in the environment [7]. Apart from environmental considerations, biodegradable polymers improve resource use, landfill reduction, and material recovery [8–10].
Petro-plastic waste contamination can cause devastating effects on both land and aquatic animals, thereby destabilizing natural ecosystems. The possibility of chemical leaching from plastic products, which can be absorbed from the soil into plants, and injection of microplastics could transfer toxins to wildlife and humans, leading to hormonal disorders, cancer, infertility, autism, and pathogenic infections [11, 12]. One of the ways to circumvent the challenges associated with non-biodegradable plastics is the application of bioplastics, which have the tendency to biodegrade in the environment. However, such bio-based plastic needs to possess the characteristics of stiffness, tensile strength, and tear strength to effectively serve as an alternative.
Bio-based plastics are derived naturally from plants while some microorganisms can transform biomass into plastics. For example, lignin, proteins, lipids, and polysaccharides (e.g., starch, chitin, and cellulose) are natural materials that can be directly used to make bioplastics [13, 14]. Adding synthetic or other materials to a polymer matrix changes the mechanical and physical properties of polymer base composites [15–19]. Due to the availability of suitable precursors from agricultural and/or agro-industrial wastes, it was anticipated that the global market for biodegradable polymers would increase by 12.6% on an annualized basis, reaching 206 million pounds by 2020 and 675 million pounds by 2030 [20, 21]. Cassava starch has been reported as a suitable precursor for the synthesis of plastics with the required properties [22, 23]. In addition, Viana
Bioplastics are generally manufactured in three types on a commercial scale: biodegradable plastics made from fossil carbon sources, biodegradable plastics made from biomass-derived polymers, and non-biodegradable biomass-derived polymers. Traditional biotechnological approaches have only had limited success converting biomass to industrially useful polymers, indicating that complex networks acting in synergy are needed [27]. Notably, in the European Union, recycling containers like compostable plastics from naturally regenerating materials like crops have gained attention [8, 28, 29]. This advancement has necessitated research efforts to seek more bioplastic precursors in waste crops, in support of plastic circularity.
West Africa, especially Nigeria, has historically grown crops such as corn, which is predominantly cellulose and starch. Corn starch has been found to contain 28 wt% amylose and 72 wt% amylopectin; these two molecules, branching amylopectin and linear and helical amylose, are necessary for bioplastic synthesis [14]. Moreover, the production of next-generation materials and processes is driven by renewable biocomposites owing to their industrial, environmental, and ecological sustainability, and the green chemistry involved [30, 31]. Hence, the aim of this work was to statistically model and optimize the production of bioplastic from waste corn. Furthermore, the physical and mechanical properties of the synthesized bioplastics such as weight, tensile strength, and elongation were optimized using polynomial regression analysis.
As depicted in Figure 1, bioplastics will solve the problems of littering, biofouling, chemical injection, sedimentation, and so on, as characterized by the unchecked disposal of petro-plastics. These environmental challenges are gaining attention globally.
This research contributes to the field of sustainable materials and waste management by proposing a biodegradable alternative product for packaging while cleaning the environment of waste.
In the course of these experiments and the processing of waste corn into bioplastic, the following apparatus and instruments were used: glass flasks (250 ml and 100 ml); magnetic hot plate and stirrer (Jiangsu J. Instrument Tech, China); thermometers (−10 to 110 °C and −10 to 360 °C); stopwatch (Accusplit, China); digital mass balance (Scientific Enterprises, Kochi, India); glass spoons; foil papers; graduated cylinders (10 ml and 100 ml); dumbbell mold (16 cm × 2 cm × 0.5 cm); heating and drying oven (Genlab Limited, UK).
The following materials were also used: starch extracted from waste corn (95% purity, 5% moisture content; Chemical and Polymer Laboratory, Lagos State University, Epe, Lagos, Nigeria); glycerin (99% purity; Veeclare Chemicals, Ojota, Lagos, Nigeria); vinegar (90% purity; Veeclare Chemicals, Ojota, Lagos, Nigeria); distilled water.
Waste corn, a starchy agricultural crop waste, used as the precursor for bioplastic synthesis, was obtained from a food processing industry. The waste corn was soaked in pure water for 48 h and thereafter milled, washed, and screened through a fine screen of 120 US mesh to remove the starch. The resulting thin slurry was transferred into a cloth sieve of 500 US mesh under pressure to remove water content. The obtained starch was sun-dried for 4 days.
Response variables such as energy consumed (computed from the amount of heat absorbed/consumed by each sample), elongation, tensile strength (obtained by analyzing samples on the Instron tensile machine), and weight were recorded.
According to [14]; 15.0 g of the starch obtained was dissolved in 50 ml of distilled water in a 250 ml beaker. The mixture was stirred on a hot plate at 105 °C and then 3.0 ml of glycerin was added [32], followed by the addition of 2.5 ml of vinegar while stirring continued for 5 min. Thereafter, the flask was removed from the hot plate and transferred into an oil-greased dumbbell wooden mold with the aid of aluminum foil paper for curing to take place. The product formed (bioplastic) took the shape of the mold at this point. The mold was baked at above 90 °C for 4 h, and then the bioplastic was extracted from the mold. Subsequently, the procedure was repeated by varying the weight of starch, volumes of glycerin, vinegar, and water, and mixing time.
To test for biodegradability or bioplastic decay, random samples of bioplastic formed were buried in humus soil for 15 days, about 20 cm deep, with an average soil temperature of 23.5 °C [4] and 1.80% moisture [33].
In a compression test, the specimen is placed between two plates, and a force is applied by closing the crossheads. The specimen is compressed, and the resulting deformation is measured and plotted against the applied force.
Using the same Instron testing machine (model 3369), compressive testing is carried out similarly to tensile strength testing. Anvils take the role of gripping jaws, and rather than pulling away, the crosshead moves in the direction of the stationary grasp. In contrast to dogbone specimens, compressive testing of the plastic film sample is carried out on typically thick pads under far less force. For plastics and rubbers, this kind of test arrangement is usually appropriate. The specimen is pushed inward by opposing uniaxial forces acting on the plastic film sample from opposite sides. According to ASTM E9-19, compressive testing is carried out with loading forces that are the opposite of those in conventional tensile strength tests.
The experimental design for the bioplastic synthesis experiment was implemented with the Box–Behnken Design (BBD) of response surface methodology (RSM) as presented in Table 1. The BBD is considered to be one of the most proficient and powerful designs among others such as central composite design, Doehlert design, and full factorial design. Typically, for the five factors considered, the BBD involves a total of 46 experimental runs with 21 coefficients in the quadratic model.
Factor type | Name | Units | Low | High |
---|---|---|---|---|
A [Numeric] | Mass of starch, | g | 10 | 20 |
B [Numeric] | Volume of glycerin, | ml | 2 | 3 |
C [Numeric] | Volume of vinegar, | ml | 2 | 3 |
D [Numeric] | Volume of water, | ml | 50 | 60 |
E [Numeric] | Time, | min | 5 | 15 |
W [Numeric] | Energy consumed | kJ | ||
X [Numeric] | Weight of bioplastic | g | ||
Y [Numeric] | Tensile strength | MPa | ||
Z [Numeric] | Elongation | % |
The design equation is of the following form:
The experimental results for the production of bioplastic from waste corn, presented in Table 2, show the effects of the independent variables—mass of starch, volume of glycerin, volume of vinegar, volume of water, and time—on the response variables.
Factors | Responses | ||||||||
---|---|---|---|---|---|---|---|---|---|
Run | |||||||||
1 | 15 | 3.0 | 2.5 | 50.0 | 10 | 240 | 34.50 | 0.4173 | 9.80 |
2 | 20 | 2.5 | 3.0 | 55.0 | 10 | 360 | 51.75 | 0.4350 | 9.20 |
3 | 15 | 2.5 | 2.5 | 55.0 | 10 | 270 | 39.00 | 1.6916 | 6.90 |
4 | 15 | 2.0 | 3.0 | 55.0 | 10 | 270 | 39.00 | 1.5433 | 11.30 |
5 | 20 | 2.5 | 2.5 | 55.0 | 15 | 390 | 48.75 | 0.7400 | 6.80 |
6 | 15 | 2.5 | 2.5 | 50.0 | 5 | 210 | 36.00 | 0.5400 | 7.30 |
7 | 10 | 2.5 | 2.5 | 60.0 | 10 | 210 | 32.25 | 1.1135 | 14.30 |
8 | 15 | 3.0 | 2.5 | 60.0 | 10 | 270 | 44.25 | 1.3450 | 12.40 |
9 | 15 | 2.0 | 2.5 | 55.0 | 15 | 300 | 38.10 | 0.5740 | 13.40 |
10 | 15 | 2.5 | 2.5 | 60.0 | 5 | 240 | 41.25 | 0.3010 | 11.80 |
11 | 10 | 2.5 | 2.5 | 55.0 | 5 | 180 | 30.75 | 0.5470 | 12.70 |
12 | 15 | 3.0 | 3.0 | 55.0 | 10 | 270 | 40.50 | 1.003 | 8.43 |
13 | 10 | 2.5 | 2.0 | 55.0 | 10 | 210 | 30.00 | 0.4159 | 14.74 |
14 | 20 | 2.5 | 2.5 | 55.0 | 5 | 330 | 46.50 | 0.7500 | 7.10 |
15 | 15 | 2.5 | 2.0 | 50.0 | 10 | 240 | 34.50 | 0.9678 | 0.85 |
16 | 20 | 2.5 | 2.0 | 55.0 | 10 | 360 | 47.25 | 0.3564 | 9.20 |
17 | 15 | 2.5 | 2.5 | 60.0 | 15 | 300 | 41.25 | 0.3423 | 8.80 |
18 | 15 | 2.5 | 2.5 | 50.0 | 15 | 270 | 38.25 | 0.1658 | 6.43 |
19 | 10 | 2.5 | 2.5 | 55.0 | 15 | 240 | 30.75 | 0.8950 | 5.70 |
20 | 15 | 2.5 | 2.0 | 55.0 | 5 | 240 | 38.25 | 0.9560 | 8.40 |
21 | 15 | 3.0 | 2.5 | 55.0 | 5 | 240 | 38.25 | 1.2560 | 11.90 |
22 | 15 | 2.5 | 2.0 | 60.0 | 10 | 270 | 41.00 | 0.4489 | 7.00 |
23 | 15 | 2.5 | 2.5 | 55.0 | 10 | 270 | 39.00 | 1.6600 | 6.70 |
24 | 15 | 2.0 | 2.5 | 60.0 | 10 | 270 | 40.50 | 0.1764 | 7.40 |
25 | 20 | 2.5 | 2.5 | 60.0 | 10 | 360 | 50.25 | 1.4560 | 8.50 |
26 | 15 | 2.0 | 2.5 | 55.0 | 5 | 240 | 38.25 | 1.5640 | 7.70 |
27 | 10 | 2.5 | 2.5 | 50.0 | 10 | 210 | 28.50 | 0.3140 | 6.67 |
28 | 15 | 2.5 | 2.5 | 55.0 | 10 | 270 | 39.00 | 1.6820 | 13.31 |
29 | 15 | 2.5 | 3.0 | 55.0 | 15 | 300 | 40.50 | 1.4320 | 14.87 |
30 | 20 | 3.0 | 2.5 | 55.0 | 10 | 360 | 49.50 | 0.7905 | 13.41 |
31 | 10 | 2.0 | 2.5 | 55.0 | 10 | 210 | 28.50 | 0.4292 | 6.51 |
32 | 15 | 3.0 | 2.0 | 55.0 | 10 | 270 | 39.00 | 1.3915 | 7.50 |
33 | 15 | 2.5 | 2.0 | 55.0 | 15 | 300 | 38.25 | 0.8965 | 9.30 |
34 | 15 | 3.0 | 2.5 | 55.0 | 15 | 300 | 38.25 | 0.4446 | 14.50 |
35 | 15 | 2.0 | 2.5 | 50.0 | 10 | 240 | 34.50 | 1.2498 | 13.40 |
36 | 15 | 2.5 | 2.5 | 55.0 | 10 | 270 | 39.00 | 1.7600 | 10.38 |
37 | 10 | 3.0 | 2.5 | 55.0 | 10 | 210 | 31.50 | 0.1930 | 8.91 |
38 | 15 | 2.5 | 3.0 | 55.0 | 5 | 240 | 41.25 | 0.8759 | 6.60 |
39 | 15 | 2.5 | 3.0 | 50.0 | 10 | 240 | 40.50 | 0.8532 | 6.43 |
40 | 20 | 2.0 | 2.5 | 55.0 | 10 | 360 | 47.25 | 1.2500 | 5.00 |
41 | 15 | 2.5 | 3.0 | 60.0 | 10 | 270 | 43.65 | 1.7630 | 15.67 |
42 | 15 | 2.5 | 2.5 | 55.0 | 10 | 270 | 39.00 | 1.8100 | 9.40 |
43 | 20 | 2.5 | 2.5 | 50.0 | 10 | 360 | 47.10 | 1.9502 | 12.80 |
44 | 10 | 2.5 | 3.0 | 55.0 | 10 | 210 | 32.25 | 0.4913 | 7.20 |
45 | 15 | 2.0 | 2.0 | 55.0 | 10 | 270 | 36.00 | 0.5617 | 3.02 |
46 | 15 | 2.5 | 2.5 | 55.0 | 10 | 270 | 39.00 | 1.6990 | 12.35 |
Figure 2 shows the six-centered point samples synthesized for the BBD of response surface methodology, indicating the uniformity in the appearance of the products. Table 2 also shows that these six-centered point samples have close responses and are therefore reproducible.
The effect of water on the produced bioplastic composition can be observed vividly when comparing runs 1 and 8 having 50.0 ml and 60.0 ml of water, respectively, but with other parameters held constant: run 8 has 12.50% more energy consumed, 28.26% higher weight of bioplastic, 222.31% more tensile strength, and 26.53% better elongation. Expectedly, the increase in water proportion resulted in a higher weight of bioplastic except that the mold is baked at above 90 °C to allow excess water to evaporate. The presence of water has been found to increase water vapor permeability and elongation of the bioplastic [34, 35]. Although the addition of water during the mixing stage aids the plasticization, injection molding can be affected by excess water as voids are created in the mold cavity when the water evaporates, leading to the production of stiffer but brittle and less elastic biopolymers. In addition, moisture content has been shown to increase the biodegradability of bioplastics. The starch bioplastic produced by Shafqat
The effect of glycerol (plasticizer) on the produced bioplastic composition can be seen in runs 8 and 24 having 3.0 ml and 2.0 ml of glycerol with other parameters held constant, respectively. The trend shows that run 8 has 9.26% higher weight of bioplastic, 622.47% more tensile strength, and 67.56% better elongation; a similar trend is depicted in runs 7 and 27. Glycerol is hydrophilic and thus can form water molecules through its hydroxyl group, thereby increasing the size of the biopolymer. Moreover, plasticizers are vital in impacting the mechanical properties of the bioplastic produced. Glycerol acted as a plasticizer, yielding increased film thickness and creating a slippage of starch chains over each other for flexibility. As observed in this study, a 50% increase in glycerol proportion led to a 622.47% increase in tensile strength. According to Tarique
The effect of vinegar on the produced bioplastic composition can be seen in runs 17 and 22 having 2.5 ml and 2.0 ml of vinegar with other parameters held constant, respectively. Run 22 has 11.11% less energy consumed, 0.61% lesser weight of bioplastic, 31.14% more tensile strength, and 25.71% lesser elongation, as excess vinegar content leads to less cohesion between the starch chains. Vinegar imparts elongation and flexibility to the plastic film.
The effect of the mass of starch on the produced bioplastic composition can be seen in runs 7 and 25 having 10 g and 20 g of starch with other parameters held constant, respectively. Run 7 consumed 71.43% less energy, 55.81% lesser weight of bioplastic, 30.76% lesser tensile strength, and 68.24% better elongation. This showed that an optimal mixing time is required. Furthermore, the greater the mass of starch, the harder the bioplastic [13]. A similar attribute is reproduced in runs 27 and 43.
Bioplastics are made by physically combining biomass into a single component [38]. The effect of the mixing time on the produced bioplastic composition can be seen in runs 10 and 17, having 5 min and 15 min mixing time with other parameters held constant, respectively. Run 17 with more mixing time consumed 25.00% more energy, no weight difference, 13.72% more tensile strength, and 34.09% less elongation. Samer
The interactive effect of the mass of starch (g), the volume of glycerin (ml), the volume of vinegar (ml), the volume of water (ml), and time (min) on the response of the system was assessed by plotting three-dimensional curves of the response against the predicting variables as shown in Figures 3–6. The response distribution in this experimental work with respect to the variation of the independent variables shows that for a higher
Within the limit of statistical optimization as depicted by this contour plot, the energy consumed, the weight of bioplastic, tensile strength, and elongation are found to be 289.64 kJ, 44.15 g, 1.44 MPa, and 12.02%, respectively, when the optimum predicting mass of starch, the volume of glycerin, volume of vinegar, volume of water, and time are 17.27 g, 2.0 ml, 3.0 ml, 50.8 ml, and 10.25 min, respectively, as depicted in Figure 7.
The analysis of variance (ANOVA) for each of the response variables was performed at a 95% confidence level, indicating a 5% level of significance (𝛼 = 0.05). The model results for the ANOVA for the quadratic model for energy consumed are presented in Table 3. The
Source | Sum of squares | Df | Mean square | ||
---|---|---|---|---|---|
Model | 1.122E+05 | 20.0000 | 5609.8400 | 124.6600 | <0.0001 |
A: Mass of starch | 90000.0000 | 1.0000 | 90000.0000 | 2000.0000 | <0.0001 |
B: Volume of glycerin | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
C: Volume of vinegar | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
D: Volume of water | 2025.0000 | 1.0000 | 2025.0000 | 45.0000 | <0.0001 |
E: Time | 1.4400E+04 | 1.0000 | 1.4400E+04 | 0.3200E+03 | <0.0001 |
AB | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
AC | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
AD | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
AE | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
BC | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
BD | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
BE | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
CD | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
CE | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
DE | 0.0000 | 1.0000 | 0.0000 | 0.0000 | 1.0000 |
A2 | 3068.1800 | 1.0000 | 3068.1800 | 68.1800 | <0.0001 |
B2 | 13.6400 | 1.0000 | 13.6400 | 0.3030 | 0.5869 |
C2 | 13.6400 | 1.0000 | 13.6400 | 0.3030 | 0.5869 |
D2 | 1104.5500 | 1.0000 | 1104.5500 | 24.5500 | <0.0001 |
E2 | 13.6400 | 1.0000 | 13.6400 | 0.3030 | 0.5869 |
| 1125.0000 | 25.0000 | 45.0000 | ||
Lack of fit | 1125.0000 | 20.0000 | 56.2500 | ||
Pure error | 0.0000 | 5.0000 | 0.0000 | ||
| 1.133E+05 | 45.0000 |
Factor | Coefficient estimate | Df | Standard error | 95% CI low | 95% CI high | VIF |
---|---|---|---|---|---|---|
BE | 0.0000 | 1.0000 | 3.3500 | −6.9100 | 6.9100 | 1.0000 |
CD | 0.0000 | 1.0000 | 3.3500 | −6.9100 | 6.9100 | 1.0000 |
CE | 0.0000 | 1.0000 | 3.3500 | −6.9100 | 6.9100 | 1.0000 |
DE | 0.0000 | 1.0000 | 3.3500 | −6.9100 | 6.9100 | 1.0000 |
A2 | 18.7500 | 1.0000 | 2.2700 | 14.0700 | 23.4300 | 1.2000 |
B2 | −1.2500 | 1.0000 | 2.2700 | −5.9300 | 3.4300 | 1.2000 |
C2 | −1.2500 | 1.0000 | 2.2700 | −5.9300 | 3.4300 | 1.2000 |
D2 | −11.2500 | 1.0000 | 2.2700 | −15.9300 | −6.5700 | 1.2000 |
E2 | −1.2500 | 1.0000 | 2.2700 | −5.9300 | 3.4300 | 1.2000 |
Weight | Tensile strength | Elongation | Energy consumed | |
---|---|---|---|---|
Std. dev. | 0.9130 | 0.4140 | 3.4500 | 6.7100 |
Mean | 39.2400 | 0.9650 | 9.4400 | 271.300 |
C.V. % | 2.3300 | 42.9600 | 36.600 | 2.4700 |
0.9860 | 0.8550 | 0.8360 | 0.9900 | |
Adjusted | 0.9750 | 0.7800 | 0.7280 | 0.9820 |
Predicted | 0.9450 | 0.6790 | 0.6400 | 0.9600 |
Adeq precision | 37.8330 | 5.4660 | 3.8890 | 46.3300 |
Table 6 summarizes the lack of fit analyses for additional response variables that are not included in Table 3. Specifically, lack of fit was statistically evaluated for the weight of bioplastic, tensile strength, and elongation to assess deviations from model fitness. The results indicate that the lack of fit values were negligible, suggesting an excellent fit of the data with minimal error, thereby confirming the model's suitability for these response variables.
Responses | Source | DF | Adj SS | Adj MS | ||
---|---|---|---|---|---|---|
Weight of bioplastic, | Error | 25 | 22.72 | 0.91 | ||
Lack of fit | 20 | 22.72 | 1.14 | * | * | |
Pure error | 5 | 0 | 0 | |||
Total | 45 | 1496.12 | ||||
Tensile strength, | Error | 25 | 4.7351 | 0.1894 | ||
Lack of fit | 20 | 4.7187 | 0.23594 | 72.22 | 0 | |
Pure error | 5 | 0.0163 | 0.00327 | |||
Total | 45 | 12.9745 | ||||
Elongation, | Error | 25 | 294.702 | 11.7881 | ||
Lack of fit | 20 | 257.372 | 12.8686 | 1.72 | 0.285 | |
Pure error | 5 | 37.329 | 7.4659 | |||
Total | 45 | 510.711 |
The statistical correlation is often shown by regression results and significance between the independent variables and the dependent variables. The correlation coefficient (
Hence, for evaluating and analyzing the explanatory strength of models with different numbers of independent variables, the adjusted
The predictive models are as follows:
Similar model types were generated for the weight of bioplastic, tensile strength (MPa), and elongation (%):
Tables 7 and 8 present the optimized response predictions and validation analyses, respectively, for the specified process variables. These tables support the findings by comparing the predicted outcomes with the experimentally validated results, thereby verifying the reliability and accuracy of the optimization approach. The data underscores the effectiveness of the optimized conditions in enhancing the desired response variables.
Variable | Setting | |||
---|---|---|---|---|
A: Mass of starch (g) | 17.2727 | |||
B: Volume of glycerin (ml) | 2 | |||
C: Volume of vinegar (ml) | 3 | |||
D: Volume of water (ml) | 50.8081 | |||
E: Time (min) | 10.2525 | |||
Z: Elongation (%) | 12.02 | 3.57 | (4.66, 19.38) | (1.81, 22.22) |
Y: Tensile strength (MPa) | 1.439 | 0.453 | (0.507, 2.372) | (0.146, 2.733) |
X: Weight of bioplastic (g) | 44.150 | 0.992 | (42.107, 46.193) | (41.316, 46.983) |
W: Energy consumed (kJ) | 289.64 | 6.98 | (275.26, 304.01) | (269.70, 309.58) |
Process variables | Synthesized | Statistic | Deviation | % error | Std. dev. |
---|---|---|---|---|---|
Energy (kJ) | 290.500000 | 289.637500 | −0.862500 | −0.297786 | 0.609879 |
Weight of film (g) | 44.200000 | 44.149900 | −0.050100 | −0.113477 | 0.035426 |
Tensile strength (MPa) | 1.450200 | 1.439300 | −0.010900 | −0.757313 | 0.007707 |
Elongation (%) | 11.950000 | 12.019400 | 0.069400 | 0.577399 | 0.049073 |
The moisture contents of the randomly selected samples are determined as follows:
The % moisture of the selected samples shown in Figure 8 above are 5%, 7%, and 10%, respectively.
The biodegradation test showed that the selected bioplastic samples of masses 11.5 g, 8.0 g, and 10.0 g, respectively, before burial are depleted by mass after 15 days of burial, resulting in masses of 6.0 g, 5.0 g, and 4.5 g, respectively, at 47.83%, 37.5%, and 55% mass depletion rates. This proved that the produced bioplastic meets the material circularity or sustainability requirement. Comparable biodegradability results have been reported by researchers. For instance, Marichelvam
The study designed, statistically analyzed, and optimized bioplastic synthesis from waste corn.
The correlation coefficient,
Biofillers like eggshell, chitosan, bagasse, or any suitable polysaccharides and proteins can be added to the bioplastic at the optimal conditions to improve its tensile strength and elongation, thermal resistance, moisture, or gas barriers, stiffness, flexibility, and durability, making it useful in more applications.
This research did not receive external funding from any agencies.
Not Applicable.
Source data sharing is not applicable.
The authors declare no conflict of interest.
Written by
Article Type: Research Paper
Date of acceptance: October 2024
Date of publication: November 2024
DOI: 10.5772/geet.20240012
Copyright: The Author(s), Licensee IntechOpen, License: CC BY 4.0
© The Author(s) 2024. Licensee IntechOpen. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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