The Chinese Journal of Process Engineering•2026•DOI: 10.12034/j.issn.1009-606X.225225
The non-Newtonian rheological properties of plastic melts are critical for regulating plastic processing, molding, and recycling processes, ensuring processing stability and product performance. However, rheological data for commonly used plastics and their blends remain incomplete. This study combined experimental testing and theoretical modeling to investigate the rheological behaviors of four pure plastics—polypropylene (PP), polyethylene (PE), polystyrene (PS), and acrylonitrile-butadiene-styrene copolymer (ABS)—and three binary blend systems: PE/ABS, PP/ABS, and PS/ABS. Rheological tests were conducted using a rheometer over a shear rate range of 0.1–100 s⁻¹ and temperatures from 180°C to 250°C. Results showed that the flow behavior index n was less than 1 for all samples, and apparent viscosity decreased significantly with increasing shear rate, indicating clear shear-thinning behavior. The consistency coefficient K followed the Arrhenius relationship with temperature, and melt viscosity decreased as temperature increased. The study quantitatively characterized the relationship between the mass fraction m (0.5 < m ≤ 1) of the main component in binary blends and melt viscosity. Based on experimental data, a component correction term was introduced into the traditional power-law model to construct a constitutive equation that simultaneously describes the effects of shear rate, temperature, and component fraction on melt viscosity. The average relative error between model predictions and experimental values was only 5.90%. These rheological data and the modified constitutive equation provide important theoretical support and data reference for optimizing process parameters in waste plastic recycling and injection molding.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202607001
Co-combustion of municipal solid waste (MSW) and sewage sludge (SS) offers a promising route for synergistic waste management, yet pollutant release dynamics and environmental trade-offs remain inadequately characterized. This study systematically investigated the combustion behavior, pollutant emissions, and environmental impacts of MSW-SS blends at 850, 950, and 1050 °C with varying SS mass fractions (0–100%). Machine learning models, particularly artificial neural networks (ANN), were optimized to predict pollutant generation, and SHAP analysis identified key influencing factors. Results demonstrated that combustion temperature and blending ratio significantly affected burnout efficiency, with temperature exerting a more pronounced effect. An SS proportion of 20% yielded favorable combustion performance. Among pollutants, N2O and C2H4 emissions were significantly influenced by temperature, blending ratio, and their interaction, indicating high sensitivity to operating conditions. CO and C6H6 were primarily affected by blending ratio, while C7H8 responded to both temperature and blending ratio. N2O and CH4 were predominantly released during the initial combustion stage; elevated temperatures markedly suppressed N2O formation, and co-combustion generally reduced CH4 emissions. A 20% SS blend effectively reduced SO2 emissions, and NO synergistic reduction was optimal at 950 °C. Emissions of CO, C2H4, C6H6, and C7H8 exhibited antagonistic behavior under co-combustion. The ANN model accurately predicted pollutant concentrations, with combustion temperature, volatile matter, and fixed carbon content identified as critical factors. Environmental impact assessment revealed that higher temperatures reduced global warming potential (GWP) and photochemical ozone creation potential (POCP), while lower MSW proportions decreased POCP but increased GWP and acidification potential (AP). Integrating combustion performance, pollutant release, and environmental impacts, an SS proportion of 20% is recommended for optimized co-combustion.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60655-X
Defect-induced nonradiative recombination critically restricts the power conversion efficiency (PCE) and stability of perovskite solar cells (PSCs). Lewis base additives show great promise in defect passivation, but current screening methods rely heavily on empirical trial and error and lack clear design principles, making it difficult to efficiently discover high-performance candidate materials. Here, we present a machine learning (ML) framework to intelligently screen Lewis base molecules for defect passivation. We trained six ensemble models on a dataset of 146 experimental data points, with Light Gradient Boosting Machine (LightGBM) yielding the best classification performance (87% accuracy). Shapley Additive Explanations (SHAP) interpretability analysis subsequently identifies the highest occupied molecular orbital (HOMO) energy (−7.5 to −6.3 eV), additive concentration (2.5 to 6.5 mg/mL), and simplified molecular backbones (O atom ≤ 2, C atom < 5) as critical design criteria. The ML prediction was experimentally validated: (S)-pyrrolidine-3-carboxylic acid ((S)-PCA) and 2-methyl-1,3-cyclopentanedione (MCPD) (Class Ⅱ) improved PCE by 2.22% and 2.01%, respectively, while 3-hydroxymethyl-3-methylbutanenitrile (3-HMBN) (Class Ⅰ) showed minimal gain. Density functional theory (DFT) calculations further confirmed the stronger binding affinities and elevated defect formation energies of Class Ⅱ additives. Notably, the champion (S)-PCA device achieved a PCE of 24.05%. This work established an ML-accelerated paradigm for the rational design of defect passivators, bridging data science and photovoltaics.