Multi-sensor characterization for an improved identification of polymers in WEEE recycling

Polymers represent around 25% of total waste from electronic and electric equipment. Any successful recycling process must ensure that polymer-specific functionalities are preserved, to avoid downcycling. This requires a precise characterization of particle compounds moving at high speeds on conveyor belts in processing plants. We present an investigation using imaging and point measurement spectral sensors on 23 polymers including ABS, PS, PC, PE-types, PP, PVC, PET-types, PMMA, and PTFE to assess their potential to perform under the operational conditions found in recycling facilities. The techniques applied include hyperspectral imaging sensors (HSI) to map reflectance in the visible to near infrared (VNIR), short-wave (SWIR) and mid-wave infrared (MWIR) as well as point Raman, FTIR and spectroradiometer instruments. We show that none of the sensors alone can identify all the compounds while meeting the industry operational requirements. HSI sensors successfully acquired simultaneous spatial and spectral information for certain polymer types. HSI, particularly the range between (1600-1900) nm, is suitable for specific identification of transparent and light-coloured (non-black) PC, PE-types, PP, PVC and PET-types plastics; HSI in the MWIR is able to resolve specific spectral features for certain PE-types, including black HDPE, and light-coloured ABS. Fast-acquisition Raman spectroscopy (down to 500 ms) enabled the identification of all polymers regardless their composition and presence of black pigments, however, it exhibited limited capacities in mapping applications. We therefore suggest a combination of both imaging and point measurements in a sequential design for enhanced robustness on industrial polymer identification.

Impact of fluorescence on Raman remote sensing of temperature in natural water samples

A comprehensive investigation into the impact of spectral baseline on temperature prediction in natural marine water samples by Raman spectroscopy is presented. The origin of baseline signals is investigated using principal component analysis and phytoplankton cultures in laboratory experiments. Results indicate that fluorescence from photosynthetic pigments and dissolved organic matter may overlap with the Raman peak for 532 nm excitation and compromise the accuracy of temperature predictions. Two methods of spectral baseline correction in natural waters are evaluated: a traditional tilted baseline correction and a new correction by temperature marker values, with accuracies as high as ± 0.2°C being achieved in both cases.

A LIDAR-Compatible, Multichannel Raman Spectrometer for Remote Sensing of Water Temperature

The design and operation of a custom-built LIDAR-compatible, four-channel Raman spectrometer integrated to a 532 nm pulsed laser is presented. The multichannel design allowed for simultaneous collection of Raman photons at two spectral regions identified as highly sensitive to changes in water temperature. For each of these spectral bands, the signals having polarization parallel to (∥) and perpendicular to (⟂), the excitation polarization were collected. Four independent temperature markers were calculated from the Raman signals: two-colour(∥), two-colour(⟂), depolarization(A) and depolarization(B). A total of sixteen datasets were analysed for one ultrapure (Milli-Q) and three samples of natural water. Temperature accuracies of ±0.4 °C-±0.8 °C were achieved using the two-colour(∥) marker. When multiple linear regression models were constructed (linear combination) utilizing all simultaneously acquired temperature markers, improved accuracies of ±0.3 °C-±0.7 °C were achieved.