Adsorption Isotherms of PP, PVC, PA6, LDPE, and HDPE Microplastic Particles, and Their Blend on a Hydrophobic Bio-Substrate at Three Temperatures and Two Environments

Abstract

Micro- and nano-plastic pollution caused by the mismanagement of plastics waste is a significant problem worldwide, causing severe impacts in aquatic and terrestrial environments. The purpose of this study was to evaluate the adsorption capacity of a thermally stable and superhydrophobic bio-substrate to remove microplastic particles (MPPs) from aqueous systems. In this work, the adsorption efficiency of cattail fluff towards MPPs from pristine PP, PVC, PA6, LDPE, HDPE, and their blend was evaluated. The effect of temperature (30 °C, 40 °C, and 50 °C) and two binding environments (distilled water and industrial wastewater) on adsorption was determined. Non-linear regressions of seven adsorption isotherm models including Langmuir, Freundlich, Temkin, Dubinin–Radushkevich (D–R), Redlich–Peterson (R–P), Toth, and Sips were applied to fit the experimental data. Error function analysis confirmed that the D–R adsorption isotherm model offers the best fit of the experimental data. The results show that the bio-substrate is very effective in adsorbing MPPs from aqueous systems with adsorption capacities of q_e = 3597 mg/g and q_e = 2807 mg/g in distilled water and synthetic industrial water, respectively. The composition of the MPPs determines the effect of temperature and binding environment on the adsorption performance of the bio-substrate. Physisorption dynamics for the MPP/bio-substrate system are also provided and discussed. Overall, the hydrophobic bio-substrate is highly effective in removing MPPs from aqueous systems, with the added advantages of low cost, sustainability, and scalability for practical applications.

1. Introduction

Micro- and nano-plastic pollution caused by the mismanagement of plastic waste is a significant problem worldwide. The severity of this pollution is that under environmental conditions (e.g., UV radiation, temperature, humidity, physical abrasion, etc.) the weathering of micro- and nano-plastics induces the leaching of dissolved organic compounds (DOCs) (e.g., a mixture of complex organic substances including a range of plastic monomers, oligomers, bisphenol A, phenol derivatives, oxidized organic components of different molecular weights, flame retardants, phthalates, etc.) in aquatic and terrestrial systems, causing important environmental impacts [1,2,3,4,5,6,7,8]. As reported in the literature [1], only 14 days of weathering of polyvinylchloride (PVC) and polystyrene (PS) microparticles (e.g., particle sizes < 5 mm) under simulated environmental conditions was enough to trigger the release of dissolved organic compounds, DOCs, from microplastic particles (MPPs) at concentrations ranging from 0.12 to 3.3 mg of DOC/g of MPPs, which is alarming. Therefore, the ensuing problem of micro- and nanoplastic ingestion by aquatic and terrestrial organisms through the food chain is a threat to aquatic and terrestrial ecosystems [2,4,6,9,10,11,12,13,14,15]. Furthermore, it has been demonstrated that micro- and nanoplastics, which display large surface areas and hydrophobicity, function as carriers of other toxic pollutants via adsorption on their surface through different interactions (e.g., hydrophobic, van der Waals, π–π interactions, etc.), increasing the environmental pollution problem [2,5,6,7,8,9,10,11,12,13,14,15,16]. For instance, previous research confirmed that the adsorption of organic compounds onto a microplastic surface is double the adsorption of organic compounds onto natural sediments and soils [3].

Micro- and nano-plastic pollution caused by the mismanagement of plastic waste is a significant problem worldwide. The severity of this pollution is that under environmental conditions (e.g., UV radiation, temperature, humidity, physical abrasion, etc.) the weathering of micro- and nano-plastics induces the leaching of dissolved organic compounds (DOCs) (e.g., a mixture of complex organic substances including a range of plastic monomers, oligomers, bisphenol A, phenol derivatives, oxidized organic components of different molecular weights, flame retardants, phthalates, etc.) in aquatic and terrestrial systems, causing important environmental impacts [1,2,3,4,5,6,7,8]. As reported in the literature [1], only 14 days of weathering of polyvinylchloride (PVC) and polystyrene (PS) microparticles (e.g., particle sizes < 5 mm) under simulated environmental conditions was enough to trigger the release of dissolved organic compounds, DOCs, from microplastic particles (MPPs) at concentrations ranging from 0.12 to 3.3 mg of DOC/g of MPPs, which is alarming. Therefore, the ensuing problem of micro- and nanoplastic ingestion by aquatic and terrestrial organisms through the food chain is a threat to aquatic and terrestrial ecosystems [2,4,6,9,10,11,12,13,14,15]. Furthermore, it has been demonstrated that micro- and nanoplastics, which display large surface areas and hydrophobicity, function as carriers of other toxic pollutants via adsorption on their surface through different interactions (e.g., hydrophobic, van der Waals, π–π interactions, etc.), increasing the environmental pollution problem [2,5,6,7,8,9,10,11,12,13,14,15,16]. For instance, previous research confirmed that the adsorption of organic compounds onto a microplastic surface is double the adsorption of organic compounds onto natural sediments and soils [3].

Hence, it is paramount to remove micro- and nanoplastics from the environment, especially from water bodies and/or water sources, to control the spread of this contamination [9]. Common techniques for the separation of micro- and nanoplastics from the environment include visual inspection and manual sorting, density separation, flotation, screening, filtration, and adsorption [2,9,17]. From these separation techniques, the adsorption process has drawn interest in the field of wastewater treatment operations for the removal of micro- and nano-plastics because it is a cost-effective, flexible, easy, and environmentally friendly process [9,15,18,19,20,21,22,23,24,25,26].

Adsorption dynamics are determined by molecular interactions between the adsorbate (e.g., solute) and the adsorbent. Therefore, the type of molecular interaction dictates if physical adsorption (e.g., van der Waals forces, hydrophobic interactions, London forces, dipole–dipole forces, etc.), chemical adsorption (e.g., covalent bonds), or ionic exchange (e.g., electrostatic interaction) takes place [18,19,20,22,25]. The efficiency of the adsorption process is affected by several variables including adsorbent type, the availability of active sites on the adsorbent surface, the formation of mono or multilayer adsorption layers, the enthalpy of adsorption, the adsorption Gibb’s free energy, the adsorbate composition, interactions between adsorbate molecules, interactions between adsorbate and adsorbent molecules, the binding environment, and the temperature of the system, among others [27]. The adsorption process is generally exothermic; thus, increasing the temperature of the system normally decreases the adsorption efficiency [27]. Therefore, it is important to evaluate the effect of temperature and binding environment on the adsorption efficiency of the adsorbents.

Experimental adsorption data is commonly fitted through adsorption isotherm models to gain more insights on the adsorbate–adsorbent relationship at equilibrium. This is important because a complete understanding of the dynamics of the adsorption process via adsorption isotherm models allows the establishment of key information such as adsorption mechanisms, maximum adsorption capacities, and adsorbate–adsorbent affinities under different adsorption conditions [18,20,22,24]. This information is also crucial for the design and optimization of adsorption processes and for equipment for practical applications [21,22,25,26,28]. Some of the isotherm models commonly used in water treatment processes include Langmuir, Freundlich, Temkin, Dubinin–Radushkevich, Hill, Sips, Toth, Kahn, Koble–Corrigan, and Redlich–Peterson, among others [18,19,21,22,24,25].

According to the literature [19,20], suitable adsorbents must fulfill the following criteria: they must be derived from wide sources, non-toxic, bio-compatible, bio-degradable, renewable, low cost, and available; they must allow green synthesis modifications, be chemically and thermally stable, have uniform particle size, have high mechanical strength, have a high adsorption capacity, have high selectivity, and allow for easy regeneration. Biobased and natural substrates fulfill several of the requirements for high-quality adsorbents, especially low-cost ones. Furthermore, natural sorbents and bio-substrates exhibit heterogeneous adsorption distribution sites [25]. Common natural and bio-substrates used as adsorbents include minerals (e.g., clays, hydrotalcites, bentonite, etc.), agricultural products (e.g., agricultural waste), seaweeds, yeasts, mosses, bacteria, fungi, lichen, algal waste, spider silk, cellulose, chitosan, biochar, and biopolymers, among others [2,20,25]. Especially for micro- and nano-plastics, previous work showed that three-dimensional adsorbent structures, such superhydrophobic sponges (e.g., contact angle > 150°) or magnetic sponges produced from natural materials, are effective in removing microplastics from wastewater [2].

This work focuses on the evaluation of a hydrophobic bio-substrate’s efficiency in removing microplastics from water. The bio-substrate evaluated was cattail fluff from Typha latifolia, which is an aquatic “herbaceous, rhizomatous, and perennial [plant] with long sword-like leaves from the bare stalk which terminates in a cylindrical inflorescence of female flowers immediately below the male flowers, erupting as fluff when matured” [29]. T. latifolia is found in wetlands worldwide [30,31,32] and in every province and territory in Canada [33]. The flowers (also called fruits) contain achene and perigone hairs (e.g., cattail fluff) that retain most of the seeds rigidly attached to their fibers, while a small proportion of seeds fall or remain in between the fiber clusters [34]. Cattail fluff has been used in several applications such as in wound dressing to adsorb the ichor [29,35], the treatment of burns and ulcers [35], raw material to produce paper, stuffing for mattresses and toys, bay beds, board linings, lifejackets [33,34,35,36], pillow cores [37,38,39], baskets, mats, fans, rope [33,35], textile yarns [40], tinder and insulation [33,39,41], torches dipped into coal oil or kerosene [34,35,41], and the adsorption of oil [42].

The main goal of this study was to determine the efficiency of cattail fluff for the removal of microplastic particles from wastewater. Therefore, the following specific objectives were established: (1) We aimed to perform experimental batch adsorptions tests on five different pristine microplastics of irregular shapes: polypropylene (PP), polyvinyl chloride (PVC), polyamide 6 (PA6), low-density polyethylene (LDPE), high-density polyethylene (HDPE), and their combination (e.g., equal mass). The batch adsorption experiments were conducted at three temperatures (30 °C, 40 °C, and 50 °C) and in two binding environments (distilled water type 2 and an industrial wastewater). (2) We also performed non-linear regression fitting of the experimental adsorption data to seven adsorption models including Langmuir, Freundlich, Temkin, Dubinin–Radushkevich (D–R), Redlich–Peterson (R–P), Toth, and Sips to establish the relationships between the adsorbent and adsorbate under equilibrium conditions. (3) We established the best adsorption isotherm model via error analysis including the coefficient of determination (R²), adjusted coefficient of determination (Adj R²), root mean square deviation error (RMSD), mean square residual (MSE), and non-linear chi-square test (X²). (4) Finally, we determined the effect of the type of microplastic material, temperature, and binding environment on the microplastic adsorption efficiency of cattail fluff.

The main findings of this study indicate that the hydrophobic bio-substrate (cattail fluff) is very effective in adsorbing MPPs of different pristine plastic materials (e.g., PP, PVC, PA6, LDPE, HDPE, and their blend) from aqueous systems at different adsorption temperatures and in different binding environments. The potential applicability of the bio-substrate at a larger scale in real wastewater systems is currently being evaluated by our research team. The ongoing project aims to evaluate the effectiveness of the bio-substrate in removing MPPs from surface water drainage (e.g., rainwater runoff from roofs, driveways, and land) before it enters a lake in a rural area in New Brunswick, Canada.

2. Materials and Methods

2.1. Bio-Substrate: Cattail Fluff

Mature cattail flowers (Typha latifolia) were collected from wild wetlands around the city of Fredericton, New Brunswick, Canada. The cattail fluff was manually separated from the flowers and placed on a flat surface covered with a fine mesh to let it dry under ambient conditions. The dried cattail fluff was used as the bio-substrate without any chemical modification. The contact angle of cattail fluff was characterized using type 2 deionized water as the liquid phase, employing a Goniometer, model G16-2, manufactured by Wet Scientific (Beaumont, TX, USA). The cattail fluff was subjected to thermogravimetric analysis (TGA) using a TA Instrument Model SDT Q600 manufactured by Waters^TM (New Castle, DE, USA). The thermogravimetric analysis was conducted under a nitrogen atmosphere at a flow rate of 50 mL/min and using a heating ramp of 10 °C/min. The bio-substrate was exposed to Fourier Transform Infrared (FTIR) spectrometry using a Bruker ALPHA II spectrometer (Billerica, MA, USA) equipped with an attenuated total reflectance (ATR) module. The OPUS software, https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/opus-touch.html?source=google&medium=cpc&campaign=BAXS_HMP_|_TITAN-Launch_PMax&content=world&s_kwcid=AL!14677!3!!!!x!!&gad_source=1&gad_campaignid=23686891660&gbraid=0AAAAACRXyuK9Eb3DYYPQO8a0GNY81s9Rp&gclid=CjwKCAjw1tLOBhAMEiwAiPkRHkOkED5B0906Z0J2tDKCiTRfcsEgLt9yKNtu-uJhkLStlV49mlmjXRoC9BoQAvD_BwE (accessed on 20 March 2026) was used to operate the instrument. Prior to each measurement, the ATR crystal was cleaned with distilled water. A background spectrum was collected under ambient conditions by averaging 24 scans, providing a spectral resolution of 4 cm⁻¹. Cattail fiber samples were then placed directly onto the ATR crystal. The spectra were acquired by averaging 24 scans for each sample. Baseline correction was applied automatically to all spectra in the OPUS software.

2.2. Microplastic Materials

Five pristine MPP materials, which were provided by the Centre de Recherche sur les Matériaux Avancés, CERMA (Laval University, Quebec City, QC, Canada), of an irregular shape were evaluated as follows: polypropylene (PP), polyvinyl chloride (PVC), polyamide 6 (PA6), low-density polyethylene (LDPE), and high-density polyethylene (HDPE).

Table 1. Microplastic particles (MPPs), weight average diameter, contact angle, and average molecular weight.

Plastic Material	Weight Average Diameter [μm]	Contact Angle [°]	Average Molecular Weight [g/mol]
PP	284	111.1 ± 4.2	30,000 [44]
PVC	127	121.0 ± 0.9	87,500 [44]
PA6	298	68.5 ± 5.5	47,667 [45]
LDPE	292	102.5 ± 2.4	23,000 [44]
HDPE	299	104.1 ± 1.9	145,000 [46]

summarizes information on the weight-average diameter of the irregular-shape MPPs used in this study. Table 1 also provides the contact angle of the MPPs as reported in our previous work [43] and the corresponding average molecular weights.

Particles within a diameter ranging from 1 μm to 5000 μm are classified as microplastic particles [2,3,9]. Therefore, the MPPs used in this work fall inside the micro-size particle classification.

MPPs were also subjected to Fourier Transform Infrared (FTIR) spectrometry using the analytical equipment previously described in Section 2.1.

2.3. Binding Environment

The binding environments used during the batch adsorption experiments were type 2 deionized water (DWT2) and synthetically produced water (SPW) from an oil recovery operation. The latter was used as a proxy for industrial wastewater. The objective was to establish the effect of binding environment on the adsorption behavior of microplastic particles on cattail fluff. An Arium^® Advanced EDI Bench-Top Unit (Sartorius Corporation, Bohemia, NY, USA) was employed to produce DWT2 with an electrical conductivity of 0.07 μS/cm, an ionic strength of 1.12 × 10⁻⁶ mol/L, and a pH of 5.62. The SPW was prepared following standard procedures for solution preparation. Therefore, the accurate masses of salts and light crude oil were obtained and added to the pre-established volume of DWT2. The amount of crude oil (e.g., condensate) added was 105 mg/L. The light crude oil was provided by Contact Exploration Inc. Stoney Creek, Sussex, NB, Canada. The SPW has an electrical conductivity of 34,120 μS/cm, which was determined using a FiveGo™ Conductivity Meter F3 (Mettler Toledo, Fisher Scientific, Hampton, NH, USA), an ionic strength of 54.8 mol/L, and a pH of 6.38.

Table 2. Composition of the synthetically produced water (SPW).

Salts	Content (wt.%)
NaCl	1.72
MgCl2	0.04
CaCl2	0.33
Na2SO4	0.01
Water	97.9
Total	100

presents the composition of the synthetically produced water.

2.4. Adsorption Experiments

2.4.1. Experimental Procedure

A fixed amount of bio-substrate (cattail fluff) of 100 mg was placed in a clear, round wide-mouth plastic jar. Afterwards, the pre-established mass of microplastic particles (MPPs) was added to the jar. Next, the bottle was filled to a total volume of 50 mL with the corresponding binding environment (DWT2 or SPW). Finally, the jar was securely closed with the cap. Teflon tape was applied to the tread of the bottle before placing the cap to avoid the leakage of the aqueous solution. Subsequently, the prepared jar was thoroughly mixed to maximize the contact of the bio-substrate with the MPPs before being placed in a water bath at the corresponding temperature of the experimental run (30 °C, 40 °C, or 50 °C) for a period of 24 h. Once the adsorption time of 24 h was achieved, the bottle was opened and the buoyant bio-substrate containing the adsorbed MPPs was carefully removed using a tweezer. The remaining solution was subjected to filtration under a vacuum using Fisherbrand^® (Thermo Fisher Scientific, Waltham, MA, USA) filter paper P8 (porosity: coarse; pore size: a range from 20 to 25 μm; flow rate: fast, at 160 mL/min) manufactured by Fisher Scientific (Hampton, NH, USA). The filter paper was pre-weighted and dried before use. After filtration, the filter paper containing the free (non-adsorbed) residual MPPs was placed in an oven at 45 °C. The mass of the filter paper with the retained free MPPs was continuously monitored until a constant mass was obtained. Later, the concentration of free MPPs (Ce or the equilibrium concentration) was calculated. The median of the standard error of the calculated Ce was ±63.09 mg/L. Figure 1 shows a simplified schematic of the experimental procedure.

2.4.2. Experimental Matrix

In this study, the concentration of MPPs in aqueous solution ranged from 1000 mg/L to 10,000 mg/L, with increments of 1000 mg/L for a total of 10 solution concentrations. The mass of adsorbent (bio-substrate) was kept fixed at 100 mg. Two binding environments were evaluated: DWT2 and SPW. The adsorption performance of 5 polymers, PP, PVC, PA6, LDPE, HDPE, and their blend or M5Poly (equal mass), was determined. Batch adsorption tests were conducted at three temperatures, 30 °C, 40 °C, and 50 °C, for a period of 24 h until equilibrium was reached. The batch adsorption tests were carried out in triplicate.

Table 3. Experimental matrix: batch adsorption tests conducted in triplicate.

Binding Environment: DWT2 or SPW Adsorption Time: 24 h
	MPPs Solution [mg/L]	T = 30 °C	T = 40 °C	T = 50 °C
MPPs Materials	1000	✓	✓	✓
	2000	✓	✓	✓
	3000	✓	✓	✓
	4000	✓	✓	✓
	5000	✓	✓	✓
	6000	✓	✓	✓
	7000	✓	✓	✓
	8000	✓	✓	✓
	9000	✓	✓	✓
	10,000	✓	✓	✓

summarizes the experimental matrix applied in this work.

2.4.3. Adsorption Isotherm Models

The relationship between the adsorbent and adsorbate under equilibrium conditions was established by performing non-linear regression fitting on the experimental adsorption data using seven adsorption isotherm models: Langmuir, Freundlich, a modified Temkin, modified Dubinin–Radushkevich (D–R), Redlich–Peterson (R–P), Toth, and Sips. Table 4 reports on the non-linear equations of these models and the corresponding relevant information.

2.4.4. Statistical Analysis

Non-linear regression was applied to fit the experimental data to the adsorption isotherm models selected (Table 4). PolymathPlus Pro, Web Server Version: 7.0.56, was used to compute the non-linear regression models.

The statistical interpretation of the best fit of the adsorption isotherm models to the experimental data was conducted by applying a 95% confidence interval and performing error function analysis including the coefficient of determination (R²), the adjusted coefficient of determination (Adj R²), the root mean square deviation error (RMSD), the mean square residual (MSE), and a non-linear chi-square test (X²). The modified Thompsom τ technique was used to reject questionable experimental data points [48].

Table 4. Adsorption isotherm models: non-linear equations and relevant information.

Langmuir adsorption isotherm model

$q_e={\displaystyle \frac{q_{max}{K}_L{C}_e}{1+K_L{C}_e}}$ where qe is the mass of solute uptake per unit mass of adsorbent at equilibrium [mg/g], Ce is the equilibrium concentration of the solute [mg/L], qMax is the maximum adsorption capacity [mg/g], and KL is the Langmuir adsorption constant [L/mg]. Monolayer adsorption. Homogeneous adsorption surface (e.g., uniform adsorption sites), thus constant adsorption energy. No interaction between adsorbate molecules adsorbed on neighboring sites. No steric hindrance and lateral interaction between the adsorbed molecules. Reversible chemical reaction [18,19,21,22,25].

Freundlich adsorption isotherm model

$q_e=K_f{C}_e^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}$ where qe is the mass of solute uptake per unit mass of adsorbent at equilibrium [mg/g], Ce is the equilibrium concentration of the solute [mg/L], Kf is the Freundlich adsorption constant [L/mg], and n is the Freundlich exponent related to surface heterogeneity and adsorption intensity. Applies to reversible non-ideal adsorption process, multilayer adsorption, physical adsorption process, heterogeneous adsorption systems(e.g., different surface energies) [18,19,22,25].

Complete Temkin adsorption isotherm model

$q_e=q_T\ln \left({\displaystyle \frac{1+K_T{C}_e}{1+K_1{C}_e}}\right), q_T={\displaystyle \frac{RT}{b_T}}$ where qe is the mass of solute uptake per unit mass of the adsorbent at equilibrium [mg/g], qT is the surface capacity for the adsorption/unit binding energy [(mg/g)/(J/mol)], bT is heat of adsorption [J/mol], Ce is the equilibrium concentration of the solute [mg/L], KT is the Temkin adsorption constant [L/mg], K1 is the equilibrium binding constant [L/mg], R is the universal gas constant [J/mol·K], and T is the temperature [K]. Applies to uniform distribution of heterogeneous binding sites on the adsorbant surface (e.g., the binding energy varies linearly over these different binding sites) and multilayer adsorption. This isotherm model accounts for the effect of adsorbate/adsorbate interactions on the adsorption process and the increase of surface coverage leads to a linear decrease in the heat of adsorption [18,19,21,22,25,49,50].

Dubinin–Radushkevich (D–R) adsorption isotherm model

$q_e=q_{max}{e}^{\left(-\beta {\epsilon }^2\right)},\epsilon =RT\ln \left(1+{\displaystyle \frac{C^o}{C_e}}\right), E={\displaystyle \frac{1}{\sqrt{2\beta }}}$ where qe is the mass of solute uptake per unit mass of adsorbent at equilibrium [mg/g], qMax is the maximum sorption capacity [mg/g], β is a constant related to the adsorption energy [mol2/J2], ε is the adsorption potential [J/mol], R is the universal gas constant [J/mol·K], T is the absolute temperature [K], Co is the standard molar concentration of the solute [1 mol/L], Ce is the solute concentration at equilibrium [mol/L], and E is the mean free energy of adsorption [J/mol]. Applies to adsorption experimental data at different temperatures, heterogenous adsorption systems, multilayer adsorption, and well-suited for physical adsorption processes. The term E is defined as “the free energy change when 1 mol of adsorbate is transferred to the solid surface from infinity from the solution” [19]. Therefore, E is a measure of adsorption strength between the adsorbent and adsorbate [18,19,21,22,25,51].

Redlich–Peterson (R–P) adsorption isotherm model

$q_e={\displaystyle \frac{K_R{C}_e}{1+a_R{C}_e^{b_R}}}$ where qe is the mass of solute uptake per unit mass of adsorbent at equilibrium [mg/g], Ce is the equilibrium concentration of the solute [mg/L], KR is the Redlich–Peterson adsorption constant [L/g], aR is an R–P constant [L/mg], and bR is an exponent that varies between 0 and 1 [dimensionless]. Applies to homogeneous and heterogeneous adsorption systems. This is an empirical hybrid model of the Langmuir and Freundlich adsorption isotherm models [18,19,21,22,25].

Toth adsorption isotherm model

$q_e={\displaystyle \frac{q_{max}{K}_T{C}_e}{{\left(1+ {\left(K_T{C}_e\right)}^n\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}}$ where qe is the mass of adsorbate per unit mass of the adsorbent at equilibrium [mg/g], qMax is the maximum adsorption capacity [mg/g], Ce is the equilibrium concentration of the solute [mg/L], KT is the Toth adsorption constant [L/mg], and n is an empirical parameter [dimensionless]. Applies to heterogeneous adsorption systems. This isotherm model is based on quasi-gaussian energy distribution model [18,19,21,22,25].

Sips adsorption isotherm model

$q_e={\displaystyle \frac{q_{max}{K}_S{C_e}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}{1+K_S{C_e}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}}$ where qe is the adsorbate uptake per unit mass of the adsorbent at equilibrium [mg/g], qMax is the maximum adsorption capacity [mg/g], KS is the Sips adsorption constant [L/mg], Ce is the equilibrium concentration of the solute [mg/L], and n is the Sips isotherm exponent [dimensionless]. Applies to homogeneous and heterogeneous adsorption systems. This is a hybrid model that combines the Freundlich and Langmuir adsorption isotherms [18,19,21,22,25].

Table 4. Adsorption isotherm models: non-linear equations and relevant information.

Langmuir adsorption isotherm model

$q_e={\displaystyle \frac{q_{max}{K}_L{C}_e}{1+K_L{C}_e}}$ where qe is the mass of solute uptake per unit mass of adsorbent at equilibrium [mg/g], Ce is the equilibrium concentration of the solute [mg/L], qMax is the maximum adsorption capacity [mg/g], and KL is the Langmuir adsorption constant [L/mg]. Monolayer adsorption. Homogeneous adsorption surface (e.g., uniform adsorption sites), thus constant adsorption energy. No interaction between adsorbate molecules adsorbed on neighboring sites. No steric hindrance and lateral interaction between the adsorbed molecules. Reversible chemical reaction [18,19,21,22,25].

Freundlich adsorption isotherm model

$q_e=K_f{C}_e^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}$ where qe is the mass of solute uptake per unit mass of adsorbent at equilibrium [mg/g], Ce is the equilibrium concentration of the solute [mg/L], Kf is the Freundlich adsorption constant [L/mg], and n is the Freundlich exponent related to surface heterogeneity and adsorption intensity. Applies to reversible non-ideal adsorption process, multilayer adsorption, physical adsorption process, heterogeneous adsorption systems(e.g., different surface energies) [18,19,22,25].

Complete Temkin adsorption isotherm model

$q_e=q_T\ln \left({\displaystyle \frac{1+K_T{C}_e}{1+K_1{C}_e}}\right), q_T={\displaystyle \frac{RT}{b_T}}$ where qe is the mass of solute uptake per unit mass of the adsorbent at equilibrium [mg/g], qT is the surface capacity for the adsorption/unit binding energy [(mg/g)/(J/mol)], bT is heat of adsorption [J/mol], Ce is the equilibrium concentration of the solute [mg/L], KT is the Temkin adsorption constant [L/mg], K1 is the equilibrium binding constant [L/mg], R is the universal gas constant [J/mol·K], and T is the temperature [K]. Applies to uniform distribution of heterogeneous binding sites on the adsorbant surface (e.g., the binding energy varies linearly over these different binding sites) and multilayer adsorption. This isotherm model accounts for the effect of adsorbate/adsorbate interactions on the adsorption process and the increase of surface coverage leads to a linear decrease in the heat of adsorption [18,19,21,22,25,49,50].

Dubinin–Radushkevich (D–R) adsorption isotherm model

$q_e=q_{max}{e}^{\left(-\beta {\epsilon }^2\right)},\epsilon =RT\ln \left(1+{\displaystyle \frac{C^o}{C_e}}\right), E={\displaystyle \frac{1}{\sqrt{2\beta }}}$ where qe is the mass of solute uptake per unit mass of adsorbent at equilibrium [mg/g], qMax is the maximum sorption capacity [mg/g], β is a constant related to the adsorption energy [mol2/J2], ε is the adsorption potential [J/mol], R is the universal gas constant [J/mol·K], T is the absolute temperature [K], Co is the standard molar concentration of the solute [1 mol/L], Ce is the solute concentration at equilibrium [mol/L], and E is the mean free energy of adsorption [J/mol]. Applies to adsorption experimental data at different temperatures, heterogenous adsorption systems, multilayer adsorption, and well-suited for physical adsorption processes. The term E is defined as “the free energy change when 1 mol of adsorbate is transferred to the solid surface from infinity from the solution” [19]. Therefore, E is a measure of adsorption strength between the adsorbent and adsorbate [18,19,21,22,25,51].

Redlich–Peterson (R–P) adsorption isotherm model

$q_e={\displaystyle \frac{K_R{C}_e}{1+a_R{C}_e^{b_R}}}$ where qe is the mass of solute uptake per unit mass of adsorbent at equilibrium [mg/g], Ce is the equilibrium concentration of the solute [mg/L], KR is the Redlich–Peterson adsorption constant [L/g], aR is an R–P constant [L/mg], and bR is an exponent that varies between 0 and 1 [dimensionless]. Applies to homogeneous and heterogeneous adsorption systems. This is an empirical hybrid model of the Langmuir and Freundlich adsorption isotherm models [18,19,21,22,25].

Toth adsorption isotherm model

$q_e={\displaystyle \frac{q_{max}{K}_T{C}_e}{{\left(1+ {\left(K_T{C}_e\right)}^n\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}}$ where qe is the mass of adsorbate per unit mass of the adsorbent at equilibrium [mg/g], qMax is the maximum adsorption capacity [mg/g], Ce is the equilibrium concentration of the solute [mg/L], KT is the Toth adsorption constant [L/mg], and n is an empirical parameter [dimensionless]. Applies to heterogeneous adsorption systems. This isotherm model is based on quasi-gaussian energy distribution model [18,19,21,22,25].

Sips adsorption isotherm model

$q_e={\displaystyle \frac{q_{max}{K}_S{C_e}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}{1+K_S{C_e}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}}$ where qe is the adsorbate uptake per unit mass of the adsorbent at equilibrium [mg/g], qMax is the maximum adsorption capacity [mg/g], KS is the Sips adsorption constant [L/mg], Ce is the equilibrium concentration of the solute [mg/L], and n is the Sips isotherm exponent [dimensionless]. Applies to homogeneous and heterogeneous adsorption systems. This is a hybrid model that combines the Freundlich and Langmuir adsorption isotherms [18,19,21,22,25].

3. Results

3.1. Bio-Substrate: Cattail Fluff Analysis

3.1.1. Contact Angle

The average contact angle obtained from four samples of cattail fluff is 158.4 ± 1°. Materials with water contact angles above 150° are considered superhydrophobic [52]. Figure 2a–e display pictures of the mature cattail fruit and cattail fluff, a picture of a drop of water (e.g., DWT2) over the surface of fibers during contact angle measurement, a picture of the cattail fluff in contact with type 2 deionized water (e.g., DWT2), and a picture of cattail fluff in contact with a blend of kerosene and light crude oil (condensate), respectively. Figure 2d shows that the cattail fluff is not wetted by water; on the contrary, it functions as a water-repellent material. Figure 2e indicates that when the cattail fluff was put in contact with oil, the fluff was instantaneously submerged in the oil phase. The hydrophobicity of cattail fluff is caused by the presence of a wax coating on the surface of the fluff fibers, which has been previously reported [42,53]. As reported in [43], the surface area of the cattail fibers is 0.0097 ± 0.037 m²/g.

3.1.2. Thermogravimetric Analysis (TGA)

Figure 3 shows the thermal behavior of cattail fluff via the thermogravimetric (TG) curve and its derivative (DTG). The TG curve shows three distinct regions. The first region (16 to 200 °C) corresponds to the dehydration stage where unbound and bound water are removed from the fluff with a weight loss of 7.8%. The maximum rate of dehydration occurred around T_Max 49 °C, as the DTG curve indicates. The second region (200 to 370 °C) corresponds to the main thermal decomposition of the bio-substrate with T_Onset = 262 °C. The DTG curves indicate that the maximum rate of degradation occurs at T_Max = 328 °C. The thermal degradation of hemicellulose and part of the cellulose takes place in this region with a total weight loss of 72%. It is also possible that the degradation of lignin is initiated in this region (around 350 °C). In this second region, the DTG curve shows a broad peak that could indicate the occurrence of overlapping decomposition reactions [54]. The third region (370 to 560 °C) represents a weight loss of 18%. The maximum degradation peak in this region occurs at T_Max = 402 °C. In this region, the total decomposition of cellulose and lignin takes place. This thermal behavior coincides with previous reported TGA information on cattail fluff [54,55] and other natural fibers [56,57]. The thermogravimetric analysis suggests that the cattail fluff is thermally stable up to 262 °C; therefore, it is suitable for relatively high-temperature applications.

3.2. Microplastic Particle (MPP) FTIR Analysis

The microplastic particles were subjected to FTIR analysis to verify the composition of the plastic materials used in this work. Figure 4a–e present the FTIR spectrum of each of the microplastic materials with the corresponding peak’s locations and functional groups. The assignment of functional groups was guided from previous studies [58,59]. The FTIR analysis confirmed that the composition of the plastic materials agrees with those described in previous FTIR studies on the characterization of plastics [59,60,61,62].

3.3. Adsorption of Microplastic Particle (MPP)/Adsorption Isotherms

3.3.1. Polypropylene (PP) Adsorption Isotherms

Table 5. Adsorption of PP onto a bio-substrate. Non-linear fitting of isotherm adsorption models (Table 4) at three temperatures and in two binding environments.

Binding Environment: DWT2
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.94	0.93	58	21	33,289
Freundlich	0.87	0.84	110	60	117,644
Modified Temkin	0.94	0.91	58	21	41,985
Dubinin–Radushkevich (D–R)	0.94	0.92	2.04 × 10−6	2.2 × 10−17	4.9 × 10−11
Redlich–Peterson (R–P)	0.95	0.92	55	19	37,423
Toth	0.94	0.91	58	21	41,666
Sips	0.96	0.94	48	14	27,771
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.65	0.59	174	192	322,083
Freundlich	0.84	0.82	116	93	144,328
Modified Temkin	0.97	0.96	48	16	29,928
Dubinin–Radushkevich (D–R)	0.81	0.78	4.3 × 10−6	3.8 × 10−6	2 × 10−10
Redlich–Peterson (R–P)	0.94	0.91	75	40	71,658
Toth	0.65	0.51	173	191	385,042
Sips	0.81	0.73	128	111	211,287
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.88	0.86	105	54	107,103
Freundlich	0.87	0.84	110	60	117,644
Modified Temkin	0.88	0.82	105	55	134,189
Dubinin–Radushkevich (D–R)	0.87	0.85	3.5 × 10−6	1.9 × 10−6	1.3 × 10−10
Redlich–Peterson (R–P)	0.88	0.82	104	54	133,523
Toth	0.88	0.82	105	54	133,927
Sips	0.88	0.83	103	53	129,005
Binding Environment: SPW
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.89	0.87	66	32	46,049
Freundlich	0.89	0.87	68	33	47,581
Modified Temkin	0.90	0.86	62	29	49,898
Dubinin–Radushkevich (D–R)	0.89	0.87	2.2 × 10−6	1.1 × 10−6	5.15 × 10−11
Redlich–Peterson (R–P)	0.89	0.85	66	32	55,227
Toth	0.89	0.85	66	32	55,227
Sips	0.89	0.85	65	32	54,874
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.88	0.86	83	54	74,178
Freundlich	0.95	0.94	53	24	30,009
Modified Temkin	0.95	0.93	53	24	36,363
Dubinin–Radushkevich (D–R)	0.95	0.94	1.8 × 10−6	8.0 × 10−7	3.32 × 10−11
Redlich–Peterson (R–P)	0.95	0.93	54	24	36,657
Toth	0.88	0.84	83	53	87,766
Sips	0.95	0.93	53	24	36,108
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.94	0.92	22	7.0	4881
Freundlich	0.92	0.90	26	9.0	6522
Modified Temkin	0.93	0.90	22	7.0	6103
Dubinin–Radushkevich (D–R)	0.92	0.91	8.1 × 10−7	2.8 × 10−7	6.48 × 10−12
Redlich–Peterson (R–P)	0.94	0.91	22	7.0	5930
Toth	0.94	0.91	21	6.0	5476
Sips	0.95	0.93	19	5.0	4485

shows the values of the statistical parameters and the analysis of several error functions obtained from the non-linear regression of seven isotherm adsorption models to determine the model generating the best fit of the PP MPP adsorption experimental data obtained at three temperatures (30 °C, 40 °C, and 50 °C) and two binding environments (DWT2 and SPW). The non-linear regression analysis indicates that the Dubinin–Radushkevich (D–R) model offers the best fit of the experimental adsorption data for PP MPPs for all the experimental conditions. The D–R isotherm adsorption model allows for the description of adsorption experimental data at different temperatures on heterogeneous adsorbents, which might explain why this model provides the best fit of the adsorption experimental data [18,51].

Table 6. D–R model best fit parameters for polypropylene (PP).

Binding Environment: DWT2
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	1.8 × 10−3 ± 2.0 × 10−4	6.7 × 10−9 ± 2.3 × 10−11	8.53
40	1.4 × 10−3 ± 2.6 × 10−2	5.7 × 10−9 ± 4.5 × 10−11	9.36
50	1.1 × 10−3 ± 2.0 × 10−5	4.6 × 10−9 ± 3.3 × 10−11	10.48
Binding Environment: SPW
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	1.2 × 10−3 ± 5.7 × 10−6	6.3 × 10−9 ± 9.7 × 10−12	8.93
40	7.3 × 10−4 ± 4.0 × 10−3	5.7 × 10−9 ± 2.0 × 10−10	9.40
50	3.2 × 10−4 ± 4.3 × 10−6	5.1 × 10−9 ± 2.5 × 10−11	9.95

summarizes the D–R model adsorption parameters: the maximum adsorption capacity (q_Max) calculated from the experimental adsorption data, the D–R constant (β) showing the corresponding 95% confidence index, and the calculated mean free energy of adsorption (E).

The results in Table 6 indicate that in DWT2, the adsorption (q_Max) of PP MPPs onto the bio-substrate underwent a minor decrease as temperature increased. However, for the case in which SPW served as the binding environment, adsorption decreased as temperature increased. In the case of an insoluble solute in a solution adsorbing onto a solid phase, the mean free energy of adsorption (E) derived from the D–R model cannot be used to distinguish the type of adsorption (e.g., physisorption, ion exchange, or chemisorption) [19,25,63]. However, in the context of this study, E is useful for evaluating the relative strength of the adsorbate–adsorbent interaction. For PP MPPs, the E values in Table 6 indicate that the strength of the PP MPP–adsorbent interactions increased slightly as the temperature increased in both binding environments, which agrees with previous research on the effect of temperature on hydrophobic interactions [19].

Effect of Temperature. Figure 5a,b display the effect of temperature on the adsorption capacity of the bio-substrate. Figure 5a,b present q_e as a function of C_e and temperature for the corresponding binding environments (DWT2 and SPW).

The adsorption of PP MPPs on the bio-substrate is strongly affected by temperature. Figure 5a reveals the higher adsorption of PP MPPs at higher temperatures in DWT2. This trend reverses in the case of SPW (Figure 5b), in which adsorption decreases with temperature.

Effect of Binding Environment. Figure 6 implies that an overall higher adsorption of PP MPPs is observed in DWT2 as the binding environment.

3.3.2. Polyvinylchloride (PVC) Adsorption Isotherms

Table 7. Adsorption of PVC onto the bio-substrate. Non-linear fitting of isotherm adsorption models (Table 4) at three temperatures and in two binding environments.

Binding Environment: DWT2
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.81	0.75	196	117	321,328
Freundlich	0.74	0.66	232	165	446,793
Modified Temkin	0.81	0.63	197	117	483,633
Dubinin–Radushkevich (D–R)	0.76	0.68	2.6 × 10−6	1.8 × 10−6	5.45 × 10−11
Redlich–Peterson (R–P)	0.85	0.70	190	74	452,289
Toth	0.79	0.58	207	136	537,758
Sips	0.99	0.98	48	7	28,913
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.94	0.92	102	38	94,305
Freundlich	0.94	0.93	101	37	91,778
Modified Temkin	0.94	0.90	104	38	128,753
Dubinin–Radushkevich (D–R)	0.94	0.92	1.2 × 10−6	4.4 × 10−7	1.25 × 10−11
Redlich–Peterson (R–P)	0.94	0.89	105	41	133,502
Toth	0.94	0.90	101	37	122,529
Sips	0.94	0.90	102	37	122,791
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.93	0.92	75	32	60,129
Freundlich	0.94	0.93	74	30	58,187
Modified Temkin	0.97	0.96	49	14	31,761
Dubinin–Radushkevich (D–R)	0.93	0.91	9.1 × 10−7	4.03 × 10−7	8.81 × 10−12
Redlich–Peterson (R–P)	0.94	0.91	74	31	69,354
Toth	0.94	0.91	75	31	71,058
Sips	0.94	0.91	74	30	70,299
Binding Environment: SPW
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.95	0.94	57	18	35,017
Freundlich	0.96	0.95	52	15	28,747
Modified Temkin	0.96	0.95	50	14	32,456
Dubinin–Radushkevich (D–R)	0.96	0.95	6.2 × 10−7	1.9 × 10−7	4.1 × 10−12
Redlich–Peterson (R–P)	0.93	0.90	70	29	63,034
Toth	0.96	0.94	52	15	34,606
Sips	0.96	0.94	52	15	34,740
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.95	0.94	59	19	34,391
Freundlich	0.94	0.93	64	20	39,892
Modified Temkin	0.95	0.93	57	16	39,621
Dubinin–Radushkevich (D–R)	0.94	0.93	6.95 × 10−7	2.0 × 10−7	4.7 × 10−12
Redlich–Peterson (R–P)	0.95	0.93	60	16	39,705
Toth	0.92	0.89	71	25	61,219
Sips	0.95	0.92	58	16	40,898
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.95	0.94	73	24	57,173
Freundlich	0.93	0.92	83	30	73,638
Modified Temkin	0.95	0.93	72	23	68,194
Dubinin–Radushkevich (D–R)	0.94	0.93	8.94 × 10−7	3.0 × 10−7	8.53 × 10−12
Redlich–Peterson (R–P)	0.95	0.93	73	24	68,495
Toth	0.93	0.90	86	34	95,543
Sips	0.95	0.93	72	23	65,737

reports the values of the statistical parameters and the analysis of error functions obtained from the non-linear regression of seven isotherm adsorption models (Table 4) at different experimental conditions. According to the error function analysis, the Dubinin–Radushkevich (D–R) model provides the best fit of the adsorption experimental data.

Table 8. D–R model best fit parameters for polyvinyl chloride (PVC).

Binding Environment: DWT2
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	5.5 × 10−4 ± 8.7 × 10−8	3.96 × 10−9 ± 2.1 × 10−13	11.24
40	2.5 × 10−3 ± 1.4 × 10−7	6.02 × 10−9 ± 7.6 × 10−14	9.12
50	2.6 × 10−3 ± 1.1 × 10−7	6.13 × 10−9 ± 5.5 × 10−14	9.03
Binding Environment: SPW
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	7.7 × 10−4 ± 7.3 × 10−7	5.2 × 10−9 ± 1.4 × 10−12	9.84
40	5.5 × 10−4 ± 8.2 × 10−8	4.0 × 10−9 ± 4.8 × 10−13	11.16
50	3.3 × 10−4 ± 3.9 × 10−12	2.9 × 10−9 ± 3.9 × 10−12	13.20

summarizes the D–R model adsorption parameters. The maximum adsorption capacity (q_Max) and the D–R constant (β) show the corresponding 95% confidence index. Column 4 in Table 8 displays the calculated mean free energy of adsorption (E).

Table 8 indicates that in DWT2, q_max increases as temperature increases, while the opposite occurs in SPW. The mean free energy of adsorption values, denoted by E, suggest that stronger adsorbate–adsorbent interactions take place in the SPW environment.

Effect of Temperature. The adsorption of PVC MPPs in DWT2 (Figure 7a) decreases as temperature increases. In the case of SPW (Figure 7b), adsorption increases as temperature increases, following a consistent trend.

Effect of Binding Environment. Figure 8a displays the larger adsorption of PVC MPPs in DWT2 compared to the adsorption observed in SPW. Figure 8b shows overlapping adsorption behavior in both binding environments, while Figure 8c indicates greater adsorption of PVC MPPs in SPW. These experimental observations reveal the important role of the binding environment composition on the adsorption of PVC MPPs onto the bio-substrate.

3.3.3. PA6 Adsorption Isotherm

Table 9. Adsorption of PA6 on the bio-substrate. Non-linear fitting of isotherm adsorption models (Table 4) at three temperatures and in two binding environments.

Binding Environment: DWT2
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.92	0.91	144	53	187,628
Freundlich	0.88	0.85	182	84	298,770
Modified Temkin	0.92	0.87	145	53	251,050
Dubinin–Radushkevich (D–R)	0.89	0.87	3.6 × 10−6	1.6 × 10−6	1.2 × 10−10
Redlich–Peterson (R–P)	0.96	0.93	110	31	145,601
Toth	0.95	0.92	114	33	156,051
Sips	0.98	0.97	67	12	54,412
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.87	0.85	179	108	314,134
Freundlich	0.92	0.90	147	74	212,674
Modified Temkin	0.87	0.81	180	107	397,580
Dubinin–Radushkevich (D–R)	0.91	0.89	3.2 × 10−6	1.7 × 10−6	9.98 × 10−11
Redlich–Peterson (R–P)	0.93	0.90	133	59	218,075
Toth	0.88	0.82	175	98	373,978
Sips	0.91	0.87	148	75	269,440
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.95	0.94	113	42	135,695
Freundlich	0.96	0.95	107	37	123,265
Modified Temkin	0.89	0.84	174	98	387,925
Dubinin–Radushkevich (D–R)	0.95	0.94	2.4 × 10−6	8.8 × 10−7	6.0 × 10−11
Redlich–Peterson (R–P)	0.96	0.94	108	38	148,745
Toth	0.95	0.93	117	46	175,327
Binding Environment: SPW
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.93	0.92	97	27	92,987
Freundlich	0.88	0.86	129	48	162,296
Modified Temkin	0.93	0.90	97	28	117,180
Dubinin–Radushkevich (D–R)	0.89	0.87	2.6 × 10−6	9.1 × 10−7	6.5 × 10−11
Redlich–Peterson (R–P)	0.94	0.91	93	25	106,658
Toth	0.94	0.91	92	25	104,657
Sips	0.97	0.96	61	11	45,577
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.93	0.92	102	26	93,848
Freundlich	0.82	0.77	171	73	262,946
Modified Temkin	0.93	0.89	102	26	125,436
Dubinin–Radushkevich (D–R)	0.84	0.80	3.35 × 10−6	1.3 × 10−6	1.0 × 10−10
Redlich–Peterson (R–P)	0.94	0.91	93	22	104,394
Toth	0.95	0.91	93	21	103,200
Sips	0.98	0.97	53	7	33,353
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.99	0.99	47	7	23,732
Freundlich	0.99	0.98	56	11	35,700
Modified Temkin	0.99	0.99	47	8	28,638
Dubinin–Radushkevich (D–R)	0.99	0.99	1.1 × 10−6	2.0 × 10−7	1.33 × 10−11
Redlich–Peterson (R–P)	0.99	0.99	47	7	27,687
Toth	0.99	0.99	46	7	27,538
Sips	0.99	0.99	47	7	27,968

shows the values of the statistical parameters and the analysis of several error functions obtained from the non-linear regression of seven isotherm adsorption models (Table 4) for the adsorption of PA6 MPPs at different experimental conditions. Table 9 indicates that the Dubinin–Radushkevich (D–R) model provides the best fit of the experimental adsorption data.

Table 10. D–R model best fit parameters for PA6.

Binding Environment: DWT2
Temperature [C°]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	1.4 × 10−3 ± 3.0 × 10−6	4.4 × 10−9 ± 3.2 × 10−12	10.67
40	6.9 × 10−3 ± 1.0 × 10−5	5.6 × 10−9 ± 6.9 × 10−14	9.47
50	8.3 × 10−3 ± 9.2 × 10−6	5.0 × 10−9 ± 1.2 × 10−12	9.95
Binding Environment: SPW
Temperature [C°]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	8.0 × 10−4 ± 3.6 × 10−6	3.4 × 10−9 ± 6.0 × 10−12	12.18
40	5.2 × 10−4 ± 1.1 × 10−6	2.4 × 10−9 ± 2.4 × 10−12	14.34
50	3.2 × 10−3 ± 1.4 × 10−4	4.4 × 10−9 ± 5.2 × 10−11	10.61

summarizes the D–R model adsorption parameters: the maximum adsorption capacity (q_Max) and the D–R constant (β) showing the corresponding 95% confidence index. Column 4 in Table 10 displays the calculated mean free energy of adsorption (E). Table 10 shows that q_Max increases as temperature increases in both binding environments. The E values suggest stronger adsorbate–adsorbent interactions in SPW.

Effect of Temperature. Figure 9a,b demonstrate that the adsorption of PA6 MPPs increases as temperature increases in both binding environments. However, the effect of temperature on PA6 adsorption is prominent in DWT2.

Effect of Binding Environment. Figure 10a–c reveal that the adsorption of PA6 MPPs increases substantially in DWT2 as the binding environment.

3.3.4. Low-Density Polyethylene (LDPE) Adsorption Isotherm

Table 11. Adsorption of LDPE onto bio-substrate. Non-linear fitting of isotherm adsorption models (Table 4) at three Temperatures and in two binding environments.

Binding Environment: DWT2
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.93	0.91	32	14	10,019
Freundlich	0.94	0.93	30	12	8571
Modified Temkin	0.93	0.90	31	13	11,653
Dubinin–Radushkevich (D–R)	0.94	0.93	1.2 × 10−6	4.9 × 10−7	1.5 × 10−11
Redlich–Peterson (R–P)	0.94	0.92	28	11	9720
Toth	0.93	0.89	32	14	12,339
Sips	0.97	0.96	19	5	4501
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.91	0.88	52	24	24,526
Freundlich	0.90	0.87	54	26	26,724
Modified Temkin	0.90	0.84	52	24	32,726
Dubinin–Radushkevich (D–R)	0.90	0.88	2.6 × 10−6	1.1 × 10−6	4.7 × 10−11
Redlich–Peterson (R–P)	0.91	0.84	52	24	32,702
Toth	0.91	0.84	52	24	32,715
Sips	0.93	0.89	43	17	22,654
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.91	0.88	28	11	7689
Freundlich	0.91	0.90	27	10	7145
Modified Temkin	0.92	0.88	26	10	8413
Dubinin–Radushkevich (D–R)	0.91	0.90	1.2 × 10−6	4.5 × 10−7	1.35 × 10−11
Redlich–Peterson (R–P)	0.91	0.87	27	10	8869
Toth	0.91	0.87	27	10	8869
Sips	0.91	0.87	27	10	8939
Binding Environment: SPW
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.72	0.67	98	122	94,668
Freundlich	0.84	0.81	74	61	54,328
Modified Temkin	0.84	0.77	74	56	66,721
Dubinin–Radushkevich (D–R)	0.84	0.81	3.3 × 10−6	2.7 × 10−6	1.0 × 10−10
Redlich–Peterson (R–P)	0.85	0.78	72	56	62,784
Toth	0.80	0.70	83	71	84,154
Sips	0.87	0.81	67	50	54,861
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.68	0.58	194	155	313,447
Freundlich	0.67	0.56	198	158	327,729
Modified Temkin	0.68	0.37	194	155	470,560
Dubinin–Radushkevich (D–R)	0.88	0.83	3.2 × 10−6	2.4 × 10−6	5.0 × 10−10
Redlich–Peterson (R–P)	0.69	0.40	192	152	461,553
Toth	0.68	0.37	194	156	470,331
Sips	0.73	0.47	178	145	395,811
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.76	0.73	121	119	156,407
Freundlich	0.92	0.90	72	46	54,901
Modified Temkin	0.97	0.95	45	18	26,422
Dubinin–Radushkevich (D–R)	0.92	0.90	3.1 × 10−6	2.0 × 10−6	1.0 × 10−10
Redlich–Peterson (R–P)	0.91	0.88	73	47	67,855
Toth	0.77	0.67	121	118	186,399
Sips	0.92	0.89	70	45	63,413

shows the values of the statistical parameters and the analysis of several error functions obtained from the non-linear regression of seven isotherm adsorption models (Table 4) of LDPE MPPs’ adsorption under different experimental conditions.

The error function analysis (Table 11) demonstrates that the Dubinin–Radushkevich (D–R) model offers the best fit of the LDPE MPPs’ experimental adsorption data.

Table 12. D–R model best fit parameters for low-density polyethylene (LDPE).

Binding Environment: DWT2
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	3.4 × 10−3 ± 1.6 × 10−5	1.0 × 10−8 ± 1.0 × 10−11	7.0
40	1.2 × 10−3 ± 9.0 × 10−7	6.8 × 10−9 ± 1.5 × 10−12	8.6
50	3.2 × 10−4 ± 1.1 × 10−4	5.0 × 10−9 ± 6.7 × 10−10	10.0
Binding Environment: SPW
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	1.2 × 10−2 ± 3.6 × 10−4	1.1 × 10−8 ± 5.7 × 10−11	6.8
40	5.3 × 10−2 ± 1.9 × 10−2	1.3 × 10−8 ± 4.0 × 10−10	6.3
50	9.4 × 10−2 ± 3.7 × 10−2	1.4 × 10−8 ± 7.5 × 10−10	5.9

summarizes the D–R model adsorption parameters: the maximum adsorption capacity (q_Max) and the D–R constant (β) showing their corresponding 95% confidence index. Column 4 in Table 12 displays the calculated mean free energy of adsorption (E).

The data in Table 12 implies that q_Max decreases as temperature increases in DWT2, while the opposite occurs in SPW. However, the calculated mean free energy of adsorption values, denoted by E, suggest that stronger interactions between the adsorbate–adsorbent take place in DWT2.

Effect of Temperature. Figure 11a and Figure 11b display the effect of temperature on the adsorption of LDPE MPPs onto the bio-substrate surface for DWT2 and for SPW as the binding environments, respectively.

Effect of Binding Environment. The isotherm adsorption curves displayed in Figure 12a–c indicate the overall greater adsorption of LDPE MPPs in the presence of SPW as the binding environment.

3.3.5. High-Density Polyethylene (HDPE) Adsorption Isotherm

Table 13. Adsorption of HDPE on the bio-substrate. Non-linear fitting of isotherm adsorption models (Table 4) at three temperatures and in two binding environments.

Binding Environment: DWT2
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.80	0.75	135	91	163,994
Freundlich	0.81	0.76	134	91	160,639
Modified Temkin	0.65	0.41	181	162	393,577
Dubinin–Radushkevich (D–R)	0.81	0.76	9.2 × 10−7	6.3 × 10−7	7.6 × 10−12
Redlich–Peterson (R–P)	0.81	0.68	134	91	214,165
Toth	0.80	0.67	135	91	217,668
Sips	0.82	0.70	129	88	198,617
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.89	0.86	74	39	48,654
Freundlich	0.90	0.88	69	36	42,821
Modified Temkin	0.80	0.66	101	73	121,326
Dubinin–Radushkevich (D–R)	0.90	0.88	4.8 × 10−7	2.5 × 10−7	2.1 × 10−12
Redlich–Peterson (R–P)	0.90	0.84	69	36	57,141
Toth	0.89	0.82	73	39	64,173
Sips	0.90	0.84	69	36	56,698
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.86	0.83	46	23	18,870
Freundlich	0.88	0.84	50	21	20,579
Modified Temkin	0.87	0.75	50	22	31,752
Dubinin–Radushkevich (D–R)	0.87	0.83	3.5 × 10−7	1.5 × 10−7	1.0 × 10−12
Redlich–Peterson (R–P)	0.88	0.75	50	21	30,942
Toth	0.85	0.70	55	28	37,951
Sips	0.83	0.66	59	31	43,031
Binding Environment: SPW
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.91	0.90	61	27	39,604
Freundlich	0.92	0.91	59	25	37,421
Modified Temkin	0.93	0.90	57	23	41,073
Dubinin–Radushkevich (D–R)	0.91	0.90	4.3 × 10−7	1.8 × 10−7	1.9 × 10−12
Redlich–Peterson (R–P)	0.92	0.89	59	25	45,087
Toth	0.91	0.88	61	27	48,286
Sips	0.88	0.83	73	39	67,405
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.71	0.65	122	120	146,383
Freundlich	0.86	0.83	84	69	69,028
Modified Temkin	0.90	0.85	70	45	60,810
Dubinin–Radushkevich (D–R)	0.98	0.97	7.5 × 10−8	1.0 × 10−8	4.7 × 10−14
Redlich–Peterson (R–P)	0.72	0.57	120	68	177,519
Toth	0.71	0.56	122	129	182,114
Sips	0.88	0.82	78	63	75,479
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.84	0.79	46	32	19,342
Freundlich	0.91	0.89	34	19	10,124
Modified Temkin	0.96	0.93	24	9	6957
Dubinin–Radushkevich (D–R)	0.91	0.89	2.4 × 10−7	1.4 × 10−7	5.1 × 10−13
Redlich–Peterson (R–P)	0.95	0.92	25	11	7599
Toth	0.84	0.73	46	32	25,554
Sips	0.91	0.84	35	21	14,631

shows the values of the statistical parameters and the analysis of several error functions obtained from the non-linear regression of seven isotherm adsorption models (Table 4) under the experimental adsorption conditions for HDPE MPPs. The Dubinin–Radushkevich (D–R) model provides the best fit of the experimental adsorption data for HDPE MPPs (Table 13).

Table 14. D–R model best fit parameters for high-density polyethylene (HDPE).

Binding Environment: DWT2
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	4.1 × 10−3 ± 1.31 × 10−4	7.8 × 10−9 ± 4.4 × 10−11	8.0
40	3.7 × 10−3 ± 2.91 × 10−7	8.4 × 10−9 ± 1.1 × 10−13	8.0
50	2.8 × 10−4 ± 7.21 × 10−5	5.1 × 10−9 ± 3.4 × 10−10	9.8
Binding Environment: SPW
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	1.1 × 10−3 ± 1.5 × 10−6	6.4 × 10−9 ± 1.9 × 10−12	8.8
40	1.1 × 10−4 ± 3.5 × 10−7	4.4 × 10−9 ± 4.1 × 10−12	10.6
50	2.9 × 10−2 ± 5.9 × 10−3	1.2 × 10−8 ± 2.9 × 10−10	6.3

summarizes the D–R model adsorption parameters: the maximum adsorption capacity (q_Max) and the D–R constant (β) showing the corresponding 95% confidence index. Column 4 in Table 14 displays the calculated mean free energy of adsorption (E).

Table 14 indicates that q_max is inversely affected by temperature in DWT2, whereas in SPW, q_Max tends to increase with temperature, with the largest value of q_Max = 2.9 × 10⁻² ± 5.9 × 10⁻³ mol/g at 50 °C. The E values exhibit a more consistent trend in DWT2, where the strength of the adsorbate–adsorbent interaction increases as temperature increases.

Effect of Temperature. Figure 13a,b imply that the adsorption of HDPE MPPs is inversely related to temperature in both binding environments, with the remarkable adsorption of HDPE MPPs at a temperature of 30 °C.

Effect of Binding Environment. Figure 14 displays the greater adsorption of HDPE MPPs in DWT2 as the binding environment at all adsorption temperatures.

3.3.6. Adsoprtion Isotherm for Mixture of 5 Polymers (M5Poly)

Table 15. Adsorption of M5Poly on the bio-substrate. Non-linear fitting of isotherm adsorption models (Table 4) at three temperatures and in two binding environments.

Binding Environment: DWT2
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.91	0.90	68	34	49,499
Freundlich	0.93	0.92	59	26	36,771
Modified Temkin	0.87	0.82	83	50	88,756
Dubinin-Radushkevich (D–R)	0.93	0.92	1.3 × 10−6	6.1 × 10−7	1.8 × 10−11
Redlich–Peterson (R–P)	0.95	0.93	52	20	34,597
Toth	0.92	0.88	66	31	56,176
Sips	0.94	0.92	55	23	38,907
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.89	0.87	70	36	52,540
Freundlich	0.89	0.88	70	36	51,718
Modified Temkin	0.98	0.97	31	7	12,385
Dubinin–Radushkevich (D–R)	0.88	0.86	1.5 × 10−6	8.3 × 10−7	2.6 × 10−11
Redlich–Peterson (R–P)	0.90	0.86	67	33	57,507
Toth	0.89	0.85	70	35	62,272
Sips	0.89	0.85	70	36	62,745
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.89	0.88	63	27	48,878
Freundlich	0.93	0.95	42	12	20,713
Modified Temkin	0.94	0.93	46	14	28,565
Dubinin–Radushkevich (D–R)	0.95	0.94	9.2 × 10−7	2.6 × 10−7	9.9 × 10−12
Redlich–Peterson (R–P)	0.95	0.94	42	12	24,284
Toth	0.86	0.81	74	37	73,229
Sips	0.95	0.94	42	12	24,209
Binding Environment: SPW
Temperature: 30 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.89	0.87	92	52	97,149
Freundlich	0.89	0.87	91	52	96,024
Modified Temkin	0.87	0.83	99	61	131,368
Dubinin–Radushkevich (D–R)	0.89	0.87	1.9 × 10−6	1.1 × 10−6	4.3 × 10−11
Redlich–Peterson (R–P)	0.89	0.85	91	32	112,073
Toth	0.89	0.85	91	52	112,519
Sips	0.89	0.85	91	52	112,045
Temperature: 40 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.93	0.91	70	31	52,435
Freundlich	0.93	0.92	69	31	50,890
Modified Temkin	0.98	0.97	39	9	19,343
Dubinin–Radushkevich (D–R)	0.92	0.91	1.6 × 10−6	7.5 × 10−7	2.6 × 10−11
Redlich–Peterson (R–P)	0.93	0.91	66	29	56,117
Toth	0.93	0.90	69	31	61,107
Sips	0.93	0.90	69	31	61,458
Temperature: 50 °C
Adsorption Isotherm Models	R2	R2adj	RMSD	X2	MSE
Langmuir	0.98	0.98	24	4	6685
Freundlich	0.97	0.96	34	8	13,426
Modified Temkin	0.98	0.98	24	4	7870
Dubinin–Radushkevich (D–R)	0.97	0.97	6.5 × 10−7	1.41 × 10−7	4.8 × 10−12
Redlich–Peterson (R–P)	0.98	0.98	24	4	7795
Toth	0.98	0.98	25	4	8114
Sips	0.98	0.98	23	3.81	7171

summarizes the error function analysis of the experimental data on the M5Poly MPPs’ adsorption, fitted to seven adsorption isotherm models. As shown in Table 15, the best fit is provided by the Dubinin–Radushkevich (D–R) model.

Table 16. D–R model best fit parameters for the 5-polymer mix (M5Poly).

Binding Environment: DWT2
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	1.3 × 10−2 ± 1.4 × 10−3	1.0 × 10−8 ± 2.0 × 10−10	6.9
40	4.1 × 10−3 ± 2.0 × 10−4	7.8 × 10−9 ± 8.0 × 10−11	8.0
50	3.0 × 10−4 ± 7.3 × 10−9	3.1 × 10−9 ± 3.6 × 10−14	12.6
Binding Environment: SPW
Temperature [°C]	qmax [mol/g]	β [mol2/J2]	E [kJ/mol]
30	5.3 × 10−3 ± 2.4 × 10−4	8.2 × 10−9 ± 7.6 × 10−11	7.8
40	3.9 × 10−3 ± 4.6 × 10−4	7.6 × 10−9 ± 1.9 × 10−10	8.1
50	5.8 × 10−4 ± 1.1 × 10−6	4.3 × 10−9 ± 2.8 × 10−12	10.8

summarizes the D–R model adsorption parameters: the maximum adsorption capacity (q_Max) and the D–R constant (β) showing their corresponding 95% confidence index. Column 4 in Table 16 displays the calculated mean free energy of adsorption (E).

Table 16 suggests that the adsorption of M5Poly MPPs decreases as temperature increases in both binding environments, whereas the strength of the adsorbate–adsorbent interactions increases as temperature increases in DWT2 and SPW.

Effect of Temperature. According to Figure 15a, the adsorption of M5Poly MPPs increases as the temperature increases in DWT2. However, at C_e ≥ 4000 mg/L, the isotherm adsorption curves intercept, and the adsorption performance reverses; thus, from this interception point on, the adsorption of MPPs decreases with increasing temperature. For the case of SPW, Figure 15b shows that the adsorption of M5Poly MPPs decreases as the temperature increases. The 30 °C isotherm adsorption curve displays the best adsorption performance in SPW, while the 40 °C and the 50 °C isotherm curves display overlapping adsorption behavior. This adsorption behavior matches the findings reported in Table 16.

Effect of Binding Environment. Figure 16a reveals that higher adsorption of M5Poly MPPs occurs in SPW at 30 °C, while at 40 °C, the adsorption performance is very similar in both binding environments (Figure 16b), with slightly larger adsorption in SPW at higher C_e values. Figure 16c indicates that the adsorption performance reverses, and greater adsorption of MPPs is obtained in DWT2 according to the 50 °C isotherm adsorption curves.

Table 17. Key experimental observations of the MPPs on the bio-substrate surface.

Polymer	Chemical Formula	Binding DWT2 Adsorption (A)	Environment SPW Adsorption (A)	Adsorption Strength, E [kJ/mol]
Polypropylene (PP)	(CH2-CH(CH3))n Linear Polymer	A ↑ T ↑ A ↑ Ce ↑ Overall ≫ A	A ↓ T ↑ A ↑ Ce ↑	E ↑ T ↑ DWT2 and SPW
Polyvinylchloride (PVC)	(C2H3Cl)n Linear Polymer	A ↓ T ↑ A ↑ Ce ↑ A >> @ 30 °C	A ↑ T ↑ A ↑ Ce ↑ A > @ 50 °C	DTW2 E ↓ T ↑ SPW E ↑ T ↑
Polyamide 6 (PA6)	-[NH(CH2)5CO]- Linear Polymer	A ↑ T ↑ A ↑ Ce ↑ Overall ≫ A	A ↑ T ↑ A ↑ Ce ↑	DTW2 E ↓ T ↑ SPW E ↑ T ↑
Low-Density Polyethylene (LDPE)	(C2H4)n Branched Polymer	A > @ 40 °C A ↑ Ce ↑	A > @ 40 °C A ↑ Ce ↑ Overall ≫ A	DTW2 E ↑ T ↑ SPW E constant
High-Density Polyethylene (HDPE)	(C2H4)n Linear Polymer	A ↓ T ↑ A ↑@ Ce ↑ Overall ≫ A	A ↓ T ↑ A ↑ Ce ↑	DWT2 >E @ 50 °C SPW >E @ 40 °C
M5Poly	-	A ↑ T ↑ A ↑ Ce ↑ A > @ 40 °C	A ↓ T ↑ A ↑ Ce ↑ >A @ 30 °C	E ↑ T ↑ DWT2 & SPW

summarizes the key experimental observations of the MPPs’ adsorption behavior onto the bio-substrate at the experimental range of q_e as a function of C_e.

Table 17 identifies comparable adsorption performance trends between the MPPs using the same color shading in the background of the rows. For instance, with DWT2 as the binding environment, PP, PA6, and M5Poly MPPs show the same adsorption performance (clear blue shading). Likewise, PVC and HDPE MPPs (clear green shading) display identical adsorption behavior in DWT2. In SPW as the binding environment, PP, HDPE, and M5Poly MPPs exhibit similar adsorption traits (clear yellow shading). Likewise, PVC and PA6 MPPs in SPW show matching adsorption trends (clear orange shading), whereas LDPE MPPs have unique adsorption performance in both binding environments.

These experimental results reveal that MPP materials do not display a consistent adsorption pattern in aqueous environments. Therefore, the adsorption of MPPs onto the bio-substrate is strongly influenced by the chemical composition of the plastic materials, the composition of the binding environment, and the temperature of the adsorption process. As presented in Table 17, some of the MPPs (PP, PA6, and HDPE) display higher adsorption in DWT2, while other materials, like LDPE MPPs, exhibit greater adsorption in SPW; whereas PVC MPPs exhibit the same adsorption at specific temperatures and larger adsorption at 30 °C in DWT2 and at 50 °C in SPW.

Figure 17a–f display q_e (solute uptake per unit mass of adsorbent at equilibrium) as a function of temperature at a fixed solute equilibrium concentration of C_e = 4500 mg/L for all the MPP materials. Figure 17a–f also provide information on the average q_e in the range of temperature evaluated for both binding environments at C_e = 4500 mg/L.

The general adsorption performance summarized in Figure 17a–f confirms the significant solute uptake per unit mass of bio-adsorbent (q_e) within the range of temperatures evaluated. The lowest q_e value registered was 730 mg/g for LDPE MPPs in DWT2 (Figure 17d), while the highest q_e value was 11,768 mg/g for PA6 MPPs in DWT2 (Figure 17c). The information presented in Figure 17 matches the general adsorption trends provided in Table 17. A discussion on the potential adsorption mechanisms of MPPs on the bio-substrate is presented in the following section.

4. Discussion

The modified Dubinin–Radushkevich (D–R) isotherm adsorption model provided the best fit of the experimental adsorption data for all the MPP materials evaluated in this work. The D–R model is a commonly used isotherm model suitable for physical and chemical adsorption processes [4,18,21,24]. The D–R isotherm adsorption model “is especially well-suited for physical adsorption processes” [18], and for describing monolayer and/or multilayer adsorption on heterogeneous adsorbent surfaces [18,19,21,22,24]. According to the literature [19], the “D-R model is defined more broadly than the Langmuir model and is extended to cover the case of aqueous contaminants adsorption”. The D–R isotherm model is temperature-dependent and assumes an adsorption mechanism based on the Gaussian energy distribution onto heterogenous surfaces [19,25]. Consequently, as the D–R isotherm adsorption model allows for the description of adsorption experimental data at different temperatures on heterogeneous adsorbents, it provides the best fit of the adsorption experimental data in this study [18,51]. On one hand, the Temkin adsorption isotherm model is also temperature-dependent, as previous research has demonstrated several limitations associated with its applicability. These limitations include the range of validity of adsorbate concentration (e.g., restricted to only intermediate concetrations) [25,49] and a limited range of ion concentrations in the binding media [18]. Likewise, the newly proposed nonlinear three-constant Temkin adsorption isotherm model requires complex iteration processes to determine the model fitting parameters [50]. On the other hand, the Redlich–Peterson (R–P), Toth, and Sips adsorption isotherm models are empirical hybrid models of the Langmuir and Freundlich adsorption isotherm models, which offer limited relevance in our study because these models are non-temperature-dependent.

The bio-substrate used in this study exhibits a superhydrophobic solid surface (contact angle of 158.4 ± 1°), as its surface is coated with a film of wax, making it have a low-energy surface. The MPP materials PP, PVC, LDPE, and HDPE are hydrophobic plastic materials with contact angles > 100° (Table 1); hence, they are low-surface-energy materials. The PA6 MPPs, on the other hand, exhibit a contact angle of 68°, indicating that the surface of PA6 displays a surface that is neither hydrophobic nor hydrophilic; therefore, it could be considered a solid surface with dual-surface wettability and higher surface energy relative to those of the other MPPs materials evaluated in this work.

Hydrophobic surfaces exhibit low surface energy. In a liquid–hydrophobic solid adsorption system, the MPPs in the aqueous solution do not interact with the aqueous solvent (having a negligible energetic incentive) but migrate toward the hydrophobic solid surface because the interactions between MPPs and the hydrophobic surface are energetically favored. This is because solid surfaces will always try to minimize its energy by adsorbing a material with a lower energy onto its surface. Thus, in the context of this study, surface energy minimization occurs both ways on the MPPs and on the bio-substrate surface. Accordingly, “through the adsorption process, the number of exposed surface atoms with [relatively] high surface energy [either on the bio-substrate and/or on the MPPs surface] are minimized [through hydrophobic interactions] with lower energy atoms or molecules” [64].

FTIR spectroscopy was performed to evaluate the interactions between PP MPPs and the bio-substrate. Figure 18 displays the FTIR spectra of the bio-substrate before adsorption (Figure 18a) and the PP MPPs before adsorption (Figure 18b), and Figure 18c–e present the spectra of PP MPPs adsorbed onto the bio-substrate at 30 °C, 40 °C, and 50 °C in SPW as the binding environment.

Figure 18a shows the spectrum of the bio-substrate before adsorption. The broad frequency located at 3349.23 cm⁻¹ corresponds to the H-bonded OH stretch related to the presence of alcohols. The strong frequencies located at 2921.54 cm⁻¹ and 2852.77 cm⁻¹ are assigned to the methyl (-CH₃) and methylene (-CH₂) asym./sym. stretching of saturated aliphatic groups attributed to natural waxes. The strong peak at location 1742.51cm⁻¹ corresponds to esters (e.g., R-C(=O)-O-R′), while the peaks at 1631.63 cm⁻¹ and at 1456.8 cm⁻¹ are assigned to alkenyl C=C stretching and methylene C-H bending. These functional groups are normally associated with natural waxes. The strong peaks observed from 1317.57 cm⁻¹ to 665.77 cm⁻¹ are attributed to skeletal vibrations of the C-C stretching of saturated aliphatic (alkane/alkyl) group frequencies. The interpretation of the FTIR spectra was guided by [58]. Figure 18b displays the FTIR spectrum of PP MPPs before adsorption, which is also presented in Figure 4a, showing the corresponding peaks’ location and allocation of functional groups. The FTIR spectra of the bio-substrate samples after the adsorption of PP MPPs at different temperatures display the peak locations of both materials: the bio-substrate and the PP MPPs. The peaks surrounded by dashed squares in Figure 18c–e correspond to peaks characteristic to the bio-substrate, whereas the peaks surrounded by dashed ovals belong to PP MPPs. In the adsorbed bio-substrate–PP MPP samples in Figure 18c–e, some peaks of the saturated aliphatic group frequencies of the bio-substrate and PP MPPs overlap each other. All the peaks of PP MPPs identified in the FTIR spectra are observed in the bio-substrate samples after adsorption. The FTIR spectra of the of adsorbed samples do not reveal the presence of new peak locations, so there is no formation of new components through covalent bonding; therefore, physisorption of PP MPPs onto the bio-substrate takes place. Furthermore, all the peaks associated with saturated alkane/alkyl group frequencies, including -CH₃, -CH₂, C-H bend, skeletal C-C vibrations, alkene group frequency C=C, and carbonyl ester or R-C(=O)-O-R′, are slightly shifted after adsorption, indicating that all these functional groups interact during the physisorption process. The intensity of some of the peaks slightly increase or decrease after adsorption, suggesting hydrophobic interactions between the PP MPPs and the bio-substrate. Some peaks of saturated aliphatic group frequencies (e.g., -CH₃ and -CH₂) at locations characteristic of the bio-substrate, 2852.77 cm⁻¹ and 2921.54 cm⁻¹, completely overlap after adsorption in the presence of PP MPPs (e.g., Figure 18c–e). The average gain in peak intensity was 20%, while the average reduction in peak intensity was 24%. Figure 19 presents the stacked FTIR spectra of the materials before and after adsorption for better visualization of the peak location before and after adsorption. According to the FTIR spectra displayed in Figure 18 and Figure 19, physisorption of PP MPPs onto the bio-substrate occurs via weak forces such as hydrophobic interactions due to the affinity of the saturated aliphatic groups (e.g., alkane/alkyl), alkene, and carbonyl functional groups present in both materials: the bio-substrate and PP MPPs.

4.1. Hydrophobic Interactions: Effect of Temperature

Hydrophobic interactions rely upon the affinity of non-polar hydrophobic materials for water molecules, in terms of their aggregation and repulsion in a polar medium [10,65,66,67]. According to the literature [66], “hydrophobic association is driven by an increase in entropy as the ordered water gets released from the surface of the hydrophobic solute to the bulk upon formation of the contact [of the hydrophobic] pair”. On average, higher adsorption of PP, PVC, PA6, HDPE, and M5Poly MPPs (e.g., except for LDPE MPPs) onto the bio-substrate occurs in DWT2 as the binding environment (Table 17 and Figure 17), suggesting that the adsorption of these MPPs onto the bio-substrate is dominated by hydrophobic interactions, hence making the process one of physisorption process. These results agree with the earlier FTIR spectroscopy analysis results (Figure 18) and with the findings of other researchers regarding the adsorption of MPPs onto hydrophobic surfaces [14,68]. Table 17 and Figure 17 also show that in DWT2 as the binding environment, the adsorption of all MPPs (excluding HDPE MPPs) increases with temperature. A slight increase in the absorption of MPPs onto the bio-substrate is observed for PVC, LDPE, and M5Poly MPPs as a function of temperature, while for PP and PA6 MPPs, adsorption increases significantly as temperature increases. This performance is expected as the strength of hydrophobic interactions increases with temperature. This occurs because the water molecules trapped within the hydrophobic aggregates or clusters are expelled into the bulk of the aqueous solution to maximize the hydrogen bonds among the water molecules [59,65,66]. The effect of temperature on hydrophobic interactions is evident from the mean free energy of adsorption values, denoted by E, reported in this work. E provides information on the strength of the interactions between the MPPs and the bio-substrate. As Table 17 outlines, E values increase as temperature increases for PP, LDPE, HDPE, and M5Poly MPPs in both binding environments (DWT2 and SPW), while for PVC and PA6 MPPs, E increases as temperature increases in SPW as the binding environment.

4.2. Hydrophobic Interactions: Effect of Binding Environment Composition

The synthetically produced water, SPW, used in this study as a binding environment contained monovalent and divalent cations including Na⁺, Ca²⁺, and Mg²⁺ (refer to Table 2), as well as monovalent and divalent anions such as Cl⁻ and SO₄²⁻. In addition, the SPW aqueous solution contained 105 mg/L of light crude oil (e.g., condensate).

Table 17 and Figure 17 display the significant effect of the SPW binding environment on the sorption performance of MPPs on the bio-substrate. Reduced adsorption was observed for all the MPPs apart from the LDPE MPPs. The overall average adsorption of MPPs in SPW as the binding environment was 2807 mg/g, while the overall average adsorption of MPPs in DWT2 was 3597 mg/g. The average adsorption reduction in SPW was 22%. Consequently, the effect of binding environment composition on the performance of MPP adsorption onto the bio-substrate is noteworthy.

Hydrophobic interactions between the adsorbate (MPPs) and the adsorbent (bio-substrate) are affected by the presence of salts in the binding environment. The addition of electrolytes tends to increase the strength of hydrophobic interactions because electrolytes decrease the solubility of non-polar materials in water [66,69]. This implies that “higher salt concentration favors aggregation [of hydrophobic particles] because an increase in ion concentration in the medium acts to neutralize Coulombic repulsion between the surface charge and the counterion cloud” of the MPPs. This causes the compression of the double layer surrounding the MPPs screening the repulsion among particles in solution, which promotes the agglomeration or formation of MPPs clusters in SPW [70,71]. The agglomeration of MPPs causes steric hindrance effects at the adsorption surface, causing the reduced adsorption of MPPs clusters onto available adsorption sites on the hydrophobic bio-substrate. The steric hindrance effect on the reduced adsorption of agglomerates has been confirmed by previous researchers [72,73], who reported on the significant effect of adsorbate steric factors of agglomerated solid solutes on the adsorption process, with reported adsorption reductions of up to 40%.

The distinctive behavior of the adsorption of LDPE MPPs onto the bio-substrate surface when SPW was used as binding environment was probably caused by the “unique” relation between the surface charge of LDPE and its adsorption performance in aqueous solutions of inorganic electrolytes. During the manufacture of LDPE, the polymerization stage and/or further processing cause the oxidation of the LDPE surface due to the dissociation of carboxyl groups. Thus, at pH > 2.5, the surface of LDPE is negatively charged [74]. As expected, the addition of electrolytes at low concentrations (<0.01 M) enhances the adsorption of electrolytes onto the double layer at the LDPE surface, compressing it [74]. This effect causes the agglomeration of LDPE MPPs, which decreases the adsorption of MPPs onto the bio-substrate. However, the addition of higher concentrations of electrolytes (e.g., 0.3 mol/L in this work) suppresses it [75]. According to the literature [74], at higher concentrations of electrolytes, “the effect of adsorption of counterions is probably counterbalanced by the inverse effect of increasing the surface charge”. The increase in the negative surface charge on the LDPE MPPs promotes repulsion between the particles in solution, preventing their agglomeration in the SPW solution, which favors their adsorption onto the bio-substrate. The surface potential decay and the effect of humidity on charge formation and transport in LDPE have been studied by other researchers [76,77].

Furthermore, the addition of lyophobic components (e.g., light crude oil) to the binding environment also positively influences the adsorption behavior of MPPs. The enhanced adsorption of MPPs onto the bio-substrate via the addition of a lyophobic additive to the binding environment increases the hydrophobicity of the bio-substrate by forming a uniform layer of oil on the bio-substrate surface, which increases its hydrophobicity. As the hydrophobicity of the absorbents increases, its adsorption capacity increases toward hydrophobic components [4]. According to the literature [78], the addition of light oil causes the “pre-paving” of the substrate, which increases its interaction with hydrophobic components. Previous work observed that hydrophobization of the surface of porous aerated concrete using oleic and stearic acid significantly increased the sorption capacity of the adsorbent for dispersed oil [79]. In our previous work [43], we also demonstrated that the addition of heavy crude oil to the binding environment drastically enhanced the rate of adsorption of MPPs onto the hydrophobic surface of the bio-substrate. However, in this study, it is difficult to quantify the independent effect of adding electrolytes and lyophobic additives to the binding environment on the adsorption of MPPs, because we did not conduct independent experimental runs by adding only electrolytes and/or only the lyophobic component to the binding environment.

4.3. Isotherm Adsorption Curve

In this study, the D–R adsorption isotherm curves seem to follow the class C isotherm in Figure 20 [80,81]. The linear isotherm “is consistent with conditions in which the number of sites (not necessarily of equal energy [heterogenous adsorbents]) remain constant throughout the whole range of solute concentration up to the saturation of the substrate” [80]. However, in this work, the adsorption isotherms do not reach a plateau, indicating incomplete saturation of the adsorbent surface, possibly due to adsorbate steric effects. Consequently, the adsorption isotherm is class C subgroup 1 [81]. According to the literature [80], the adsorption dynamics for this class of isotherm “consider the model as an adsorbing surface with an area which expands by opening-up or disentangling [the fibers network] of the structure. We may then assume that the rate of adsorption is independent of the available area of substrate at any given time, because each molecule as it becomes adsorbed can readily generate a new vacant site; it is therefore dependent only on the concentration of c_A of the solute A in the solution in contact with it”. This process will continue until a horizontal plateau is reached, which “indicates saturation of all the internal area of substrate which can be opened up by the solute” that would correspond to class C, subgroup 2, in Figure 20. Isotherms in class C are common for “(a) a [heterogenous] porous substrate with flexible molecules, and a solute with (b) higher affinity for the substrate than [for] the solvent, and with (c) better penetrating power, by virtue of condition (b)” [81]. In other words, a class C subgroup 1 isotherm indicates “constant partition of the substance [MPPs] between the surface layer and the bulk phase” [82].

As earlier indicated, in this work, all the isotherm curves seem to follow the adsorption mechanism described for a class C-curve subgroup 1 aside from the adsorption isotherm curves of LDPE MPPs in SPW as the binding environment. The LDPE MPP isotherm curves appear to follow the adsorption mechanisms associated with an isotherm in class S subgroup 1 (Figure 20). The adsorption dynamics of a S isotherm curve indicates that “adsorption becomes easier as concentrations increases” [81]. The S-curve isotherm is common for “a monofunctional solute molecule with moderate intermolecular attraction, causing it to pack vertically [vertical or possible inclined] in regular array in the adsorbed layer…Sometimes, high salt concentration promotes S-curve formation” [81,82]. As previously discussed, the distinctive effect of electrolytes on the adsorption performance of LDPE MPPs is probably related to the adsorption mechanism attributed to the S-curve isotherm class.

The Dubinin–Radushkevich (D–R) model constant β (mol²/J²) determines the shape of the isotherm [19]. In this study, the average value of β for all the isotherms displaying a class C subgroup 1 is β = 5.9 × 10⁻⁹ mol²/J², while the average β value for the isotherm following a class S subgroup 1 isotherm is β = 1.3 × 10⁻⁸ mol²/J². These experimental results agree with the reported values in [19], which describe a β value for a S-curve isotherm of one order of magnitude higher than the that of L-shape curve. The C-curve subgroup 1 isotherm appears to be in between the L-curve and S-curve isotherms in subgroup 1 in terms of its behavior (see Figure 20).

5. Conclusions

The adsorption performance of a superhydrophobic and thermally stable (up to 262 °C) bio-substrate toward microplastic particles (MPPs) in aqueous solutions was investigated. Batch adsorption tests were conducted at three temperatures (30 °C, 40 °C, and 50 °C) and in two binding environments (DWT2 and SPW) for 24 h to reach adsorption equilibrium. Several adsorption isotherm models were evaluated, including Langmuir, Freundlich, Temkin, Dubinin–Radushkevich (D–R), Redlich–Peterson (R–P), Toth, and Sips. Non-linear regression and error function analyses indicated that the D–R adsorption isotherm model offered the best fit for the adsorption experimental data. The experimental results indicated that the hydrophobic bio-substrate (cattail fluff) is very effective in adsorbing MPPs of different pristine plastic materials (PP, PVC, PA6, LDPE, HDPE, and their blend) from aqueous systems at different adsorption temperatures and in different binding environments. The effect of binding environment on the adsorption of MPPs was established. Finally, it was observed that the adsorption process was dominated by hydrophobic interactions between the adsorbate/bio-substrate system, therefore following a physisorption process. The majority of the MPPs followed the adsorption dynamics of class C curve subgroup 1.

Overall, 22% higher MPP adsorption was obtained in distilled water compared to industrial wastewater. Nonetheless, the average bio-substrate adsorption capacities in both binding environments were very high, with q_e = 3597 mg/g in DWT2 and q_e = 2807 mg/g in SPW. The effects of temperature and binding environment on the adsorption of MPPs onto the bio-substrate are a function of the plastic material composition. Adsorption mechanisms of MPPs onto the bio-substrate were discussed in detail, but more research should be conducted using weathered MPPs to establish the effect of weathering conditions on the adsorption capacity of this superhydrophobic bio-substrate. Other plastics could also be investigated, such as acrylonitrile–butadiene styrene (ABS), polyethylene terephthalate (PET), and polystyrene (PS), just to name the most important. Furthermore, more research is justified to evaluate regeneration processes to reuse the spent bio-adsorbent, the adsorption effectiveness of the regenerated bio-substrate, and the mechanical stability of the bio-substrate after several regeneration stages.

Overall, the hydrophobic bio-substrate was highly effective in removing MPPs from aqueous systems, with the added advantages of low cost, sustainability, and scalability for practical applications.