No abstract

No abstract

β-Fe2O3/CuO nanosystems were synthesised by using a two-step plasma-assisted strategy. β-Fe2O3 nanostructures (host) were initially deposited by plasma assisted-chemical vapour deposition (PA-CVD) on indium tin oxide (ITO) substrates. Subsequently, CuO nanoparticles (NPs, guest) were over-deposited on host matrices by means of radio frequency (RF) sputtering under mild conditions. The combined use of\ud structural, morphological and chemical analyses evidenced the formation of pure and homogeneous β-Fe2O3/CuO systems possessing a high dispersion of CuO NPs in/on β-Fe2O3 hosts. The target nanomaterials were characterized by an intimate contact between the two oxides, with CuO NP size and tuneable content as a function of sputtering time. These features, along with the tailored nano-organization, make the present β-Fe2O3/CuO nanosystems attractive candidates for diverse technological applications involving solar light harvesting

Valorisation of the urban plastic waste in high-quality recyclates is an imperative challenge in the new paradigm of the circular economy. In this scenario, a key role in the improvement of the recycling process is exerted by the optimization of waste sorting. In spite of the enormous developments achieved in the field of automated sorting systems, the quest for the reduction of cross-contamination of incompatible polymers as well as a rapid and punctual sorting of the unmatched polymers has not been sufficiently developed. In this paper, we demonstrate that a miniaturized handheld near-infrared (NIR) spectrometer can be used to successfully fingerprint and classify different plastic polymers. The investigated urban plastic waste comprised polyethylene (PE), polypropylene (PP), poly(vinyl chloride) (PVC), poly(ethylene terephthalate) (PET), and poly(styrene) (PS), collected directly in a recycling plastic waste plant, without any kind of sample washing or treatment. The application of unsupervised and supervised chemometric tools such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) on the NIR dataset resulted in a complete classification of the polymer classes. In addition, several kinds of PET (clear, blue, coloured, opaque, and boxes) were correctly classified as PET class, and PE samples with different branching degrees were properly separated.