It is crucial to realize the municipal solid waste (MSW) classification in terms of its treatments and disposals. Deep learning used for the classification of residual waste and wet waste from MSW was considered as a promising method. While few studies reported using the method of deep learning with transfer learning to classify organic waste and residual waste. Thus, this study aims to discuss the effect of the transfer learning on the performance of different deep learning structures, VGGNet-16 and ResNet-50, for the classification of organic waste and residual waste, which were compared in terms of the training time, confusion matric, accuracy, precision, and recall. In addition, the algorithms of PCA and t-SNE were also adopted to compare the representation extracted from the last layer of various deep learning models. Results indicated that transfer learning could shorten the training time and the training time of various deep learning follows this order: VGGNet-16 (402 s) > VGGNet-16 with TL (272 s) > ResNet-50 (238 s) > ResNet-50 with TL (223 s). Compared with the method of PAC, waste representations were better separated from high dimension to low dimension by t-SNE. The values of organic waste in terms of F1 score follows this order: ResNet-50 with transfer learning (97.8%) > VGGNet-16 with transfer learning (97.1%) > VGGNet-16 (95.0%) > ResNet-50 (92.5%).Therefore, the best performance for the classification of organic and residual waste was ResNet-50 with transfer learning, followed by VGGNet-16 with transfer learning and VGGNet-16, and ResNet-50 in terms of accuracy, precision, recall, and F1 score.

Fe–Ce-MOFs with a rice-grain-like morphology were successfully obtained via hydrothermal synthesis, where ferric chloride (FeCl₃) and cerium nitrate [Ce(NO₃)₃] served as the metal precursors and terephthalic acid (PTA) acted as the organic coordinating ligand. The effects of the Fe:Ce molar ratio, (Fe/Ce):PTA ratio, reaction duration, and synthesis temperature on adsorption performance of the Fe–Ce-MOFs were systematically studied. A comprehensive evaluation was conducted on the removal of fluoride and phosphate ions from aqueous solution. Under optimized conditions, the maximum adsorption capacities of Fe–Ce-MOFs for fluoride and phosphate reached 183.82 mg g⁻¹ and 110.74 mg g⁻¹, respectively. Adsorption data correlated strongly with the Langmuir isotherm, were best represented by the pseudo-second-order kinetic model, and were identified as a spontaneous and endothermic reaction. After three regeneration cycles, the adsorbent still maintained high removal efficiencies for fluoride (85.17%) and phosphate (47.34%) removal. In practical wastewater treatment, removal efficiencies of 92.04% for fluoride and 93.87% for phosphate were achieved. Mechanistic studies revealed that fluoride removal was dominated by electrostatic attraction and hydroxyl–fluoride ion exchange, whereas phosphate removal was attributed to the generation of inner-sphere complexes involving PO₄³⁻ and Fe/Ce active sites. This study not only elucidates the synergistic mechanism of fluoride and phosphate elimination by Fe–Ce-MOFs but also provides theoretical guidance and application prospects for the development of highly efficient and stable bimetallic MOF-based adsorbents for environmental remediation.

To promote the bone repair ability of drug-loaded scaffolds, poly(lactic acid) (PLA)/graphene oxide (GO)/Salvianolic acid B (Sal-B)/aspirin (ASA) dual drug-loaded biomimetic composite scaffolds were prepared. The results showed that the addition of these two drugs delayed the gel formation of the composite system, but a biomimetic nanofiber structure could still be obtained by extending the gel time. The addition of Sal-B increased the hydrophilicity of the scaffold, while an increase in ASA reduced the porosity. Dual drug-loaded scaffolds had good haemocompatibility and synergically promoted the proliferation of MC3T3-E1 cells and enhanced alkaline phosphatase activity. Sustained-release experiments of the two drugs showed that the presence of ASA slowed the cumulative release of Sal-B, while Sal-B promoted the release of ASA. Kinetic modeling showed that the release of both drugs conforms to the Korsmeyer–Peppas model, but Sal-B conforms to the Fick diffusion mechanism and ASA follows Fick diffusion and carrier swelling/dissolution.

In this study, a hierarchical rod-assembled, polypyrrole-modified Ce–Al–La trimetallic metal–organic framework (Ce–Al–La–MOFs@PPy) composite was successfully synthesized through a solvothermal method combined with in situ polymerization for efficient fluoride removal from aqueous solutions. The incorporation of conductive polypyrrole (PPy) into the multimetallic MOFs effectively enhanced the structural stability, surface properties, and adsorption performance of the material. Adsorption experiments demonstrated that the optimal metal-to-ligand molar ratio was 3:1, and the prepared adsorbent exhibited excellent fluoride removal performance under weakly acidic conditions (pH = 5). The maximum adsorption capacity calculated from the Langmuir model reached 216.92 mg·g⁻¹ at 45 °C. Adsorption kinetics were well fitted by the pseudo-second-order model, indicating that the adsorption process was mainly governed by chemisorption. Mechanism investigations based on FTIR and XPS analyses demonstrated that fluoride removal mainly occurred through electrostatic attraction and coordination exchange between fluoride ions and the Ce/La active sites. In addition, the composite exhibited good selectivity, anti-interference ability toward coexisting ions, and satisfactory fluoride removal performance in practical industrial wastewater. These findings suggest that Ce–Al–La–MOFs@PPy is a promising adsorbent for efficient fluoride-contaminated wastewater treatment.