In our previous article we discussed how polymer plastics have become ubiquitous worldwide and include some of the most important and useful materials available. However, significant attention is being directed to recycling these plastics in order to minimize their environmental impact and to reduce the need for petrochemical raw materials used in their manufacture. Properly identifying and separating different recyclable plastics from each other so that they are processed correctly requires a great deal of effort and complex chemical analysis.
Fourier transform near-infrared spectroscopy (FT-NIR) provides a means to identify and analyze various polyethylenes. Two separate studies on different polyethylene materials were performed. The first focuses on classifying polyethylene samples of different densities, and development of a quantitative prediction of polyethylene densities. The second illustrates the ability of the FT-NIR analyzer to quantify the amount of polyethylene in polypropylene copolymers.
In the first study, three sets of polyethylene samples with distinct density ranges were analyzed using an FT-NIR analyzer, integrating sphere module with a spinning sample cup. The materials were classified as either linear low density polyethylene (LLDPE, density 0.9170-0.9200 g/cm3); medium density polyethylene (MDPE, density 0.9260- 0.9400g/cm3); or high density polyethylene (HDPE, density range >0.941g/cm3). Each of the samples was scanned in the range between 10,000 and 4000 cm-1. A discriminant analysis chemometric model was developed. The first derivative spectra were analyzed between 6000 and 5700 cm-1 where there was clear spectral difference between the three groups of materials. A Norris derivative smoothing filter was applied to the spectra before the chemometric modeling.
The principal component scores plot showed excellent separation of the different density classes. Principal components described the spectral variation in a discriminant analysis. The first principal component described most of the variation within the standard spectra and each subsequent principal component described the remaining variation. The spectra was plotted against the first and second principal component. The separation between the different density classes of polyethylene indicated these materials can be successfully classified.
Quantitative Analysis Performed
In addition to qualitatively classifying different polyethylene materials, a quantitative analysis was performed on the MDPE samples. The 11 samples of MDPE ranging in density from 0.9340 to 0.9395 g/cm3 were re-analyzed using a partial least squares (PLS) chemometric model. The unprocessed spectra were analyzed in the range from 10,000 to 6200 cm-1 using a 1 point baseline correction at 8840 cm-1. A plot of the chemometric model’s calculated values vs. actual values indicates that density can be accurately predicted. Selected validation spectra provide a root mean standard error of prediction (RMSEP) of 0.0005 g/cm3 with a correlation coefficient of 0.97699.
Polypropylene films containing ethylene as a copolymer have better clarity and lower melting points than polypropylene alone. These characteristics make such materials useful in low temperature heat-sealable applications. Melting points are linearly related to ethylene content, which makes ethylene an important measurable component. For the second study, a series of 28 random and impact ethylene-polypropylene copolymer samples containing various amounts of ethylene (2% to 16%) were scanned with an FT-IR system.
The unsmoothed unprocessed spectra were analyzed between 9000 and 4500 cm-1 using a one point baseline correction at 9029 cm-1. A plot of the predicted vs. actual values of ethylene concentration in polypropylene demonstrated an excellent fit. The model produced a RMSEP of less than 0.4% ethylene with a correlation coefficient of 0.99764.
The feasibility of both qualitative and quantitative analysis of polymeric materials using the FT-NIR analyzer has been clearly demonstrated. Specifically, polyethylene was correctly separated into different groups. Additionally, the density of MDPE was accurately predicted, and the levels of blends in an ethylene-polypropylene copolymer were accurately predicted.
For more specifics, including a chart of the density and classification of polyethylene materials analyzed, the spectra, principal component scores plot, and regression plots, read the Density and Copolymer Content in Polyethylene Samples by FT-NIR Spectroscopy application note.