The depletion of fossil resources and the growing demand for plastic waste reduction has put industries and academic researchers under pressure to develop increasingly sustainable packaging solutions that are both functional and circularly designed. In this review, we provide an overview of the fundamentals and recent advances in biobased packaging materials, including new materials and techniques for their modification as well as their end-of-life scenarios. We also discuss the composition and modification of biobased films and multilayer structures, with particular attention to readily available drop-in solutions, as well as coating techniques. Moreover, we discuss end-of-life factors, including sorting systems, detection methods, composting options, and recycling and upcycling possibilities. Finally, regulatory aspects are pointed out for each application scenario and end-of-life option. Moreover, we discuss the human factor in terms of consumer perception and acceptance of upcycling.

The recovery and recycling of textile waste is becoming urgent since textiles are generating more and more waste. In one year, about 92 million tons of textile waste are produced and the fashion industry accounts for 58 million tons of plastic waste per year. Several different synthetic fibres are used in textiles, thanks to their excellent processability and mechanical properties, but on the other hand, the difficulties linked to their end of life and the release of microplastics from them during washing is currently a cause of great concern. In this context, policy actions have been aimed at promoting recycling of waste and replacing fossil-based fibres with biobased fibres. The current review, considering both scientific papers published on international journals and web sources, considers the sorting of textiles and the possible recycling of polyesters, polyamides and acrylics. Nevertheless, the contamination and presence of mixed fibres in fabrics is another issue to face for recycling. Methodologies to solve the issue linked to the presence of elastane, present in the stretch fabrics, as well as the possibility of recycling textiles in the non-woven and composite sector are investigated. Moreover, chemical recycling and enzymatic recycling of fossil polymers are also considered. Thanks to the comprehensive scheme of this review, it is possible to deduce that, while the use of biobased materials should rapidly increase in textile applications, the perspective of recycling materials obtained from waste textile into durable and/or high-performance products seems the most promising.

The demand for proteins is constantly increasing and green extraction methodologies are needed to achieve environmental sustainability goals. The recovery of the by-products of the agri-food chain has also become a priority from a circular economy perspective. Some by-products are still little exploited for the extraction of proteins, such as coffee by-products. In this work, various innovative extraction technologies were applied to recover the protein fraction from the non-compliant coffee green beans (CGB), using a methodological approach that allowed to correlate the process parameters with the final quality of the extracted proteins. The ultrasound-assisted extraction (UAE) technique has been shown to have a minor impact on the quality of the proteins, thanks to the possibility of refrigerating the system, while the microwave-assisted extraction (MAE) shows a certain degree of degradation due to the high temperatures reached. The results indicate that strict temperature control is required during alkaline extraction to preserve the quality of the protein fraction.

Power ultrasound application has been proven to intensify the extraction of biocompounds from plant materials. In this work, the ultrasound-assisted extraction (UAE) of polyphenols from olive pomace (OP) has been studied at three different scales: laboratory (batch, 400 W, 0 barg), medium (continuous, 1000 W, 1.0 barg), and pilot (continuous, 2000 W, 1.0 barg) taking into consideration the influence of technological parameters: extraction time (s), solvent to solid ratio (mL/g), mixture pH, and acoustic parameters: amplitude (µm), intensity (W/cm²), and applied energy (Wh). A central composite design was used to optimize the UAE at laboratory scale (0.2 kg). The optimal conditions were: time: 490 s; ratio: 2.1 mL H₂O/g OP; pH: 5.6 at an acoustic amplitude of 46 µm for a maximum extraction yield of 3.6 g GAE/L of extract. At medium scale (2.2 kg) the UAE was carried out using amplitudes from 41 to 57 µm. The effect of the pressure (1.0 barg) on the UAE was positive, in terms of higher extraction yield (2.9 g GAE/L) and faster extraction rates compared to the non-pressurized UAE (2.5 g GAE/L), however, the extraction yield was lower than the one observed at laboratory scale. At pilot scale (120 kg), the UAE involved different ultrasound constellations (booster + sonotrode) to deliver the ultrasound energy at different acoustic intensities from 23 to 57 W/cm². The acoustic intensity (W/cm²) exerts an important effect on the extraction yield, and should be tailored to each process scale. The highest yield obtained at pilot-scale was 3.0 g GAE/L, and it was 58% higher than the one observed in the conventional extraction without ultrasound assistance (stirring and heating). In all tests, regardless of the scale, higher yields were observed between 80 and 85 °C. The application of this technology at the industrial scale to evaluate if the improvement of the extraction caused by the application of ultrasound could is still important when other operations like centrifugation, ultrafiltration, and reverse osmosis are included in the system and to evaluate its techno-economic feasibility.

Identifying the material composition of objects is crucial for many recycling sector applications. Traditionally, object classification relies either on hyperspectral imaging (HSI), which analyses the chemometric properties of objects to infer material types, or on RGB imaging, which captures an object’s visual appearance and compares it to a reference sample. While both approaches have their strengths, each also suffers from limitations, particularly in challenging scenarios such as robotic municipal waste sorting, where objects are often heavily deformed or contaminated with various forms of dirt, complicating material recognition. This work presents a novel method for material-based object classification that jointly exploits HSI and RGB imaging. The proposed approach aims to mitigate the weaknesses of each technique while amplifying their respective advantages. It involves the real-time alignment of HSI and RGB data streams to ensure reliable result correlation, alongside a machine learning framework that learns to exploit the strengths and compensate for the weaknesses of each modality across different material types. Experimental validation on a municipal waste sorting facility demonstrates that the combined HSI–RGB approach significantly outperforms the individual methods, achieving robust and accurate classification even in highly challenging conditions.

An industrial system, based on a probe for turbidity measurement and a model, has been developed and tested for the in-line monitoring of nanoparticle synthesis reactions, thus providing information on the reaction progress and particle size. Real-time turbidity measurements, reliably indicating the reaction end and allowing run-time variations to be detected, were obtained for three silica nanoparticle syntheses. The system, initially built for a research laboratory reactor of 6 L, was successfully upscaled to an industrial 160 L reactor, simply by adapting the probe’s mounting components. In a further upscaling process, transferability of the model from the smaller to the larger reactor, giving accurate particle size predictions, was achieved. In addition, a combined model, developed from the first two reactions, predicted the particle size in the third reaction without first needing to obtain any data for the model from this reaction. The combined model’s predictions showed an average relative error of 18% with respect to the measured particle size. The probe was resistant to harsh reaction conditions at a temperature of 90 °C with concentrated acids, making the system potentially useful in industrial nanoparticle production.