Poly (vinylidene fluoride‑co‑hexafluoropropylene) (PVDF‑HFP) polymer inclusion membranes (PIMs) are widely used across separation industries because of their ability to selectively separate ions or molecules, their chemical stability, and their mechanical flexibility. Still, improving how the membrane performs without making the membrane material properties deteriorate remains a challenge. In this study, Aliquat 336 and Cyphos IL 104 were blended with PVDF‑HFP and fabricated through the solvent casting method. The main objective of this study is to understand how the ionic liquids can affect the membrane’s chemical properties, physical properties, mechanical stability, and its ability to recover gold through an H-cell setup. FTIR analysis confirmed the presence of Aliquat 336, showing bands associated with quaternary ammonium groups, while Cyphos IL 104 displayed vibrations related to phosphonium‑related P–C. SEM images further revealed clear differences in how the membrane morphology looked. Pure PVDF-HFP alone had compact surfaces; Aliquat 336 membranes appeared to have smoother features, and Cyphos IL 104 membranes developed rougher surfaces with micro-voids. The addition of ionic liquids also increased membrane porosity from 25 % in the pure membrane to 45 % with the addition of ionic liquids. At the same time, the contact angle dropped from 90° to 58°, indicating improved hydrophilicity. Mechanical testing showed that pure PVDF‑HFP had the highest tensile strength (30 MPa), modulus (450 MPa), and tear resistance (2.5 N/mm). Membranes with higher Aliquat 336 content retained better tear resistance, close to 2.0 N/mm, while maintaining smoother membrane surfaces. In comparison, the addition of Cyphos IL 104 adversely impacts the mechanical properties. Gold extraction experiments showed promising performance. The PVDF-HFP/A15C20 membrane achieved 76 % recovery within 12 h. The combination of both Aliquat 336 and Cyphos IL 104 improved ion transport while maintaining acceptable membrane stability properties. Overall, the results showed that the ionic liquid composition has a strong influence on both the membrane’s durability properties and separation performance. These findings provide a useful approach for developing membranes in precious metal recovery applications.
This review rigorously analyzes the advancement of fiber-reinforced geopolymer plasters for fireproofing purposes in construction. Geopolymers possess superior thermal resistance and reduced environmental impact relative to ordinary Portland cement. The addition of fibers such as steel, glass, basalt, and synthetic materials markedly improves mechanical strength, thermal insulation, and fire resistance. Recent research is comprehensively summarized, focusing on the mechanical behavior, thermal conductivity, microstructural evolution, and degradation mechanisms of fiber-reinforced geopolymers subjected to elevated temperatures and fire exposure. Special emphasis is placed on intumescent properties and fire-protection mechanisms that support the prospective advancement of geopolymer-based surface protection plasters. This review delineates significant research deficiencies, such as long-term durability, fiber–matrix interactions under cyclic thermal loading, and standardized fire-testing protocols, while also addressing burgeoning prospects for artificial intelligence-assisted material design and performance forecasting. Fiber-reinforced geopolymers are identified as a viable low-carbon fireproofing material that meets the sustainability and safety requirements of contemporary construction.
The widespread presence of microplastics (MPs) in aquatic ecosystems raises concerns for ecological sustainability and human health. This study explores a cost-effective and operationally simple coagulation-flocculation-sedimentation (CFS) process using tamarind seed (TS) as a natural coagulant or coagulant aid to remove polyamide (PA) MPs. Three coagulant systems, alum, TS and a combined system of alum with TS (alum-TS) were tested. The alum-TS system achieved the highest removal efficiency in a distilled water matrix at pH 7, using 15 mg/L alum and 50mmg/L TS. Optimal CFS parameters include rapid mixing at 200 rpm for 2 min, followed by slow mixing at 50 rpm for 30 min, and a settling phase of 30 min. Fitting the optimized experimental data to Brownian coagulation kinetics indicated that the removal of PA MPs by alum-TS follows second-order kinetics. Increasing MPs concentration, water hardness and anionic surfactants reduced removal efficiency, whereas salinity, cationic surfactants and humic acid improved it up to certain optimal levels. Tests on various water matrices, river, lake, tap water, and municipal wastewater, achieved removal efficiencies between 95.1 ± 0.85 Alum–TS system achieved 98.76
To address plastic waste and agricultural residue underutilization, this study evaluates the thermal (DSC and FTIR), microstructural (SEM), and mechanical behavior of polylactic acid (PLA) reinforced with flax fibers and olive pit particles, processed by fused filament fabrication (FFF). The results show the FFF reduces neat PLA's tensile strength by 10.8% and its tensile modulus by 31.6%, primarily due to process-induced voids, poor interlayer bonding, and polymer degradation that affect load transfer. The incorporation of flax fibers and olive pit particles leads to a further decrease in mechanical performance, reducing tensile and bending strength by up to 37% and 39.6%, respectively. In flax fiber composites, this is mainly caused by fiber degradation, poor interfacial adhesion, and random dispersion, which limit load transfer efficiency. Similarly, while olive pit particles exhibit greater thermal stability, their hydrophobic nature restricts adhesion to the PLA matrix, resulting in insufficient stress transfer. Moreover, the composite parts present complex failure mechanisms, including fiber pull-out and crack deflection. In conclusion, the natural reinforcements reduce the PLA's mechanical performance and modify its fracture behavior. In its current form, they show potential for sustainable composites in non-structural applications where aesthetics and environmental factors are important. To expand their use into structural applications, future work should focus on improving filler-matrix compatibility, optimizing particle size, fiber content, and processing conditions.
Traditional methods struggle to accurately and efficiently monitor rice leaf area index (LAI) throughout the entire growth period, hindering precision agriculture management. To address this problem, this study integrated unmanned aerial vehicle (UAV) multi-spectral remote sensing with machine learning to develop a novel model for dynamic rice LAI monitoring in Zhejiang province, China, leveraging data from critical stages including tillering, jointing, and heading. Then, the normalized difference vegetation index (NDVI), the normalized green-red difference index (NGRDI), the normalized difference red edge index (NDRE), the red-edge ratio vegetation index (RERVI) and the enhanced vegetation index (EVI) were calculated. On this basis, linear and nonlinear models using single vegetation index were constructed separately. Additionally, two empirical models including the partial least squares regression (PLSR) and the ridge regression (RR), and four machine learning models including the random forest (RF), the support vector machine (SVM), the k-nearest neighbor (kNN) and the improved feed forward neural network (FNN) were developed utilizing five vegetation indices. The results indicated that among the traditional empirical models, the nonlinear model LAI-EVInon-linear performed the best, with R2 of 0.756 and RMSE of 0.680, which also suggested that incorporating more vegetation indices as input data did not necessarily effectively improve the inversion accuracy by the PLSRand RR models. However, the performances of four machine learning models exhibited significant improvements. The LAIFNN model achievedthe highest accuracy, with R2 and RMSE by about 0.821 and 0.583, respectively. The contribution of each input feature was also quantified byusing the SHapley Additive exPlanations (SHAP) method. Based on the optimal model, this study mapped the multi-temporal spatial patterns ofrice LAI. These findings implied that the UAV-based remote sensing combined with high-performance machine learning offers apractical approach for LAI monitoring of the rice agro-ecosystem.
