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Small polymer particles with a diameter of less than 5 mm called microplastics find their way into the environment from polymer debris and industrial production. Therefore a method is needed to identify and quantify microplastics in various environmental samples to generate reliable concentration values. Such concentration values, i.e. quantitative results, are necessary for an assessment of microplastic in environmental media. This was achieved by thermal extraction in thermogravimetric analysis (TGA), connected to a solid-phase adsorber. These adsorbers were subsequently analysed by thermal desorption gas chromatography mass spectrometry (TDS-GC-MS). In comparison to other chromatographic methods, like pyrolyse gas chromatography mass spectrometry (Py-GC-MS), the relatively high sample masses in TGA (about 200 times higher than used in Py-GC-MS) analysed here enable the measurement of complex matrices that are not homogenous on a small scale. Through the characteristic decomposition products known for every kind of polymer it is possible to identify and even to quantify polymer particles in various matrices. Polyethylene (PE), one of the most important representatives for microplastics, was chosen as an example for identification and quantification.
Using thermogravimetric analysis (TGA) with a solid-phase adsorber for thermal extraction, followed by subsequently analysing the adsorber with thermo-desorption gas chromatography mass spectrometry (TDS-GCMS) enables measurement of polymer degradation under oxidizing atmosphere, and the identification of certain complex hydrocarbon degradation products by chromatographic separation and defined mass patterns. This technique, thermal-extraction desorption gas chromatography mass spectrometry (TED-GCMS) was used to investigate the thermo-oxidative degradation of PA 66 and PA 66 doped with 2 wt% of metal oxide particles. In TGA pure PA 66 formed more residue under an oxidizing atmosphere than an inert one. In contrast to the measurements under inert atmosphere, several condensed aromatic species containing nitrogen could be identified in thermo-oxidative measurements. These degradation products were formed through condensation reactions of primary amides originating from imide hydrolysis. The formation of such highly condensed species also causes higher char formation. Four metal oxides have shown an impact on the thermo-oxidative degradation of PA 66: Fe2O3 on η-Al2O3 < pure Fe2O3 = Fe2O3 on γ-Al2O3 < pure ZnO. For ZnO even a char-stabilizing effect could be observed. A catalytic effect of these metal oxides causes more condensed cyclopentanone and pyridine derivates. Thus, more water is formed and released, resulting in increased hydrolysis of the imides and degradation at lower temperatures.
In this work a novel iterative method for isothermal cure kinetic modelling of an epoxy resin system using differential scanning calorimetry (DSC) technique is presented. To reach the isothermal cure temperature, the sample has to be heated up from ambient temperature. This is commonly done with very high heat-up rates to minimise the time the sample reacts at temperatures other than the desired one. However, during heat up with high heating the amount of released energy rates cannot be measured directly because the shape of baseline is unknown. This means that the cure state at the beginning of the isothermal stage is unknown. For fast curing systems this unknown cure state causes significant inaccuracies in cure kinetics modelling.
The presented iterative approach attempts to estimate the released enthalpy during heat-up of an isothermal run through an iterative numerical modelling of the heat-up phase. In each iteration the algorithm starts by estimating the enthalpy released during heat-up based on the recorded temperature profile and the calibrated model of the previous iteration. At the same time, it estimates the degree of cure at the end of the heat-up phase. Once the initial cure state is known the total heat of enthalpy can be recalculated for the current iteration. Subsequently the degree of cure and curing rate are re-evaluated with the newly estimated total enthalpy and used for determining the kinetics parameters. This is done by simultaneous fitting of the selected model to all experimental heat flow curves using a non-linear nonrestricted multivariable fitting method. The model with these new parameters is used again to estimate the released enthalpy and cure degree during the heat-up phase. The described loop is repeated until a predefined convergence criterion is satisfied.
For modelling the reaction kinetics, the Kamal–Sourour equation accompanied with Rabinowitch approach to consider the diffusion effects is used. The diffusion reaction rate is modelled by the free volume model proposed by Huguenin and Klein. DiBenedetto model is applied to predict the Evolution of glass transition temperature against the degree of cure. In order to compensate the effect of the Initial values in the model’s calibration, the algorithm is implemented in a routine, which assesses the quality of the fitting and consequently selects the cure kinetics parameter. The described algorithm and the Routine are implemented in MATLAB. This paper demonstrates the application of this approach for using cure kinetics modelling to predict the degree of cure and the glass transition temperature. It supports the obtained results with validation tests using isothermal, dynamic and combined temperature profiles.
For analysis of the gaseous thermal decomposition products of polymers, the common techniques are thermogravimetry, combined with Fourier transformed infrared spectroscopy (TGA–FTIR) and mass spectrometry (TGA–MS). These methods offer a simple approach to the decomposition mechanism, especially for small decomposition molecules. Complex spectra of gaseous mixtures are very often hard to identify because of overlapping signals. In this paper a new method is described to adsorb the decomposition products during controlled conditions in TGA on solid-phase extraction (SPE) material: twisters. Subsequently the twisters were analysed with thermal desorption gas chromatography mass spectrometry (TDS–GC–MS), which allows the decomposition products to be separated and identified using an MS library. The thermoplastics polyamide 66 (PA 66) and polybutylene terephthalate (PBT) were used as example polymers. The influence of the sample mass and of the purge gas flow during the decomposition process was investigated in TGA. The advantages and limitations of the method were presented in comparison to the common analysis techniques, TGA–FTIR and TGA–MS.
Durch den stetig steigenden Einsatz von Plastikprodukten sind unerwünschte Kunststofffunde in verschiedenen Umweltmatrices mittlerweile allgegenwärtig. Besonderes Interesse erfahren die Kunststoffpartikel von einer Größe kleiner 5 mm, sogenannte Mikroplastik (MP) Partikel. Es werden aktuell Risiken für Mensch und Umwelt diskutiert, die von diesen Partikeln ausgehen.
n order to determine the relevance of microplastic particles in various environmental media, comprehensive investigations are needed. However, no analytical method exists for fast identification and quantification. At present, optical spectroscopy methods like IR and RAMAN imaging are used. Due to their time consuming procedures and uncertain extrapolation, reliable monitoring is difficult. For analyzing polymers Py-GC-MS is a standard method. However, due to a limited sample amount of about 0.5 mg it is not suited for analysis of complex sample mixtures like environmental samples. Therefore, we developed a new thermoanalytical method as a first step for identifying microplastics in environmental samples. A sample amount of about 20 mg, which assures the homogeneity of the sample, is subjected to complete thermal decomposition. The specific degradation products of the respective polymer are adsorbed on a solid-phase adsorber and subsequently analyzed by thermal desorption gas chromatography mass spectrometry. For certain identification, the specific degradation products for the respective polymer were selected first. Afterwards real environmental samples from the aquatic (three different rivers) and the terrestrial (bio gas plant) systems were screened for microplastics. Mainly polypropylene (PP), polyethylene (PE) and polystyrene (PS) were identified for the samples from the bio gas plant and PE and PS from the rivers. However, this was only the first step and quantification measurements will follow.