3.1. SEM and EDS analyses
In the SEM micrographs of the recycled HDPE (Fig. 2), it is possible to identify impurities on the surface of this polymer. The EDS spectra indicate that these contaminants are constituted by K, O, Si, Na, Mg, Fe, S, Cl, Ca, and P chemical elements. These chemical elements can be related to different additives present in HDPE, as detailed in Table 1 [46–49]. The presence, in greater concentration, of carbon is due to the chemical composition of the PE polymer chains. Besides being associated with the additives mentioned previously, the oxygen element can also be associated with oxidative degradation of the polymer during its processing and life service time. These EDS elemental signals cannot be associated with residual Phillips catalyst (based on chromium oxide) supported on silica or alumina and residual Ziegler-Natta catalysts based on alkyl aluminum compounds and a salt (containing Ni, Co, Zr, or Ti) [50] due to their low residual concentrations for the synthesis of HDPE, since these chemical elements were not identified in the pristine HDPE. According to TGA results (Supplementary Material), the impurity content in the recycled polyethylenes is very low since the carbonaceous residues were 0.72 wt% and 0.78 wt% for pristine and recycled LDPE, respectively. At the same time, the residual mass is 0.68 wt% for pristine HDPE and 0.98 wt% for recycled HDPE. Moreover, the recycled and pristine polyethylenes present a similar mass loss curve with a single thermo-decomposition process with maximum rate, indicating the absence of another type of polymer in the recyclates.
The recycled LDPE also has impurities, as shown in Fig. 3, composed of Al, Fe, Mg, O, and Si. These elements are typical chemical constituents of silicates [51]. Since particle size is in the micrometer order, these silicates were probably applied as a filler or come from contamination after the disposal of the LDPE-based product. However, LDPE is typically used to manufacture food packaging, and nanometric clays can be introduced in LDPE and other polymer films to reduce oxygen gas diffusion into the product due to a barrier effect [52], reducing the speed oxidation of packaged food. In addition, nano-clays are used as reinforcement fillers in polymers, being widely applied in the development of polymer matrix nanocomposites [53, 54].
Table 1
Possible additives present in recycled PEs associated with the chemical elements detected by EDS.
Chemical element
|
Polymer additives
|
P, O, and S
|
Phosphates, phosphites, and sulfites are secondary antioxidants, acting on the decomposition of hydroperoxides that are formed in the plastic
|
Mg, O, and Si
|
Talc (filler) containing magnesium oxide (MgO), silicon dioxide (SiO2), and adsorbed water
|
Ca, C and O
|
Calcium carbonate (CaCO3), which is a filler typically used as a filler in polymers, to lower the price of the plastic product
|
Si and O
|
Silicon dioxide (SiO2) and glass are used as fillers and reinforcement in thermoplastics.
|
Cl
|
Presence of chlorinated flame retardants or presence of chlorinated compounds in recycled HDPE due to its direct contact with chlorinated drinking water, when this polymer is used as tubes
|
Fe, Na, K, Si, Mg, and O
|
Mica and clays (silicates) are commonly used as fillers in the granulometry observed in the recycled HDPE micrograph
|
3.2. ATR-FTIR spectroscopy
The ATR-FTIR spectra of HDPE and LDPE, pristine and recycled, are shown in Fig. 4. The vibration band located at 705–735 cm− 1 corresponds to the deformation in the plane (per rotation) of the connections in the methylene group (amorphous phase and crystalline). The balance-type deformations of the C-H bonds in the amorphous and crystalline phases are associated with the signal at 1450–1480 cm− 1. The signal at 2875 − 2770 cm− 1 is related to the symmetrical stretching of CH2 (amorphous and crystalline phases), while the vibrational band at 2980 − 2875 cm− 1 refers to the asymmetric stretching of CH2 (amorphous phase). The absorption bands with low intensity at 1306–1351 cm− 1 were observed and are connected with the out-of-plane deformations (torsion and swing type) of the CH3 groups. A weak absorption at 1340–1390 cm− 1 is observed in the polyethylene spectra, also due to the balance-type deformation of the CH3 groups [55–59]. The main differences between the ATR-FTIR spectra from HDPE and LDPE correspond to variations in the absorption intensity of infrared radiation in the 1450–1480 cm− 1 and 2980 − 2875 cm− 1 bands. Also, there are differences in the ATR-FTIR spectra region at 1340–1390 cm− 1. These infrared alterations are associated with the differences in the content of amorphous and crystalline phases occasioned by the increase of CH3 groups in LDPE due to the highest quantity of polymer branches in this polyethylene type than in HDPE [60].
The characteristic absorption signals for the chemical groups and their vibrational modes in PP [61], PET [62–64], silica (SiO2) [65], and calcium carbonate (CaCO3) [63, 66] from the ATR-FTIR spectra (Fig. 5) are detailed in Table 2. The FTIR spectrum from recycled PET does not indicate the presence of functional groups due to the presence of contaminants. Vaterite (space group Pbnm), calcite (space group R3 ̅C and aragonite (space group Pmcn) are the three CaCO3 polymorphic crystalline phases based on hexagonal, trigonal, and orthorhombic crystal systems, in that order [67, 68]. Silicon dioxide (silica) also displays polymorphic behavior, presenting eight different crystalline phases containing a different number of SiO2 groups per unit cell [69]: α-quartz (trigonal cell unit with space group P3221), α-cristobalite (tetragonal cell unit with space group P41212), α-tridymite (orthogonal cell unit with space group C2221), coesite (monoclinic cell unit with space group C2/c), keatite (tetragonal cell unit with space group P43212), tridymite (hexagonal cell unit with space group P63/m mc), β-quartz (hexagonal cell unit with space group P6222), and β-cristobalite (cubic cell unit with space group Fd-3m). Highlighting that calcite and α-quartz are the crystalline phases most stable at ambient temperature and pressure conditions, while the space group of vaterite is not well established in the literature [70]. The ATR-FTIR spectra suggest the calcite phase in the CaCO3 particles due to characteristic absorption bands at 874 and 2512 cm− 1.
Table 2
ATR-FTIR characteristic signals of the polymers, silicon dioxide, and calcium carbonate (part I).
Polymer
|
Wavenumber (cm-1)
|
Chemical group and vibrational mode
|
HDPE and LDPE
(pristine and recycled)
|
718
|
-CH2- (Rocking)
|
730
|
-CH2- (Rocking)
|
1306
|
-CH3 (Twisting and wagging)
|
1350
|
-CH3 (Twisting and wagging)
|
1367
|
-CH3 (Wagging)
|
1460
|
-CH- (Bending)
|
2850
|
-CH2- (Symmetric stretching)
|
2920
|
-CH2- (Asymmetric stretching)
|
PP
|
808
|
C–C (Stretching)
|
840
|
-CH- (Rocking)
|
973
|
-CH3 (Rocking)
C–C (Stretching)
|
996
|
CH3 (Rocking)
|
1166
|
C–C (Stretching)
-CH- (Wagging)
-CH3 (Rocking)
|
1376
|
-CH3 (Symmetric bending)
|
1456
|
-CH3 (Symmetric bending)
|
2870
|
-CH3 (Stretching)
|
2920
|
-CH2- (Asymmetric stretching)
|
2950
|
-CH3 (Asymmetric stretching)
|
SiO2
|
796
|
Si-O-Si (Symmetrical stretching)
|
975
|
Si-O-(H-H2O) (Bending)
|
1060
|
Si-O-Si (Asymmetrical stretching)
|
3447
|
- O-H from SiOH and adsorbed H2O (Stretching)
|
HDPE, LDPE, and PP recyclates seem to have a low degradation degree since there is not infrared signal at 1170 and 1167 cm− 1, corresponding to the formation of C-O (carbonyl) from ester groups due to the aging of polyolefin residues after their end-of-life and mechanical recycling steps [24].
Table 2
ATR-FTIR characteristic signals of the polymers, silicon dioxide, and calcium carbonate (continuation).
Material
|
Wavenumber (cm-1)
|
Chemical group and vibrational mode
|
PET
|
721
|
Interactions between polar ester groups and benzene rings
|
794 and 848
|
Vibrations of adjacent two aromatic H in p-substituted rings
|
874 and 973
|
Vibrations of adjacent two aromatic H in 1,2,4,5-tetrasubstituted aromatic rings (in- and out-of-plane bendings)
|
1042 and 1092
|
Vibrations of methylene group and C-O in ester groups
|
1119 and 1247
|
Vibrations of terephthalate group (OOCC6H4-COO)
|
1344, 1411 and 1457
|
C-O (Stretching)
O-H (bending and wagging)
|
1504 and 1577
|
C = C and C–H of phenyl rings (Vibrations of the aromatic rings with stretching)
|
1715
|
C = O of the carboxylic acid groups (Stretching)
|
1960
|
Vibrations of aromatic rings
|
2350
|
CO2 (Axial symmetrical deformation)
|
2969
|
C–H (Symmetrical stretching)
|
3054
|
CH aromatic rings (Symmetric stretching)
|
CaCO3
|
708
|
CO3 (Stretching)
|
874
|
C-O from calcite
|
1409–1460
|
C = O (Asymetric stretching)
O-H (Bending)
|
1805
|
Vibrations of carbonate ions
|
2512
|
C = O from calcite
|
The remarkable intensity of the C = O stretching vibration from carboxylic acid groups at wavenumber 1715 cm− 1 indicates that the PET recyclates present a high degradation degree [24, 71], justifying the elevated value for the MFI (14 g/10 min at 255°C) of the recycled PET. During the mechanical recycling, PET residues can degrade by thermal-oxidative and hydrolytic action combined with water and oxygen molecules, both involving breaking the ester bonds between the terephthalic acid and diethylene glycol units along the polymer backbones. The thermal-oxidative degradation leads to rupture of the polymeric chains, forming carboxyl acid and vinyl ester end groups in the PET. On the other hand, hydrolysis also causes the PET polymer chains, but carboxyl acid and hydroxyl ester end groups are generated by this degradation process [72].
3.3. Principal component analysis (PCA)
The 2D and 3D diagrams of the PC scores obtained by the PCA statistical analysis of the ATR-FTIR data from the pristine and recycled polyethylenes are presented in Fig. 6. The PC1 and PC2 scores correspond to 72.9 % and 8.6 % of the variance among the infrared spectral data from the polyethylenes, respectively. However, the PC scores do not enable the complete distinction of the pristine and recycled polyethylenes without the 5 wt% of contaminants (pHDPE, pLDPE, rHDPE, and rLDPE samples) due to the overlap of the 95 % confidence ellipses. Moreover, the PC1 score evidently has less representativeness over the ATR-FTIR spectra of LDPE. In contrast, the PCA plot using de PC1 and PC3 scores permitted the separation of some plastics, such as pristine polyethylenes, pristine HDPE and recycled LDPE (with or without 5 wt% of contaminants). Also, the contaminant presence reduces the confidence ellipse area for the recycled HDPE, enabling a more effective separation between the recycled HDPE and the samples containing LDPE.
The first three PC scores (PC1, PC2, and PC3) from PCA analysis represent 76.1 % of the total variability for the HDPE/LDPE blends based on recycled and pristine plastics, as can be seen in Fig. 7. Obviously, the addition of the contaminants modifies the format of the confidence ellipses, but the recycled and pristine HDPE/LDPE without contaminants present a similar confidence ellipse. The presence of 5 wt% of silica in the recycled HDPE/LDPE substantially increases the variation of the calculated PC scores, and consequently, the confidence ellipse area is enlarged. According to the 2D diagrams in Fig. 8, the PC1 and PC2 scores do not make it possible to separate the HDPEs, LDPEs, and HDPE/LDPE blends effectively since the groups are not clustered by the PCA analysis, indicating a high similarity of their ATR-FTIR spectra [32].
3.4. Partial Least Squares Linear Regression modified by Competitive Adaptive Reweighted Sampling (CARS/PLS-R)
The value obtained for RMSEP for the CARS-/PLS-R model is very high (Fig. 9), equal to 39.712 wt% of LDPE, and the value of Rpred is equal to 0.331. The predicted results form a straight line with an inclination equal to 1, but with a linear coefficient other than zero as a constant value shifted the model's prediction line, 40 wt% of LDPE, concerning the regression line (built in the multivariate calibration of the CARS/PLS-R prediction model). According to Miller et al. [73], this type of non-agreement between calibration and prediction values may be connected with systematic or random errors in the spectrometer's background signal. Therefore, this high discrepancy suggests that the CARS/PLS-R model is susceptible to slight variations in ATR-FTIR spectral signals related to the heterogeneity of recycled plastics. The high prediction error and low calibration error for the CARS/PLS-R predictive model indicate that the CARS/PLS-R multivariate calibration method can obtain highly calibrated predictive models with low capacity to predict the LDPE content in plastic waste containing HDPE and LDPE mixtures.
The low performance of the CARS/PLS-R model is also associated with the increase in the surface irregularity of the recyclates’ pellets since HDPE increases the viscosity of the HDPE/LDPE blend in the molten state because HDPE has a lower melt flow index than the LDPE at the same temperature. The rise in melt viscosity of HDPE, LDPE, and HDPE/LDPE polymer systems intensifies the shear rate on the surface of the molten material during its passage in the extruder matrix and, consequently, there is the formation of surface defects due to plastic deformation in the extruded polymer. If the shear stress in the extruder matrix is much higher than the melt strength, the defects are no longer superficial and the melt fracture, which is a macroscopic defect, occurs. In addition, the elevation in melt viscosity results in more outstanding adhesion with the machinery, which also intensifies the occurrence of surface defects and fracture of the cast of the extruded profile. In addition, the mixtures of HDPE with LDPE can form immiscible multiphase systems in which there are morphological changes (e.g., deformation, rupture, and coalescence) and interfacial processes (e.g., interfacial relaxation and sliding) between the dispersed polymer droplets and the polymeric matrix under stress shear; both phenomena significantly influence the HDPE/LDPE melt flow within the extruder matrix, leading to changes on the extrudate surface [74–77].
As the ATR-FTIR spectroscopy analyses are performed on the samples' surface, it is of great importance to evaluate their surface roughness. Thus, the Rq roughness of the HDPE and LDPE mixtures was determined from SEM micrographs using computational analysis. This type of essay is based on local brightness variations in the SEM micrograph (i.e., changes in grayscale uniformity) directly associated with the topography of the sample [41, 78]. According to Fig. 10, the Rq roughness results of the HDPE pellets are 100 % higher than Rq values for the LDPE pellets, independently of the plastics being recycled or pristine even though these plastics were extruded under similar conditions. Thus, Rq roughness emphasizes the weight of the topographical peaks and valleys in the results of the surface roughness. It is observed in Fig. 10a that the roughness of the pellets of the pristine LDPE/HDPE blends increases as the HDPE concentration increases in the polymer mixture. When the HDPE content is equal to or higher than 50 wt%, there is a significant increase (> 25%) in the Rq roughness of the pellets of recycled LDPE/HDPE blends (Fig. 10b).
3.5. Partial Least Squares Linear Regression (iPLS-R)
The iPLS-R algorithm found the minimum calibration and prediction errors in the range of 2875–2980 cm− 1 to construct the iPLS-R model, suggesting a close relationship between composition and the amorphous fraction of the HDPE/LDPE blends. The results of the prediction test of the iPLS-R model are shown in Fig. 11. As can be identified, the RMSEP error is equal to 6.479 wt% of LDPE, and the Rpred is 0.995, while there is a low adjustment factor for the recycled blends containing less than 10 wt% of LDPE.
Due to the highest predictive performance, the predictive capability of this iPLS-R model to determine the composition of polymeric blends of recycled HDPE/LDPE containing other materials in its composition was evaluated. In this work, we chose the contaminant concentration of 5 wt% for PP, PET, calcium carbonate and silica, since PP and PET are two of the most present thermoplastics in solid urban waste generated worldwide. Calcite and silica are often used in polymers as fillers to improve the dimensional stability of plastic products to reduce the product's final cost, as these inorganic materials are generally cheaper than the polymers available on the market. The ATR-FTIR spectra of the uncontaminated and contaminated HDPE/LDPE blends obtained with the recycled plastics are present in Supplementary Material.
As shown in Fig. 11, the prediction errors of the iPLS-R model increase significantly when the HDPE/LDPE blends present this percentage of contamination, mainly with PET, PP, and carbonate of calcium in recycled polymer blends. The minor prediction error was obtained in tests involving polymeric blends with silica gel (RMSEP = 7.771 wt% of LDPE) and a prediction error exceeding 16 wt% of LDPE when the plastic samples contain the other three contaminants investigated in this work. In addition, the pronounced calibration and prediction errors are also associated with the increase in the surface roughness of the plastic pellets (Fig. 12). The increase of the surface roughness of the polyethylene pellets due to the elevation of the viscosity of the plastic melt, which is caused by contaminant addition in the recycled polyethylene, leads to more significant uncertainty in the ATR-FTIR spectral measurements.
According to Fig. 13, there is a significant increase in the roughness of the recycled LDPE pellets when it is used silica and PP as contaminants. Silica and calcium carbonate substantially increase the roughness of the recycled HDPE, while PET and PP seem not to affect it. This phenomenon is because the contaminants (principally SiO2 and CaCO3) decrease substantially the melt fluidity of the PE-based systems, increasing their viscosity in the molten state, and consequently, the pellets’ roughness is raised as discussed so far.
3.6. Final remarks
Automatized and efficient processes to classify plastics are essential for the recycling industry to improve the recycling of the principal plastics in the waste generated worldwide. However, there are several difficulties in producing recycled polyethylene residues with technologically attractive properties and competitive prices, such as, for example, the degradative processes that act on the polymer (during and after its lifetime) and the presence of contaminants that often prevents the separation of solid polymeric waste (SPWs) [17, 79]. In addition, the quality control of the separation step, that is, assessing the purity degree of the different plastics separated to be mechanically recycled, plays a fundamental role in obtaining good quality recycled plastic products. However, quantifying the composition of mixtures of HDPE with LDPE is not a simple task since they are only constituted by hydrogen and carbon atoms connected by saturated chemical bonds.
In Brazil and other underdeveloped countries, plastics are recycled mainly by mechanical recycling, which is the most used process to reuse plastic residues [57]. However, the complete separation of the mixtures of HDPE and LDPE residues by the method of density difference, using alcoholic and aqueous solutions, after manual separation, is challenging. In addition, the presence of fillers (and other additives), plastic mixtures, and multilayer polymeric packaging in the plastic waste make this conventional sorting method unfeasible [80]. It is worth mentioning that this separation procedure is currently the cheapest and, consequently, it is the most used for the separation of polyolefin waste in the mechanical recycling process in Brazil [50].
Another aggravating factor for the proper identification and separation of plastics in polymeric packaging waste by regulatory laws. In Brazil, the correct identification of plastic-type in solid plastic waste by ABNT NBR 13.230 does not exceed 30 % of the total food and non-food products sold on the market [81], making the processes characterization and sorting involved in plastic recycling even more difficult.
In our work, the identification of polyethylene recyclates using ATR-FTIR spectroscopy combined with PCA and different PLS-R statistical analyses were investigated in-depth. This methodology would be helpful for the online characterization and sorting of plastic pieces in a large-scale recycling process involving solid plastic waste. However, several limitations were identified in this proposed procedure due to the great complexity associated with the heterogeneity observed for plastic residues, which affect the ATR-FTIR signal associated both by the composition and the surface morphology of the sample.
The objective of the present work was to point out the applicability and the barriers to the usage of the ATR-FTIR spectroscopy, which is a technique that makes it possible to characterize analytically and quickly solid samples, such as solid plastic waste, being suitable to be applied in different industrial sectors. The PCA and PLS-R multivariate analyses show appropriate development of fast, analytical, and automated protocols for classifying polyethylenes and determining the LDPE content in HDPE/LDPE mixtures using infrared measurements. However, the presence of 5 wt% of a contaminant such as another polymer with different chemical composition or microparticles of oxides restricts the capacity and generality of the PCA and PLS-R statistical models for sorting highly heterogeneous mixtures of plastic residues found in real plastic waste. Despite these limitations, the analytical iPLS method proposed here presents more excellent reliability for mixtures of LDPE/HDPE with silica impurities under the evaluated conditions of tests. The PCA method using the PC1 and PC3 plot can be helpful to separate pristine and recycled LDPE and HDPE, depending on the waste composition, showing a latent capacity to be applied as a tool to identify polyethylene-based plastic packages and support the management of the circular economy processes.