Preparation of polymer-based foam for efficient oil-water separation based on surface engineering

Magnetic Hyperporous Elastic Material with Excellent Fatigue Resistance and Oil Retention for Oil-Water Separation

Oily wastewater has caused serious threats to the environment; thus, high-performance absorbing materials for effective oil-water separation technology have attracted increasing attention. Herein, we develop a magnetic, hydrophobic, and lipophilic hyperporous elastic material (HEM) templated by high internal phase emulsions (HIPE), in which free-radical polymerization of butyl acrylate (BA) and divinylbenzene (DVB) is employed in the presence of poly(dimethylsiloxane) (PDMS), lecithin surfactant, and modified Fe3O4 nanoparticles. The adoption of the emulsion template with nanoparticles as both stabilizers and cross-linkers endows the HEM with biomimetic hierarchical open-cell micropores and elastic cross-linked networks, generating an oil absorbent with outstanding mechanical stability. Compressive fatigue resistance of the HEM is demonstrated to endure 2000 mechanical cycles without plastic deformation or strength degradation. By exploiting the synergistic effect of hierarchical structures and low-surface-energy components, the resulting HEM also possesses excellent and robust hydrophobicity (water contact angle of 164°) and good oil absorption capacity, in which Fe3O4 nanoparticles lead to convenient magnetically controlled oil recyclability as well. Notably, the unique biomimetic microporous structure demonstrates superior oil retention capacity (>95% at 1000 rpm and >60% at 10,000 rpm) over the state-of-the-art porous materials for a diverse variety of oils to reduce the risk of secondary oil leakage, along with good recoverability by squeezing owing to the excellent compression resilience. These excellent performances of our HEM provide broad prospects for practical applications in oil-water separation, energy conversion, and smart soft robotics.

Rational design of a polypropylene composite foam with open-cell structure via graphite conductive network for sound absorption

An exciting result is reported in this study where a polypropylene (PP) foam with a high open-cell content was achieved by constructing a thermally conductive network for the first time. PP and nano-graphite particles were used as substrate and filler, respectively, to prepare the PP-graphite (PP-G) composite foam by twin-screw blending, hot pressing, and supercritical CO2 foaming. The nano-graphite particles can effectively adjust the microstructure of the PP-G foam and achieve a high porosity. When the amount of nano-graphite is 10.0 wt%, the PP-G foam exhibits optimal sound absorption performance, compression resistance, heat insulation, and hydrophobic properties. In the human-sensitive frequency range of 1000-6000 Hz, the corresponding average SAC is above 0.9, and the internal tortuosity is 5.27. After 50 cycles of compression, the compressive stress is 980 kPa and the SAC loss is only 7.8%. This study also innovatively proposed a new strategy to achieve the simple and rapid preparation of open-cell PP foams by increasing the thermal conductivity of the foaming substrate.

Machine Learning Algorithms for Predicting Mechanical Stiffness of Lattice Structure-Based Polymer Foam

Polymer foams are extensively utilized because of their superior mechanical and energy-absorbing capabilities; however, foam materials of consistent geometry are difficult to produce because of their random microstructure and stochastic nature. Alternatively, lattice structures provide greater design freedom to achieve desired material properties by replicating mesoscale unit cells. Such complex lattice structures can only be manufactured effectively by additive manufacturing or 3D printing. The mechanical properties of lattice parts are greatly influenced by the lattice parameters that define the lattice geometries. To study the effect of lattice parameters on the mechanical stiffness of lattice parts, 360 lattice parts were designed by varying five lattice parameters, namely, lattice type, cell length along the X, Y, and Z axes, and cell wall thickness. Computational analyses were performed by applying the same loading condition on these lattice parts and recording corresponding strain deformations. To effectively capture the correlation between these lattice parameters and parts' stiffness, five machine learning (ML) algorithms were compared. These are Linear Regression (LR), Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). Using evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), all ML algorithms exhibited significantly low prediction errors during the training and testing phases; however, the Taylor diagram demonstrated that ANN surpassed other algorithms, with a correlation coefficient of 0.93. That finding was further supported by the relative error box plot and by comparing actual vs. predicted values plots. This study revealed the accurate prediction of the mechanical stiffness of lattice parts for the desired set of lattice parameters.

Biobased Castor Oil-Based Polyurethane Foams Grafted with Octadecylsilane-Modified Diatomite for Use as Eco-Friendly and Low-Cost Sorbents for Crude Oil Clean-Up Applications

Herein we report the synthesis and characterization of novel castor oil-based polyurethane (PU) foam functionalized with octadecyltrichlorosilane (C18)-modified diatomaceous earth (DE) particles, exhibiting superior hydrophobicity and oil adsorption, and poor water absorption, for use in effective clean-up of crude oil spillage in water bodies. High-performance and low-cost sorbents have a tremendous attraction in oil spill clean-up applications. Recent studies have focused on the use of castor oil as a significant polyol that can be used as a biodegradable and eco-friendly raw material for the synthesis of PU. However, biobased in-house synthesis of foam modified with C18-DE particles has not yet been reported. This study involves the synthesis of PU using castor oil, further modification of castor oil-based PU using C18 silane, characterization studies and elucidation of oil adsorption capacity. The FTIR analysis confirmed the fusion of C18 silane particles inside the PU skeleton by adding the new functional group, and the XRD study signified the inclusion of crystalline peaks in amorphous pristine PU foam owing to the silane cross-link structure. Thermogravimetric analysis indicated improvement in thermal stability and high residual content after chemical modification with alkyl chain moieties. The SEM and EDX analyses showed the surface's roughness and the incorporation of inorganic and organic elements into pristine PU foam. The contact angle analysis showed increased hydrophobicity of the modified PU foams treated with C18-DE particles. The oil absorption studies showed that the C18-DE-modified PU foam, in comparison with the unmodified one, exhibited a 2.91-fold increase in the oil adsorption capacity and a 3.44-fold decrease in the water absorbing nature. From these studies, it is understood that this novel foam can be considered as a potential candidate for cleaning up oil spillage on water bodies.