Green composites based on thermoplastic starches and various natural plant fibers: Impacting parameters of the mechanical properties using machine‐learning

Morin, Sophie; Dumoulin, Lionel; Delahaye, Louise; Jacquet, Nicolas; Richel, Aurore

doi:10.1002/pc.26071

Cited by 12 publications

(10 citation statements)

References 33 publications

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“… 36 , 8 The mechanical properties of SNFTs decreased with a decrease in the percentage of cellulose. Our analysis was consistent with the recent work of Morin et al, 9 who used machine learning to confirm the significance of the cellulose and lignin content and length of fibers on the mechanical properties of SNFTs.…”

Section: Introductionsupporting

confidence: 92%

“…5,6,7,8 However, the chemical composition of the fibers, their manufacturing methods, and their morphology can be considered the most important parameters that influence the mechanical performance of thermoplastic composites reinforced with short natural fibers. 2,9,10,11,12 Table 1 presents an overview of the mechanical properties of various short natural fiber-reinforced plastic composites, considering the chemical composition of the fibers. The evaluation showed that bio-composites containing natural fibers with a high combined content of cellulose and hemicellulose exhibited the best mechanical properties (higher Young's modulus and yield stress).The fibers consist of cellulose, hemicellulose, and lignin.…”

Section: Introductionmentioning

confidence: 99%

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Effect of cellulose and lignin content on the mechanical properties and drop-weight impact damage of injection-molded polypropylene-flax and -pine fiber composites

Nasri

Loranger

Toubal

2023

Journal of Composite Materials

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Designing bio-composites for structural applications requires a thorough understanding of their mechanical behavior. In this study, we examined the differences in the tensile strength and drop-weight impact response between polypropylene reinforced with flax fibers and that reinforced with pinewood short fibers, as both fibers differ in composition (cellulose, hemicellulose, and lignin) and length-to-diameter ratio. We found that flax fibers, which have higher cellulose content and are twice as long as pine fibers, increased the stiffness and shock resistance of bio-composite materials. However, pine fibers, which contain more lignin, showed increased material ductility and energy absorption. Impulse excitation, acoustic emission and micro-CT techniques were used to evaluate the post-impact mechanical properties and the contribution of each damage mechanism to the final material failure (tearing). The experimental results were used to validate a model based on finite elements. Our results revealed that the experimental and finite-element analyses were in good agreement.

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Section: Introductionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Effect of cellulose and lignin content on the mechanical properties and drop-weight impact damage of injection-molded polypropylene-flax and -pine fiber composites

Nasri

Loranger

Toubal

2023

Journal of Composite Materials

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“…The MLR model is a supervised machine learning model that examines the relationship between one target variable and two or more feature variables. 38 The model is trained by determining the coefficients with the smallest difference between the predicted and actual values of the target variable. Although the MLR is simple, fast to model and can handle large databases, it is not suitable for non-linear data.…”

Section: Mlr Modelmentioning

confidence: 99%

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

et al. 2023

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Tensile strength is the major mechanical property of basalt continuous fiber and is closely related to their chemical composition. This study constructed a machine learning model framework to predict the tensile strength of basalt continuous fiber. The database included the characteristic variables of oxides and their derived parameters, as well as the target variables of tensile strength. The mean squared error (MSE) were calculated to evaluate the performance of six machine learning models of decision tree, kernel ridge regression, multivariable linear regression, support vector regression, random forest, and k‐nearest neighbors. The k‐nearest neighbors model had a minimum MSE and was tuned using the grid search method with cross‐validation. The optimal hyper‐parameters of the k‐nearest neighbors model were K = 6, p = 10, and the determination coefficient of the model reached the maximum of 0.7110 on the test data. The limitations and implications of the present study were also demonstrated. We finally expected that the constructed framework could achieve higher prediction accuracy for tensile strength at a low cost in the future.Highlights A machine learning framework was built to predict the tensile strength of BCF. Oxides and their derived parameters were used for modeling. K‐nearest neighbors performed best in predicting the tensile strength. The framework was robust, cost‐saving, and efficient.

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“…[ 5 ] Mechanical performance is superior in films and sheets with a high amylose starch content. [ 6 ]…”

Section: Introductionmentioning

confidence: 99%

“…performance is superior in films and sheets with a high amylose starch content. [6] Amylose chains can form more hydrogen bonds than amylopectin, which contributes to forming more vital films. As a result, amylose is often preferred for film formation over amylopectin.…”

mentioning

confidence: 99%

The Effect of Curcumin and Cornstarch Biopolymers on the Shelf Life of Fresh Cheese: Physicomechanical and Antimicrobial Properties

Almajidi,

Lup,

Ramírez‐Coronel

et al. 2023

Starch Stärke

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Natural polymers are used more frequently in packaging as alternatives to synthetic plastics. Cornstarch is one of the cheapest carbohydrate biopolymers that form films with suitable properties and appearance but considerably larger hydrophilicity and low mechanical characteristics compared to synthetic films. Incorporating curcumin into cornstarch films improves mechanical and antimicrobial properties and curcumin release into aqueous solutions of the films. The results show that the introduction of curcumin decreases the water solubility of the edible films from 3.58% to 1.49% and the moisture absorption from 2.87% to 1.94% (p < 0.05) and increases the length of films from 40% to 80% without changing their thickness. Examination of color properties shows that the increased curcumin concentration leads to a decrease in transparency and an increase in the redness and yellowness indices. These properties change from 72.2, 3.5, and 5.1 to 39.2, 21, and 13.7, respectively. The number of mold and yeast colonies in the cheese sample decreases during the storage period of 5 days. Generally, the findings indicate that the bioactive film of corn starch and curcumin (0.5%) can be used as a suitable coating for food products.

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Green composites based on thermoplastic starches and various natural plant fibers: Impacting parameters of the mechanical properties using machine‐learning

Cited by 12 publications

References 33 publications

Effect of cellulose and lignin content on the mechanical properties and drop-weight impact damage of injection-molded polypropylene-flax and -pine fiber composites

Effect of cellulose and lignin content on the mechanical properties and drop-weight impact damage of injection-molded polypropylene-flax and -pine fiber composites

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

The Effect of Curcumin and Cornstarch Biopolymers on the Shelf Life of Fresh Cheese: Physicomechanical and Antimicrobial Properties

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