Identification and quantitation of semi-crystalline microplastics using image analysis and differential scanning calorimetry.
Mauricio Rodríguez ChialanzaIgnacio SierraAndrés Pérez ParadaLaura FornaroPublished in: Environmental science and pollution research international (2018)
There are several techniques used to analyze microplastics. These are often based on a combination of visual and spectroscopic techniques. Here we introduce an alternative workflow for identification and mass quantitation through a combination of optical microscopy with image analysis (IA) and differential scanning calorimetry (DSC). We studied four synthetic polymers with environmental concern: low and high density polyethylene (LDPE and HDPE, respectively), polypropylene (PP), and polyethylene terephthalate (PET). Selected experiments were conducted to investigate (i) particle characterization and counting procedures based on image analysis with open-source software, (ii) chemical identification of microplastics based on DSC signal processing, (iii) dependence of particle size on DSC signal, and (iv) quantitation of microplastics mass based on DSC signal. We describe the potential and limitations of these techniques to increase reliability for microplastic analysis. Particle size demonstrated to have particular incidence in the qualitative and quantitative performance of DSC signals. Both, identification (based on characteristic onset temperature) and mass quantitation (based on heat flow) showed to be affected by particle size. As a result, a proper sample treatment which includes sieving of suspended particles is particularly required for this analytical approach.
Keyphrases
- high resolution
- ms ms
- mass spectrometry
- human health
- liquid chromatography tandem mass spectrometry
- liquid chromatography
- high density
- high performance liquid chromatography
- tandem mass spectrometry
- bioinformatics analysis
- risk assessment
- computed tomography
- risk factors
- high throughput
- single molecule
- optical coherence tomography
- pet ct
- electronic health record
- data analysis
- life cycle
- pet imaging