Oil spills in water cause environmental and economic disasters. Herein, a superhydrophobic and oleophilic carbonaceous nanosponge (CN) with high adsorption capacity for selective oil removal from water was developed. It was grown by plasma polymerization of commercial acetylene in a radio frequency glow discharge (RFGD), a single‐step, scalable technique. The CN is a porous network of spherical nanoparticles with a broad pore size distribution. It adsorbs 33 times its own weight of light crude oil, with null water adsorption in shaking conditions (ASTM F726‐12). Because the CN could be used under sunlight exposure, the effect of UV light irradiation was studied. Potential applications of the CN arise, as it can be deposited on many substrates and change their wetting properties.
Electrospinning is one of the leading techniques for fiber development. Still, one of the biggest challenges of the technique is to control the nanofiber morphology without many trial-and-error tests. In this study, it is demonstrated that via design of experiments (DoE), response surface methodology (RSM) and machine learning regressions (MLR) it is possible to predict the beads-on-string size, size distribution and bead density in electrospun poly(vinylidene fluoride) (PVDF) mats with a small number of tests. PVDF concentration, dimethylacetamide/acetone ratio, tip-to-collector voltage and distance were the parameters considered for the design. The results show good agreement between the experimental and modeled data. It was found that concentration and solvent ratio play the main roles in minimizing bead size and number, distance tends to reduce them, and voltage does not play a significant role. As an evaluation of the potential of the method, bead-free fibers were obtained through the predicted parameter values. Comparison of the performance of the two methods is presented for the first time in electrospinning research. Response surface methodology resulted much faster, but MLR achieved a lower error and better generalization abilities. This approach and the availability of the MLR script used in this work may help other groups implement it in their research and find information hidden in the data while improving model prediction performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.