Efficient use of secondary raw materials and the development of electrified refining processes are both key enablers of more sustainable and circular hydrometallurgy. Waste printed circuit boards (PCBs), common e-waste deriving from devices such as smartphones and computers, are a prime example of a secondary feed that societies produce in increasing amounts and that contain relatively high concentration of key metals like copper, tin, and gold. This work advances hydrometallurgical processing of PCBs by showing how the recently developed electrochemically-assisted aqueous reduction (EAR) can achieve selective gold recovery from cyanide-free, cupric chloride PCB leachates. In the current study, both copper and gold are leached simultaneously from PCBs in cupric chloride media. Results show that EAR can recover gold from the leachate, with deposits containing up to ≥ 99 wt% gold, despite gold being present at several orders of magnitude lower concentration compared to more abundant metals (e.g. Cu, Fe, Al, Ni, Zn, Sn). This is the first study employing EAR in a real leach solution and thus provides insight into the performance of such tailored electrochemical recovery methods in complex solution matrixes.
Closed-loop recycling is critical for establishing a circular economy, diverting textile waste from landfill and reducing the environmental impacts of the textile industry. Although mechanical recycling is an established route for textile waste, severe fibre fragmentation limits the use of recovered material in high value yarn spinning applications. Using a controlled, laboratory mechanical recycling process, this study establishes a novel structural hierarchy to maximise fibre length outcomes from textile waste feedstocks. Thirteen bespoke 100 % cotton fabrics were produced to systematically isolate the impacts of fibre type, yarn spinning configuration, yarn count and fabric construction on the ease of fabric opening and spinnable fibre yield. The manual Fibre Array method (ASTM D1440) was utilised for fibre length distribution measurements to overcome the optical detection limits associated with Fibrogram based methods, capturing the true short fibre content of mechanically recycled material and providing a detailed assessment of the recoverable length-based value.It was demonstrated that fabric interlacing density was the primary predictor of mechanical opening efficiency, displaying a strong negative correlation (r = −0.94) with the degree of fabric opening in woven structures. Conversely, single jersey knits exhibited the highest opening resistance and significantly reduced fibre length retention. Extra Long Staple Fibres, combed yarns, coarser yarn counts and ring spun configurations enhanced fibre length outcomes under these testing conditions, with up to 70 % of the recovered fibre mass from low-interlacement weaves retaining sufficient length (> 12.7 mm) for staple yarn spinning. By elucidating the impact of structural variables on end-of-life recovery, this study provides actionable Design for Recycling principles and valuable insights to aid the development of structure-based automated textile sorting systems, ultimately facilitating the transition toward high value fibre-to-fibre circularity.
Characterization of polymer blends and their additives is an essential step towards improving plastic recycling rates. Hyperspectral imaging (HSI) has previously demonstrated effectiveness for quantification of individual additives in polymer blends. This study proposes a deep learning approach to enable the identification and quantification of multiple additives in polymer blends simultaneously. Polypropylene (PP)-based blends containing polyethylene (PE), talc and phosphorous flame retardant (B631C) were prepared for HSI data acquisition. For the quantification task, the regression model achieved high R2 values of 0.829–0.937 with low RMSE values of 1.010–2.737 wt%. For the identification task, the multi-label classification model achieved an accuracy of 0.934 ± 0.048. Threshold sensitivity analysis demonstrated a trade-off between B631C detection sensitivity in low-concentration PP/B631C blends and the false-positive rate for neat PP as the decision threshold was varied. The findings of this study provide a foundation for the further development of deep learning frameworks for advanced polymer waste profiling to support appropriate recycling pathway selection towards closed-loop manufacturing.
Plastic waste poses a growing environmental burden, and its conversion into useful producer gas using CO2 as a reactive gasifying agent offers a route that couples waste valorization with the use of CO2 as a reactive gasifying agent. In this study, steam-assisted CO2 gasification of waste polypropylene (PP) was investigated in a bench-scale downdraft fixed-bed gasifier equipped with an in-situ activated carbon (AC) bed to produce H2-rich producer gas while suppressing tar formation. The effects of the CO2/C ratio, steam-to-carbon (S/C) ratio, and reaction temperature on producer gas composition, H2/CO ratio, tar reduction, and the apparent gas-phase CO2 deficit were evaluated. The AC bed functioned as a multifunctional medium, promoting tar cracking and steam reforming while physically retaining tar condensates and char particles. Compared with the AC-free condition, the AC bed reduced tar in gas from 4410.3 to 22.1 mg/Nm3, corresponding to 99.5% removal, and increased the H2 and CO concentrations by 2.5-fold, to 51.9 vol%, and 3.0-fold, to 11.9 vol%, respectively. GC-MS analysis of the residual tar further revealed that the AC bed suppressed the growth of light aromatics into heavier polycyclic aromatic hydrocarbons. The CO2/C ratio effectively tuned the H2/CO ratio from 2.4 to 6.9 and an apparent gas-phase CO2 deficit of 90.9% was determined using an N2 tracer method. These results demonstrate that CO2/steam gasification of waste PP over an in-situ AC bed can combine tar suppression and producer-gas upgrading in a waste-to-gas process using CO2 and steam as gasifying agents.
