Development and optimization of Al 2 O 3 –HAp bifiller reinforced PLA/CS bio nanocomposites for improved tribo-mechanical performance using GRA-RSM approach

Abstract

The present article enhances the understanding of wear behaviour of developed bifiller-reinforced polymeric bio-nanocomposites (BRPBNCs) reinforced with a bifiller hydroxyapatite (HAp) (20 wt.%) and aluminum oxide (Al₂O₃) (1, 2, and 3 wt.%) into the PLA/CS blend-based matrix and pioneers the combination of optimization methods with various tribological parameters to Improved Tribo-Mechanical Performance Using Response Surface Methodology-based Grey Relational Analysis. BRPBNCs samples were prepared via the solid compression technique. The mechanical and tribological properties of the developed BRPBNCs samples were examined through compression and wear tests, respectively, using a compression testing machine and a pin-on-disk machine. All samples were tested under dry conditions. Notably, the BRPBNCs samples were prepared using bifiller materials of 20 wt% HAp and 3 wt% Al₂O₃, which increased the compressive strength of these samples by 2.56 times and compressive modulus by 1.71 times compared to the PLA/CS blend matrix. An optimization technique was employed to fine-tune the tribological parameters. Gray theory was used to determine optimal settings at 1 wt% Al₂O₃ and 20 wt% HAp, with a loading of 40 N and a cycle time of 900 s. The microstructural analysis (FESEM images) offers an extensive range of insight into the microstructural attributes and provides a detailed analysis of wear surfaces and shows the uniform dispersion of bifiller and matrix which strengthen the wear property. Incorporating bifiller materials into the matrix improved wear resistance and the coefficient of friction (COF). The modified BRPBNCs enhanced internal fixation and implant component performance under various loading conditions. By providing useful insights to improve tribological performance, it has broad implications outside of tribology, impacting areas of materials science such as biomaterials used as internal fixation devices. This approach is novel due to the selection of a unique material combination, the easy-to-prepare homogeneous BRPBNCs samples, and a hybrid method for selecting parameters that improve tribomechanical performance for potential biomedical applications.

Introduction

Many newer and advanced materials have been developed in recent years, and polymer composites are one of them. Polymeric composite materials are widely used in various industries, including aerospace, automotive, recreational goods, maritime, and biomedical fields. ¹ The high strength-to-weight ratio of polymeric composites allows their use in wear-resistant applications. This can lead to enhanced performance and reduced component failure. ² Over the last three decades, the medical sector has used polymeric materials to manufacture biocompatible, mechanically upgraded, and durable components for biomedical applications. ³ Polymers are used as biomaterial, that may safely and reliably replace a body component or function of the body part. ⁴ The polymeric materials are more comparable to the original tissue materials in terms of mechanical, tribological, and biological properties. Earlier, metallic alloys were used for biomedical and implant production, although it contain some manufacturing limitations, such as cost issues, bio inertness, and biocompatibility concerns ⁵

The use of polymers and their composites in tribological applications has grown in engineering for various technical and economic reasons. Tribological behaviour, which encompasses the study of friction, wear, and lubrication, is crucial for evaluating the performance and longevity of materials, specially in applications with sliding or rotating parts. ⁶ Advantages of polymers over other materials include self-lubrication, light weight, resistance to corrosion and oxidation, non-toxicity, and ease of manufacturing into near-net shapes. However, despite these benefits, many challenges remain for the effective and cost-efficient use of polymers in specific tribological settings. For instance, a slight increase in contact pressure can cause the wear rate to become very high, and low friction does not always mean low wear. It is noted that wear and failure are the main factors that limit the lifespan of an implant. ⁷ Therefore, selecting the right polymer materials for tribological uses is extremely important. The working conditions, nature of the material, lubricants, and environmental factors are some of the important aspects to consider regarding wear. ⁸ Additionally, their tribological performance can be enhanced and optimized through modifications to their bulk composition or surface conditions.

Because polymers are increasingly used in applications where tribological performance is crucial, conducting wear analysis on polymers is a vital part of materials science. Recently, there has been growing interest in biodegradable and biocompatible polymers, such as polylactic acid (PLA) and chitosan (CS).^9,10 Both PLA and chitosan, along with their composites, have been studied for their potential in wear-resistant applications. These materials provide an environmentally friendly alternative to traditional options. Recent investigations of nano particles on tribological behaviour carried out and found nano components are smaller than the surrounding polymer chains, which strengthens the bonding between the particles and the polymer matrix.^11,12 The addition of hydroxyapatite (HAp) and aluminum oxide (Al₂O₃) as fillers in these polymers has shown promising results. HAp is biocompatible and osteoconductive, ¹³ while Al₂O₃ is hard and resistant to wear. ¹⁴ Combining HAp and Al₂O₃ can improve the wear resistance, mechanical properties, and bioactivity of PLA and chitosan composites. These composites are suitable for biomedical applications, such as bone tissue engineering, implants, and wear-resistant coatings. To design and develop wear-resistant materials, optimize component performance, and reduce maintenance costs, it is essential to understand the wear mechanisms and properties of PLA, chitosan, and their composites reinforced with HAp and Al₂O₃.

Hybrid reinforced polymers have also garnered increased attention, including natural with natural, natural with synthetics, and synthetic with synthetic. ¹⁵ Hybrid bio-composites have recently emerged as a practical option for improving material properties. ¹⁶ Hussain et al. studied the UHMWPE hybrid nanocomposite using three types of composites with 2% vitamin C produced via the hot press, with varying weight percentages of Al₂O₃ (0.5%, 1%, and 2%). ¹⁷

Many optimization tools can be used to resolve the problem of finding the best process factors. These include the RSM, the Taguchi methodology, the GRA method, artificial neural networks (ANN) methods, and hybrid methods.^18,19 Understanding and optimizing tribological performance require a systematic approach, and the design of experiments (DOE) serves as a powerful methodology for this purpose.^20,21

Recently, scientists have encountered significant challenges in selecting suitable filler materials to enhance the performance of polymeric composite materials. ²² As a result, there has been considerable interest in researching and developing polymeric composites reinforced with nano-bifillers. To achieve these aims, the objectives outlined in this study are as follows:

I. To promote the HAp and Al₂O₃ ceramic nanoparticles as a bifiller, reinforced with a polymeric blend-based matrix such as PLA/CS blend, and develop BRPBNCs.

II. To investigate the mechanical and tribological characteristics, such as compression test, wear rate, and coefficient of friction under dry sliding wear conditions.

III. To optimize the process parameters using Gray Relation Analysis coupled with Response Surface Method to improve the tribological behavior of developed BRPBNCs.

The objectives of this article will be achieved systematically. First, PLA/CS blend-based HAp and Al₂O₃ bifiller were used to develop BRPBNCs through a solid compression technique, ²³ aiming to meet specific properties. The overall performance of BRPBNCs was then evaluated using various characterization methods, including mechanical and tribological tests, as well as morphological analysis. Next, multi-objective optimization was performed to determine the most influential wear parameters. ²⁴

This article introduces several novel aspects, although prior studies have examined the combination of PLA with CS and HAp for biomedical applications. An investigation is conducted to optimise methods with various tribological parameters to improve Tribo-Mechanical Performance Using Response Surface Methodology-based Grey Relational Analysis, filler and matrix materials ratio to develop BRPBNCs. A uniform dispersion of the bifiller (HAp and Al₂O₃) within the PLA/CS blend-based matrix is achieved using a modified processing technique. Its implications extend beyond tribology, affecting materials science fields like biomaterials by offering valuable insights to enhance tribological performance. As a result of this method, the overall performance of BRPBNCs is presented.

Materials and methods

This piece of work is based on an experimental investigation. This work is investigated in three sections (Selection of materials, development, and characterization). The primary step involves choosing the components that will be utilized to produce bifiller. The following step consists of using particular materials in the production of BRPBNCs. In conclusion, the BRPBNCs that have been developed undergo a series of tests to determine their mechanical and tribological properties, followed by optimization of selected factors and levels.

Materials

In this article, PLA and the CS were used as the matrix materials, and Hap and Al₂O₃ were used as the filler materials and as nano powders. The PLA (CAS NO.: 26100-51-6) was purchased via Merck (Sigma–Aldrich). PLA was in pallet form and was for medical-grade use. The molecular formula C₃H₅O₂ and average pallet size were 3 mm and white in colour. CS (CAS NO.: 9012-76-4) was purchased from Vedayukt India Private Limited of Jharkhand, India. The molecular formula was C₆H₁₁NO₄. The average particle size was 30–50 nm, and its purity was >99%, and in a uniform beige colour. Vedayukt India Private Limited of Jharkhand, India, also delivered the Hydroxyapatite (HAp) and Aluminum Oxide (Al₂O₃) used as filler materials in the article. The average particle size (APS) is between 30 and 50 nm, and the HAp has a CAS Number of 1306-06-5 and a purity level of approximately >99%. Similarly, Al₂O₃ is >99% pure, has an APS of 30–50 nm, and a CAS number of 1344-28-1. Acetone, chloroform, and silicone oil were purchased from a local vendor in Gorakhpur, India. The die used for sample preparation was made of mild steel with the assistance of local facilities. Table 1 provides the details of the material used for BRPBNCs.

Table 1.

Details of composition and nomenclature of developed BRPBNCs ⁵ .

Sl. No.	Composition (wt.%)			Nomenclature	Density (g/cm3)
	Matrix	Filler 1	Filler 2
	PLA/30CS	HAp	Al2O3
1	100	0	0	PLA/30CS	1.204
2	80	20	0	PLA/30CS-20HAp	1.575
3	79	20	1	PLA/30CS-20HAp/1Al2O3	1.599
4	78	20	2	PLA/30CS-20HAp/2Al2O3	1.624
5	77	20	3	PLA/30CS-20HAp/3Al2O3	1.648

Development of modified BRPBNCs

The proposed BRPBNCs were developed in two main steps: material preparation and further processing to produce the final composites having a cylindrical shape. Figure 1 illustrates the fabrication process in detail, and the detailed compositions of the bifiller Al₂O₃ and HAp for developing BRPBNCs are shown in Table 1. In the initial steps, the matrix material PLA pellets were mixed with a liquid chloroform solvent and stirred at 550 r/min and 60°C for 3 hours, while CS nano powder was combined with the liquid solvent, acetic acid, and sonicated for 60 minutes. Further reinforced bifiller materials, Al₂O₃ and HAp nano powders, were mixed in a separate beaker with chloroform as a solvent, and both solutions were sonicated at 60°C for 60 minutes. In order to obtain the final material, all the mixture is moved into a larger beaker as per the requirement of the matrix and reinforcement material shown in Table 1 and is allowed to continuously sonicate the resultant mixture at 60°C for an additionally 90 minutes, to remove almost all possible solvent traces, which should be evaporated from the final developed solution. Further, the resultant mixture was placed in a hot air oven at 80°C to improve solvent extraction. This entire process gives a jelly-type mixture. The uniform filler dispersion suggest the uniform attachment on the base matrix surface which shows a suitable process for preparing the BRPBNCs. In the second step, the developed jelly mixture was poured into a mild steel die and further processed in a hot press for 30 minutes using a hydraulic press machine with a pressure of 100 bars. After this, cylindrical pin-type polymeric nanocomposites were obtained by cooling to room temperature for 24 hours. These samples were developed according to the American Society for Testing and Materials (ASTM F732), ²⁵ with a cylindrical specimen length of 13 mm and a diameter of 9 mm. Silicon oil was applied on the poured surface of the die used as a lubricant throughout the process to easily remove samples from the die. The mentioned development process is designed to efficiently and cheaply fabricate BRPBNCs with desirable qualities.

Figure 1.

Development stages of BRPBNCs wear samples.

Experimentation

A proposed experimental work used in this article was briefly summarized in Figure 2.

Figure 2.

Process workflow of this article.

Compression test

The developed BRPBNCs samples were investigated for the compression test using the universal tensile test (Model No. EZ50, 50 KN). The samples were cut according to the International Organization for Standardization (ISO-604 standard), with sample dimensions of 10 × 10 × 4 mm³. For each composition, three consecutive runs were performed at a crosshead speed of 0.5 mm/min. According to the test standards, the method used in the experiment was precise and accurate.

Tribological (wear and friction test)

A wear test was performed primarily to investigate the tribological behaviour using the pin-on-disk machine (Model: DUCOM, Pin on Disk tribometer, TR-20LE). The disk machine (Stainless-steel disk, SS440 Grade 25) having surface roughness 0.050 µm had a counterweight shaped like a circular disk made of stainless steel grade 316 L material, with a thickness of 15 mm and a diameter of 60 mm. Three different types of loads were applied during the testing process: 20 N, 40 N, and 60 N. To conduct the wear test, the rotational speed was maintained at 200 r/min, and the profile of the wear track was circular in pattern. Other factors, such as filler content (1, 2, and 3 wt% Al₂O₃, keeping 20 wt% HAp constant) and cycle time (300, 600, and 900 s), were controlled to ensure severe wear conditions for biomedical field applications.

All tests were conducted under dry conditions at room temperature, as specified in ASTM G99. ²⁶ A schematic diagram of wear test shown in Figure 3. Before testing, all samples were cleaned with acetone and allowed to dry in the air to prevent any sample weight gain. The weighing machine with least count 0.001 mg was used to determine how much weight each BRPBNCs sample wore out during the wear test before and after the test. Weight loss during the test was recorded for each sample, and it was divided by its respective density to calculate the volume loss of each sample. After that, the wear rate was calculated according to the formula shown in the given equation.

$\mathit{W}\mathit{e}\mathit{a}\mathit{r}\hspace{0.17em}\mathit{R}\mathit{t}\mathit{a}\mathit{e}\hspace{0.17em}\left(\mathit{W}\mathit{r}\right)=\frac{\Delta \mathit{V}}{\mathit{D}*\mathit{L}}$

(1)

Where the ΔV is denoted as the volume loss measured in cubic millimetre (mm³), L is the applied load in the newton (N), and the sliding distance is D in millimetre (mm), to find out the wear rate. During the wear test, the friction force and the coefficient of friction (COF) were also recorded for each sample. A software-attached device was used to record the value of the friction force and COF more accurately. All experiment were performed at ambient temperature, with three repetitions and reported average value. The measured data were further used to figure out the wear results in terms of Wr and COF.

Figure 3.

Schematic diagram of pin-on-disk testing apparatus.

FESEM of BRPBNCs wear samples

FESEM was used after the successful operation of the slide wear test. This was conducted to understand the wear mechanism. All wear-tested samples were examined under a high-resolution scanning electron microscope (Model: NOVA NANOSEM450, Magnification 5X-1000000X, Resolution- 1.0 nm with the accelerating voltage of 30 kV). At a voltage of 15 kV, and using the EDAX software, all the FESEM images were obtained. During the slide wear test, wear scars indicated how the wear happened and how severe the harm was during the slide wear test.

Statistical modelling

Response surface design

From a statistical perspective, the design of experiments (DOE) module is a well-established method used to systematically collect and analyze large amounts of data to evaluate the responses that meet the criteria of interest. ²⁷ Through the use of statistics, it is possible to determine how the input factors of a process affect the output or effect of the process. To assess the wearability of PLA/CS blend base BRPBNCs, the Box–Behnken Design (BBD)-based response surface method (RSM) was utilized for this research article, which minimizes the total number of experiments conducted and obtains quadratic interactions between specified factor pairs. ²⁸ By combining input factors, 15 sets of experimental data were created, with three repetitions. The tribology parameters used for the study include independent variables such as filler loading in weight percent (1, 2, and 3 wt. % of Al₂O₃ and 20 wt. % of HAp with PLA/30CS blend-based matrix), applied load in newton (20, 40, and 60 N), and cycle time in second (300, 600, and 900 s) at three different design levels: low (Level 1), medium (Level 2), and high (Level 3). Additionally, the study included dependent variables such as wear rate and coefficient of friction. The specific wear rate (Wr) in mm3/N m and COF are characteristics described by the dependent responses. Tabular representations of the experimental array and the related data are shown in Tables 2 and 3.

Table 2.

Levels of selected control factors.

Control factor	Level
	Low	Medium	High
Applied load (N)	20	40	60
Cycle time (s)	300	600	900
Bifiller loading (wt.%)	1	2	3

Table 3.

Designed DOE for selected control factors using RSM (Box-Behnken Method).

Exp. No.	Independent input variable
	Applied load	Cycle time	Bifiller loading
	(N)	(s)	(wt %)
1	20	600	1
2	60	600	1
3	40	300	1
4	40	900	1
5	20	300	2
6	60	300	2
7	20	900	2
8	60	900	2
9	40	600	2
10	40	600	2
11	40	600	2
12	20	600	3
13	60	600	3
14	40	300	3
15	40	900	3

Empirical model development and parametric optimization

Based on the developed PLA/CS blend-based BRPBNCs, the RSM method was utilized to evaluate the impact of three independent variables on Wr and COF values. While the responses were the Wr and COF, the independent variables selected were bifiller loading (X3), applied load (X1), and cycle time (X2). A quadratic mathematical equation was developed for the performance of the modified PLA/CS blend BRPBNCs using response surface regression. This equation was based on the independent factors and their interactions with the responses. ²⁹

$\mathit{Y}={\mathit{b}}_{\mathit{o}}+{\displaystyle \sum }_{\mathit{i}=1}^3{\mathit{b}}_{\mathit{i}}{\mathit{X}}_{\mathit{i}}+{\displaystyle \sum }_{\mathit{i}=1}^3{\mathit{b}}_{\mathit{i}\mathit{i}}{\mathit{X}}^2+{\displaystyle \sum }_{\mathit{i}=1}^2{\displaystyle \sum }_{\mathit{j}>1}^3{\mathit{b}}_{\mathit{i}\mathit{j}}{\mathit{X}}_{\mathit{i}}{\mathit{X}}_{\mathit{j}}+\mathit{\epsilon }$

(2)

The response variables of Wr and COF are symbolically represented by the letter Y in this equation. The variable b₀ is a constant, while the term ε represents the residual or error term. In this equation, the linear coefficient is denoted by bi, the quadratic coefficient is denoted by bii, and the interaction coefficient is denoted by bij. Equation (3) was used to transform the independent variables Xi and Xj into dimensionless coded variables. These variables are described below.

${\mathit{X}}_{\mathit{i}}=\frac{{\mathit{x}}_{\mathit{i}}-{\mathit{x}}_{\mathit{o}}}{\Delta {\mathit{x}}_{\mathit{i}}}$

(3)

In this context, the coded values of the independent variables are denoted by the symbol Xi, while the real values are denoted by the symbol xi. On the other hand, x0 represents the actual values of the independent variables at the center point. The symbol Δxi indicates the step change value between the low and high values of xi, representing the difference between the two. As part of this inquiry, a BBD was utilized, consisting of three layers of variables. Based on this design, 15 wear tests were conducted, each with a different sample composition and sliding conditions. This was done to ensure that the quadratic mathematical models for Wr and COF analysis were accurate. All of the experimental data was evaluated using residual plot checks. Utilizing Minitab software, response surface plots and contour plots were generated to maximize the elements contributing to the reduction of wear.

Confidence level

To determine the factors contributing to group differences, this article employed Analysis of Variance (ANOVA) and considered p-values of 0.05 or less as statistically significant. ANOVA helped identify key drivers of variation between groups with a 95% confidence level. By using this statistical method, it is possible to determine whether the observed differences are due to chance or are related to significant causes.

Optimization method

The main aim of this article is to minimize the tribological properties of the developed BRPBNCs. It is quite difficult to determine the minimum Wr and COF under various factors and levels for different loading conditions, cycle times, and developed samples. The gray theory provides a mechanism for obtaining suitable tribological reaction values. The gray approach is a multi-response method commonly employed when dealing with challenges involving multiple tribological reactions. While the Taguchi technique is typically used for optimizing single-response problems, grey relational analysis based on grey system theory can be applied to complex interrelationships among multi-response problems. ³⁰

The procedure considers two zones: the gray zone and the white zone. The gray zone represents a system with incomplete information, while the white zone signifies a system that contains all the necessary information. Gray theory can be applied to establish the relationship between two responses. The following procedures are regarded as part of the gray theory model.

Gray theory

Step 1: To minimize the impact of experimental modifications, the data is normalized by scaling the response outcomes from 0 to 1. Equation (4) illustrates that a “smaller-the-better” method is employed in normalization to ensure that lesser values achieve greater significance. ³¹

${\mathit{x}}_{\mathit{i}}^{*}=\frac{{{\mathit{m}\mathit{a}\mathit{x}\mathit{X}}_{\mathit{i}}\left(\mathit{z}\right)-\mathit{X}}_{\mathit{i}}\left(\mathit{z}\right)}{{\mathit{m}\mathit{a}\mathit{x}\mathit{X}}_{\mathit{i}}\left(\mathit{z}\right)-{\mathit{m}\mathit{i}\mathit{n}\mathit{X}}_{\mathit{i}}\left(\mathit{z}\right)}$

(4)

Normalization for values where “higher” is “better” is performed using equation (5).

${\mathit{x}}_{\mathit{i}}^{*}=\frac{{\mathit{X}}_{\mathit{i}}\left(\mathit{z}\right)-{\mathit{m}\mathit{i}\mathit{n}\mathit{X}}_{\mathit{i}}\left(\mathit{z}\right)}{{\mathit{m}\mathit{a}\mathit{x}\mathit{X}}_{\mathit{i}}\left(\mathit{z}\right)-{\mathit{m}\mathit{i}\mathit{n}\mathit{X}}_{\mathit{i}}\left(\mathit{z}\right)}$

(5)

Where i = 1, …, m; z = 1, …, n; n denotes the total number of experiments, while m signifies the number of responses. The beginning series is denoted by xi(z). The maximum value is indicated by max xi(z), the minimum value is signified by min xi(z), and x represents the target value.

• Step 2: The gray relational coefficient (GRC) is calculated using the following equation (6).

${\mathit{\gamma }}_{\mathit{i}}\left(\mathit{k}\right)=\frac{{\Delta }_{\mathit{min}}+{\mathit{\gamma }*\Delta }_{\mathit{max}}}{{\Delta }_{\mathit{o}\mathit{i}}\left(\mathit{k}\right)+{\mathit{\gamma }*\Delta }_{\mathit{max}}}$

(6)

Where Δ_oi denotes the deviation sequence in equation (7).

${\Delta }_{\mathit{o}\mathit{i}}={\mathbf{l}\mathit{x}}_{\mathit{o}}\left(\mathit{z}\right)-{\mathit{x}}_{\mathit{i}}\left(\mathit{z}\right)\mathbf{l}$

(7)

Where, the reference sequence is x_o(z), and the comparability sequence is xi(z). Δ_min represents the minimal absolute difference among all comparison sequences, while Δ_max signifies the maximal absolute difference. This article set the identification or distinguishing coefficient (γ) to 0.5, which usually lies between 0 and 1. ³²

• Step 3: The gray relational grade (GRG) is determined by calculating the average of the GRC values. Equation (8) can be used to find the GRG values.

${\mathit{G}}_{\mathit{i}}=\frac{1}{\mathit{n}}{\displaystyle \sum }_{\mathit{z}=1}^{\mathit{n}}{\mathit{\gamma }}_{\mathit{i}}\left(\mathit{z}\right)$

(8)

Where, G_i represents the required grade value, and n denotes the number of responses.

• Step 4: ANOVA was employed to assess the impact of process limitations on tribological properties.

Results and discussion

This section presents an in-depth analysis of the results from mechanical and tribological tests, which primarily include the compression and wear rates of BRPBNCs with varying weight percentages of bifiller loading. To evaluate the compression and sliding conditions, tests were performed under various loading and sliding scenarios.

Compression test

To determine the compressive strength of the BRPBNCs material, the stress–strain curves (engineering stress-strain) for the PLA/CS blend-based matrix integrated with HAp and Al₂O₃ composites are shown in Figure 4. A yield strain of 0.2% of the material’s compressive strength (true stress) was considered during the compression test. Figure 5 illustrates the compression strength values obtained for various testing combinations, including Neat PLA, PLA-30CS, PLA/30CS-20HAp, PLA/30CS-20HAp/1Al₂O₃, PLA/30CS-20HAp/2Al₂O₃, and PLA/30CS-20HAp/3Al₂O₃. These combinations exhibit comparable compression strength values of 4.28, 6.12, 8.99, 11.04, 14.27, and 15.70 MPa, respectively.

Figure 4.

Stress versus strain curves of various BRPBNCs.

Figure 5.

Compressive strength of various BRPBNCs.

The results demonstrate that adding the bifiller reinforcement increases the compressive strength. ³³ The stress-to-strain ratio at the yield point determines the compressive modulus of a composite material. ³⁴ These combinations also have comparable compression modulus values of 39.54, 40.95, 55.79, 56.72, 68.73, and 70.27 MPa, respectively in Figure 6. The modulus of the developed samples is higher than that of the neat PLA matrix material.

Figure 6.

Compressive modulus of various BRPBNCs.

In both neat and blend PLA and CS composite materials, increasing the proportion of HAp and Al₂O₃ nanoparticles in the composition results to an increase in the compressive strength as well as the modulus of the material. When incorporated with 20 HAp and 3% aluminium oxide, base materials exhibit an immense rise in compressive modulus of 1.71 times and compressive strength of 2.56 times because of the combination of these two components.

One of the primary factors responsible for the variations in compression strength is that the compressive strength of HAp and Al₂O₃ of these nanoparticles is higher than that of the base blend matrix. ³⁵ The second benefit is that the presence of reinforcing particles can reduce the mobility of the molecular chains that comprise the polymeric matrix, ultimately resulting in an increase in compression strength due to the uniform dispersion of bifiller.^36,37 Consequently, the incorporation of HAp and Al₂O₃ nanoparticles into the polymeric PLA/CS blend enhances the compression properties of the blend.

The mechanism responsible for this is known as the bridging effect. This effect is exhibited by the presence of HAp and Al₂O₃ nanoparticles in the matrix material. It can lead to enhanced mechanical properties, including toughness, strength, and fracture resistance. An increased fracture propagation path is also observed when bifiller materials are added.^38,39 The presence of a bifiller may cause cracks to spread around them, increasing the path length and the amount of energy required for fracture. Improvements in compressive strength and resistance to deformation may arise from the combination of these mechanisms.

Tribological study

This section discusses the findings of the tribological analysis conducted on the developed nanocomposite samples. A total of 15 trials were performed on various samples, each featuring a different bifiller composition. The experimental results of Wr and COF are presented in Table 4, based on the design parameters used. For these experiments, the filler content of Al₂O₃ was considered at 1, 2, and 3 wt%, while the filler amount of HAp was 20 wt%.

Table 4.

Experimental design and measured responses for various operating conditions.

Exp. No.	Independent input variable			Responses
	Applied load	Cycle time	Bifiller loading	Wear rate (Wr)	Coefficient of friction
	(N)	(s)	(Wt%)	(×10−5 mm3/m.N)	(COF)
1	20	600	1	1.38746	0.093
2	60	600	1	0.63899	0.039
3	40	300	1	1.16614	0.047
4	40	900	1	0.71277	0.036
5	20	300	2	1.97672	0.060
6	60	300	2	1.00657	0.040
7	20	900	2	1.29720	0.046
8	60	900	2	0.54830	0.051
9	40	600	2	0.86391	0.060
10	40	600	2	0.86391	0.060
11	40	600	2	0.86391	0.060
12	20	600	3	1.54446	0.061
13	60	600	3	0.68607	0.083
14	40	300	3	1.32973	0.040
15	40	900	3	0.75765	0.065

Based on the findings from the empirical model and regression analysis conducted on the tribological properties of BRPBNCs composites, it has been observed that the residuals of the model for the BRPBNCs wear rate follow a normal distribution. Both the linearity evident in the normal probability plot (Figures 7 and 8(a)) and the even distribution of the data in comparison to the fits (Figures 7 and 8(b)) support this conclusion. The residuals show a normal dispersion, showing a linear relationship among the variables and the response variable. Residuals show randomly in all other plots, ensuring fair comparison between experimental and fitted values. ⁴⁰

Figure 7.

Residual graphs for wear rate (a) normal probability plot, (b) versus fits.

Figure 8.

Residual graphs for COF (a) normal probability plot, (b) versus fits.

Given these findings, it is clear that the model is realistic and has the potential to predict the wear rate of BRPBNCs and their composites with a high degree of precision. Consequently, the presence of a normal residual distribution in the investigation’s results indicates that both the empirical model and the regression analysis yield correct results.

Overall, the findings of our study indicate that BRPBNC composites have the potential to be materials that exhibit favourable tribological performance. The wear rate and COF both had p-values of <0.0001 and 0.0014, respectively. Since both p-values were lower than 0.05, conclude that the model constructed for the tribological investigation performed satisfactorily.

Both the wear and friction models had R² values exceeding 95%, signifying their relevance. To create a model for the Wr and COF of BRPBNCs composites, equations (9) and (10) were employed. These equations utilize coded units for filler content (wt%), load (L), and cycle time (TP).

$\mathbf{Wr}=3.468-0.6517\hspace{0.17em}\mathbf{Load}-0.002876\hspace{0.17em}\mathbf{Cycle}\hspace{0.17em}\mathbf{Time}+0.1965\hspace{0.17em}\mathbf{W}\mathbf{t}\hspace{0.17em}\%+0.000520\mathbf{L}\mathbf{o}\mathbf{a}\mathbf{d}*\text{Load}+0.000002\hspace{0.17em}\text{Cycle}\hspace{0.17em}\text{Time}*\text{Cycle}\hspace{0.17em}\mathbf{Time}-0.0076\hspace{0.17em}\mathbf{W}\mathbf{t}\hspace{0.17em}\%*\mathbf{W}\mathbf{t}\hspace{0.17em}\%+0.000009\hspace{0.17em}\mathbf{Load}*\text{Cycle}\hspace{0.17em}\mathbf{Time}-0.001374\hspace{0.17em}\mathbf{Load}*\mathbf{W}\mathbf{t}\hspace{0.17em}\%-0.000099\hspace{0.17em}\text{Cycle}\hspace{0.17em}\text{Time}*\mathbf{W}\mathbf{t}\hspace{0.17em}\%$

(9)

$\text{COF}=0.1680-0.003944\hspace{0.17em}\text{Load}+0.000121\hspace{0.17em}\text{Cycle}\hspace{0.17em}\text{Time}-0.0652\hspace{0.17em}\mathbf{W}\mathbf{t}\hspace{0.17em}\%+0.000014\hspace{0.17em}\text{Load}*\text{Load}-0.000000\hspace{0.17em}\text{Cycle}\hspace{0.17em}\text{Time}*\text{Cycle}\hspace{0.17em}\text{Time}+0.00337\hspace{0.17em}\mathbf{W}\mathbf{t}\hspace{0.17em}\%*\mathbf{W}\mathbf{t}\hspace{0.17em}\%+0.000001\hspace{0.17em}\text{Load}*\text{Cycle}\hspace{0.17em}\text{Time}+0.000950\hspace{0.17em}\text{Load}*\mathbf{W}\mathbf{t}\hspace{0.17em}\%+0.000030\hspace{0.17em}\text{Cycle}\hspace{0.17em}\text{Time}*\mathbf{W}\mathbf{t}\hspace{0.17em}\%$

(10)

Model summary statistics and p-values presented in Table 5 as part of the supporting information illustrate the impact of each input variable on the wear rate models at a statistical level. A smaller p-value indicate a stronger connection between the regression coefficient and the input variable. The most significant factor is the linear applied load, followed by the cycle time, quadratic applied load, quadratic cycle time, filler content, and the interaction between applied load and cycle time. The linear applied load remains the most important factor. The interaction between filler content and cycle time, as well as the interaction between applied load and filler content, and quadratic filler content, are less significant than the others.

Table 5.

Model summary statistics of Wear Rate.

Source	Std. Dev	R-Squared	Adj. R-Squared	Pred. R-Squared
Linear	0.1472	0.8930	0.8638	0.8137
2FI	0.1657	0.9014	0.8275	0.6552
Quadratic	0.0251	0.9986	0.9960	0.9773	Suggested
Cubic	0.0000	1.0000	1.0000		Aliased

The statistics summary model for the coefficient of friction table shown in Table 6 indicates that the input variable on the wear rate models at a statistical level.

Table 6.

Model summary statistics of COF.

Source	Std. Dev	R-Squared	Adj. R-Squared	Pred. R-Squared
Linear	0.0172	0.1186	−0.1218	−0.8751
2FI	0.0128	0.6422	0.3738	−0.8527
Quadratic	0.0041	0.9769	0.9354	0.6310	Suggested
Cubic	0.0000	1.0000	1.0000		Aliased

The coefficient of friction model suggests the interaction between load and filler content has the most significant effect on the predicted regression coefficient of COF. This information serves as supporting evidence. Next, the quadratic cycle time follows, along with the interaction between cycle time and filler content, applied linear load, the interaction between load and cycle time, and then linear filler content. The quadratic filler content and linear cycle time are less relevant than the other factors.

Effect of control parameters on wear rate

This section examines the impact of various control factors, such as bifiller loading content, applied load, and cycle time, on the Wr and the COF of BRPBNCs, illustrating how these factors relate to responses and influence one another based on the provided information.

For developed BRPBNCs composites, Figure 9(a)–(c) and Figure 10(a)–(c) display three-dimensional response surface plots and contour plots that indicate the combined effect of filler loading content, applied load, and cycle time as control factors on wear rate (Wr). Wr of BRPBNCs decreased as seen in Figure 9(a) when both the bifiller loading and applied loads were increased. Wear rate is more affected by applied load than by filler loading.

Figure 9.

Surface plot illustrating (a) applied load vs filler loading content for Wr, (b) cycle time vs applied load for Wr, (c) cycle time vs filler loading content for Wr, (d) applied load vs filler loading content for COF, (e) cycle time vs applied load for COF, (f) cycle time vs filler loading content for COF.

Figure 10.

Contour plot illustrating (a) applied load vs filler loading content for Wr, (b) cycle time vs applied load for Wr, (c) cycle time vs filler loading content for Wr, (d) applied load vs filler loading content for COF, (e) cycle time vs applied load for COF, (f) cycle time vs filler loading content for COF.

As the applied load increases, the wear rate drops dramatically, as seen in Figure 9(b). As the cycle time increased, the wear rate decreased and at 600 seconds, it was at its lowest. The wear rate of BRPBNCs composites decreased and after some value it increases slowly with increasing cycle time under high loads. The contour plot showed that a cycle duration of 500 to 700 seconds and a load of 20 to 60 N resulted in the least amount of wear.

The graph of bifiller loading content over cycle time is easily seen in Figure 9(c). The wear rate of the BRPBNCs composites was seen to be affected by both the filler loading and the cycle time. For a cycle period of 600 seconds, the wear rate was determined to be lowest with a bifiller loading of 3 wt% Al₂O₃ and 20 wt% HAp. The findings show that the wear rate of BRPBNCs with bifiller decreases with increasing applied load levels. This may be due to the accumulation of tiny fragments of wear debris from the matrix materials, which are softer polymeric materials, between the other counterparts of filler materials. Similar findings have been reported in other articles. ⁴¹

When the sample surfaces touch the hard edges of the counter face, they lose material as they slide and throw it away as wear debris. A transfer film forms on the other side after repeated motions. This new type of transfer film prevents the combined surface from wearing down further while in use. Therefore, these composites support implants that last a long time. As the load increases, there is more surface contact between the samples and the counter face, which accelerates the process. This means that as the load on the samples rises, the wear rate decreases.

The composition, chemical bonding, and properties of the composites also play a significant role in making the formation of transfer films during the sliding process dependent on these factors. The shape and characteristics of the transfer layers are influenced by the various types of materials that are used. When two surfaces rub against one another, it has been established that the Al₂O₃ nanoparticles act as a lubricant and create a smooth transfer layer between the surfaces. Consequently, HAp/Al₂O₃ filled BRPBNC is smoother and experiences less wear than the empty PLA/CS blend-based matrix, which results in BRPBNC nanocomposites having a longer lifespan. The incorporation of ceramic bifiller into the PLA/CS blend-based matrix has been shown to produce comparable outcomes, as reported by in previous studies. ⁴² As a result, it has been demonstrated that integrating HAp and Al₂O₃ nanoparticles into PLA/CS blend-based matrix materials leads to increased durability.

Effect of control parameters on COF

Response surface and contour plots in Figures 9(d)–(f) and 10(d)–(f) indicate which control parameters affect the COF of BRPBNCs composite.

This interaction between filler loading content and applied load is illustrated in Figure 9(d), demonstrating that the average coefficient of friction decreases from 0.093 to 0.036 as the filler content increases.

Figure 9(e) presents load-cycle time graphs showing that COF decreases with applied load for both shorter and longer cycle lengths. The formation of debris and the transfer of polymer film from the working surface to the counterface may account for this pattern. The lubricating coatings formed from the interaction between the sample and counterface exhibit poorer adhesion; consequently, adhesive wear may be more significant at medium cycle lengths. This could reduce the contacting surface roughness at higher weights and during 700 to 900 cycle durations. Figure 9(f) shows filler loading-cycle time graphs, which reveal similar patterns, with COF declining as filler loading increases for both shorter and longer cycle durations. It can be observed that the average COF value decreases when Al₂O₃ nanoparticles are combined with other matrix materials to reduce the amount of wear occurring between the initial face and the sample. The layered structure of the bifiller, which promotes lubricating action and consequently reduces the coefficient of friction between the contact surfaces, may contribute to this phenomenon. Additionally, the presence of ceramic particles in a well-dispersed phase aids in distributing the stress caused by the friction force. This occurs by facilitating the effective transfer of stress from the soft polymer matrix to the hard reinforcement, ultimately resulting in the developed nanocomposite exhibiting good wear resistance characteristics.

To achieve the greatest possible reduction in COF, Al₂O₃ filler content ranging from 1 to 2 wt% and HAp at 5 wt% were utilized. When both the filler and the applied loads were increased, the COF rose due to the heightened friction occurring between the composite contact surface and the sliding counter face. The enhanced agglomeration of the bifiller may be the reason for ineffective force transmission from the soft polymer to the rigid nano-bifiller. ⁴³ With a filler loading of 1 to 2 wt% Al₂O₃ and an applied load of 40 to 60 N, it was discovered that the average coefficient of friction was at its lowest.

Comparison of experimental and predicted results

Figure 11(a)–(b) compare the experimental Wr and COF to the expected values. The model demonstrates satisfactory adequacy by aligning empirical data with a small margin of error.

Figure 11.

Actual versus predicted value plots for (a) Wr, (b) COF.

Graymodule-based optimal parametric condition

In order to achieve the most efficient performance of developed BRPBNCs, the purpose of this study was to identify the optimal combination of components that would produce maximum results. The gray relational grade, often known as GRG, is a method that was developed to combine a number of different performance measurements into a single output index. Within the scope of the present inquiry, the utilization of the GRG has resulted in the optimization of the condition. For each performance metric, the GRG value was computed independently, with the smaller-the-better characteristic being applied to both Wr and COF. This was done in accordance with equation (4). This is accomplished by applying equations (6) and (7) to each response in order to determine the deviation sequence.

The GRG was determined using equation (8). To calculate the GRG, the distinguishing coefficient weight was considered, which has a value of 0.50. From Table 7, it is noted that experiment no. Four achieved the highest GRG value of 0.906, demonstrating its maximum performance among the completed experiments corresponding to the experimental array of 1 wt% Al₂O₃, 20 wt% HAp, 900 Cycle time, and an applied load of 40 N. Overall, the use of the GRG method helped identify the optimal combination of factors to achieve the best performance from PLA/CS blend-based bio-nanocomposite materials.

Table 7.

Calculated normalized, GRC, GRG and rank.

Exp. No	Independent input variable			Responses		Normalization		Gray relation coefficient		GRG	Rank
	Applied load	Cycle time	Wt%	Wr	COF	Wr	COF	Wr	COF
1	20	600	1	1.387	0.093	0.413	0.000	0.460	0.333	0.397	15
2	60	600	1	0.639	0.039	0.937	0.947	0.887	0.905	0.896	2
3	40	300	1	1.166	0.047	0.567	0.807	0.536	0.722	0.629	7
4	40	900	1	0.713	0.036	0.885	1.000	0.813	1.000	0.906	1
5	20	300	2	1.977	0.060	0.000	0.579	0.333	0.543	0.438	14
6	60	300	2	1.007	0.040	0.679	0.930	0.609	0.877	0.743	4
7	20	900	2	1.297	0.046	0.476	0.825	0.488	0.740	0.614	11
8	60	900	2	0.548	0.051	1.000	0.737	1.000	0.655	0.828	3
9	40	600	2	0.864	0.060	0.779	0.579	0.694	0.543	0.618	8
10	40	600	2	0.864	0.060	0.779	0.579	0.694	0.543	0.618	8
11	40	600	2	0.864	0.060	0.779	0.579	0.694	0.543	0.618	8
12	20	600	3	1.544	0.061	0.303	0.561	0.418	0.533	0.475	13
13	60	600	3	0.686	0.083	0.904	0.175	0.838	0.377	0.608	12
14	40	300	3	1.330	0.040	0.453	0.930	0.478	0.877	0.677	5
15	40	900	3	0.758	0.065	0.853	0.491	0.773	0.496	0.634	6

To investigate the influence that tribological elements like load, weight percent of fillers, and cycle time have on GRG, the analysis of variance (ANOVA) is utilized. It has been demonstrated that the level of influence exerted by the experiment no. 4 has the greatest impact on the value of the objective function.

The method of residual analysis, often referred to as RA, is commonly used to determine the efficiency of created models. In Figure 12(a)–(d), the various residual plots of GRG are displayed. These plots are analysed to evaluate the effectiveness of the optimization approach. The residual plots were examined to determine how accurately the model was expected to perform. The variations between the values of the response that were observed and those that were predicted are represented by residuals.

Figure 12.

GRG graphs (a) normal probability plot, (b) versus fits, (c) histogram, (d) pareto chart.

Normal probability plots of normalized residuals reveal a normal distribution in Figure 12(a). A straight residual normal plot indicates good conformance. The model’s reliability and validity were confirmed by the GRA module’s symmetric histogram of residuals’ fitted values (Figure 12(b)). The absence of shifts in the standardized residuals indicates that the GRA technique was well-designed. Figure 12(c) shows that the residuals are randomly distributed around zero in the residuals versus fitted value plot, supporting the constant variance assumption. Figure 12(d) presents the residual plot by observation order. Since the residuals are random, the model is effective.

This paper gets GRA regression equations that match the statistical model equation (11) and model summary in Table 8. With R2 values of 99.45%, the regression model is very fit. According to the gray theory, the best conditions are 40 N load, 1% weight, and 900 cycle time.

$\text{GRG}=-0.2500+0.02668\hspace{0.17em}\text{Load}-0.000143\hspace{0.17em}\text{Cycle}\hspace{0.17em}\text{Time}+0.2259\hspace{0.17em}\text{wt}\hspace{0.17em}\%-0.000100\hspace{0.17em}\text{Load}*\text{Load}+0.000001\hspace{0.17em}\text{Cycle}\hspace{0.17em}\text{Time}*\text{Cycle}\hspace{0.17em}\text{Time}+0.01586\hspace{0.17em}\text{wt}\hspace{0.17em}\%*\text{wt}\hspace{0.17em}\%-0.000004\hspace{0.17em}\text{Load}*\text{Cycle}\hspace{0.17em}\text{Time}-0.004584\hspace{0.17em}\text{Load}*\text{wt}\hspace{0.17em}\%-0.000267\hspace{0.17em}\text{Cycle}\hspace{0.17em}\text{Time}*\text{wt}\hspace{0.17em}\%$

(11)

Table 8.

Model summary statistics of GRG.

Source	Std. Dev	R-Squared	Adj. R-Squared	Pred. R-Squared
Linear	0.0924	0.7004	0.6187	0.3661
2FI	0.0638	0.8961	0.8182	0.4793
Quadratic	0.0186	0.9945	0.9846	0.9117	Suggested
Cubic	0.0000	1.0000	1.0000		Aliased

The most important value of the GRG is shown on Figure 13 as a graph. It shows a possible experiment for finding the best response value. As it observe from the green dotted circle, Experiment No. 4 has the best GRG value (0.828). This means that 1 wt% of filler, 40 N of load, and 900 seconds of cycle time are the best choices for the study.

Figure 13.

GRG values of conducted experiments.

Microscopic evaluation of wear specimens

The FESEM microscopic study of the wear surface under optimal conditions is shown in Figure 14. Figure 14(a) and (b) illustrate the least and most worn samples, respectively. During the experimental trial No. Four FESEM microscope inspection of the wear surfaces of several bifiller PLA/CS samples revealed that the sample containing 1 wt% Al₂O₃ and 20 wt% HAp fillers exhibited the least wear. This was a significant finding. A substantial improvement in load transfer between the matrix and the fillers, Al₂O₃ and HAp, is achieved with the combination of these fillers at their respective weight percentages. This occurs due to their large surface area; bifiller are materials that can be utilized for load transduction.

Figure 14.

FESEM images of wear-tested samples (a) Least wear, (b) Worst wear.

HAp and Al₂O₃ possess sufficient properties that make them prime candidates for tribology applications in polymer nanocomposites. Al₂O₃ nanoparticles can protect the surface of the polymer from abrasive wear and may also function as solid lubricants or assist in forming a tribofilm, which reduces friction. ⁴⁴ HAp nanoparticles can withstand loads while minimizing stress on the polymer matrix, resulting in enhanced wear resistance, and they also serve as solid lubricants, further reducing friction and wear. Figure 15 shows the EDAX graph of tested wear sample.

Figure 15.

EDAX images of wear samples (a) Least wear, (b) worst wear.

Polymer creates a tenacious transfer protective barrier on metal surfaces by sliding over harder materials when they make contact and groves are formed due to removal of blend matrix materials. ⁴⁵ Bifiller interlayers improve this functionality by forming a tribo layer that shields the polymer from the rougher parts of the opposing surface. As a result, this reduces adhesive wear. ⁴⁶

The primary mechanisms of friction are plowing and adhesive forces. ⁴⁷ These forces can simultaneously influence frictional behaviour. The relative significance of each force depends on the materials, surface properties, and contact conditions. Dispersion and interface quality play important roles in this process. If nanoparticles are uniformly dispersed throughout the material, adhesive forces would decrease plowing forces and reduce wear. ⁴⁸

The friction mechanism suggests that incorporating 20 wt% HAp and 1 wt% Al₂O₃ into the PLA/CS matrix blend enhances load transfer, establishes a protective tribolayer, and increases wear resistance while minimizing adhesive wear in polymer composite materials.

This study demonstrates the potential of incorporating fillers with specific weight percentages to enhance the wear performance of PLA/CS matrix blend composites and clearly indicates that adding a bifiller reduces wear and tear by significantly increasing the surface resistance of the nanocomposite. ⁴⁹ The incorporation of Al₂O₃ into the polymer matrix leads to a notable increase in microfibers, forming a network of tie molecules. This network significantly raises the energy required to initiate and propagate the failure or removal of deteriorated solids in the contact zones between surfaces, thus considerably decreasing the wear rates of nanocomposites. Furthermore, the addition of HAp greatly enhances the wear resistance of BRPBNCs. It protects the soft polymer from frictional indentations and reduces the stress exerted by the counterface, also known as the load-carrying capacity, within the nanocomposite. Overall, the incorporation of ceramic Nano fillers improves the mechanical and tribological properties of BRPBNCs, making them suitable materials for biomedical applications.

Conclusion

Both compression and tribological testing were conducted on innovative nanocomposites modified with bifiller components. This work presents the results of these tests. Control factors, including bifiller loading, applied load, and cycle time, were also evaluated for their impact on the Wr and COF of modified PLA/CS blend based polymeric bio-composites. The findings indicate that the composite exhibits superior compressive strength compared to its neat and blended counterparts. This inquiry revealed that variations in these control parameters significantly affect both COF and Wr. When selecting BRPBNCs for applications that require high wear resistance and low COF, it is essential to carefully consider and manage these attributes. Maintaining control over these properties is crucial. Overall, the findings enhance the understanding of bifiller reinforced HAp and Al₂O₃ polymeric PLA/CS blend bio-nanocomposites and their beneficial tribological characteristics. Based on the results obtained, the following conclusions can be drawn.

• According to the findings, the mechanical properties specially the compression test of PLA/CS blend based BRPBNCs that contained bifiller as 20 wt% HAPs and 3 Al₂O₃ were superior to those of neat PLA and PLA/CS blend. Additionally, the combined developed material had a compression strength of approximately 15.70 MPa, which was 2.56 times higher than that of the neat PLA and PLA/CS blend. Consequently, mechanical performance has improved significantly.

• The models demonstrated that bifiller loading, applied load, and cycle time significantly impacted the Wr and COF of the BRPBNCs. These models illustrate how these controlling elements affect performance results. Factors such as applied load, cycle time, and then the bifiller content were used to rank the variables affecting the Wr. Similarly, factors applied load, bifiller content and then the cycle time were used to rank the variables affecting the COF. In the range examined for BRPBNCs, it was found that the optimal sliding conditions for reducing wear and the coefficient of friction involved a nano-bifiller loading of 1 wt% Al₂O₃ and 20 wt% HAp, with an applied load of 40 N and a cycle period of 900 seconds.

• FESEM microscope was used to inspect the worn surfaces of BRPBNCs, and the results showed that the surface of the developed composites containing bifiller nanoparticles was smoother than that of the composites without bifiller nanoparticles.

• The findings demonstrated that incorporating a hard ceramic bifiller into the BRPBNCs composite material resulted in significantly improved lubrication, leading to better stress distribution and reduced wear and friction under lubricated conditions.

According to these findings, using a bifiller hard ceramic can be beneficial in producing hybrid materials that can endure longer and function more effectively in biological applications. Adding Al₂O₃ and HAPs bifiller to the PLA/CS blend-based matrix may enhance its mechanical and tribological properties, making it more suitable for use in biomedical applications that involve both external and internal devices.

This research has the potential to suggest that the various bifiller nanomaterials and integration methodologies each present their own unique set of challenges and the incorporation of nanomaterials as a filler may enhance the properties of developed BRPBNCs with PLA/CS blend matrix materials.