Triboelectrostatic separation for granular plastic waste recycling: A review

Study and characterization of virgin and recycled LDPE/PP blends

A Laser Induced Breakdown Spectroscopy (LIBS) technique has been applied for the identification of four widely used plastics, polyethylene terephthalate (PET), high-density polyethylene (PE), polypropylene (PP) and polystyrene (PS), whose recycling is required from commercial and biosafety points of view. The 3rd harmonic (355 nm) nanosecond pulse from an Nd:YAG laser is used to create plasma on the sample surface and identification of the type of the plastic is derived from the plasma emission. Principal Component Analysis (PCA) of the LIBS spectra is employed for the classification of plastics. Distinct methods have been used, apart from principal components of PCA, to further confirm our results. Statistical parameters, viz., Mahalanobis distance (M-distance) and spectral residuals were used for decisive match/no match test which provided successful classification of plastics. Receiver Operating Characteristic (ROC) and Youden's index analyses were carried out to obtain the diagnostic threshold for classification of all four classes of plastics. Sensitivity, specificity, predictive values and discriminative accuracy of the classification tests based on the optimum threshold were calculated. This proves the analytical predictive capabilities of the LIBS technique for plastic identification and classification. The technique of LIBS, in future, can be routinely used in field applications such as plastic waste sorting and recycling.

Analytical predictive capabilities of Laser Induced Breakdown Spectroscopy (LIBS) with Principal Component Analysis (PCA) for plastic classification

An airborne remote sensing case study of synthetic hydrocarbon detection using short wave infrared absorption features identified from marine-harvested macro- and microplastics

Detection of floating plastics from satellite and unmanned aerial systems (Plastic Litter Project 2018)

https://repository.ubn.ru.nl/bitstream/handle/2066/29596/29596.pdf

Th. Kantimm

Papers

NIR - Remote-Sensing and Artificial Neural Networks for Rapid Identification of Post Consumer Plastics