Efficient Oil-Water Separation Coating With Robust Superhydrophobicity and High Transparency

Abstract

There has been a growing interest in oil-water separation due to the massive economic and energy loss caused by world-wide oil spill. In the past decades, a new type of superhydrophobic surface has been developed for the efficient oil-water separation, but its large-scale use is significantly limited by its expensive, sophisticated, and fragile roughness structure. Meanwhile, to handle complex operating conditions, the transparency of the superhydrophobic surface has been more attractive due to its potential visual oil-water separation and optical applications. Herein, we showed a simple and versatile strategy to fabricate superhydrophobic coating with robustness and high transparency. Subsequently, this multifunctional superhydrophobic coating was utilized for oil-water separation and indicated excellent separation efficiency. In this strategy, candle soot composed of carbon nanoparticles was deposited onto the substrate and used as a rough surface template. Then, a filmy and hard silica shell was modified onto this template via chemical vapor deposition to reinforce the roughness structure. Following, this soot-silica coated substrate was calcined in air to remove the candle soot template. Finally, based on a rational surface design, this robust silica coating achieved excellent superhydrophobicity thereby showing inherently oil-water separation benefits. This reinforced superhydrophobic coating presented robust superhydrophobicity even after 410 s sand impacting with the height of 40 cm. Also, it retained excellent oil-water separation efficiency even after reuses.

Rethinking the Construction of Effective Metrics for Understanding the Mechanisms of Pretrained Language Models

“…The probe is the most common approach for associating neural network representations with linguistic properties (Voita and Titov, 2020). This approach is widely used to explore part of speech knowledge (Belinkov and Glass, 2019;Voita and Titov, 2020; and for sentence and dependency structures (Hewitt and Manning, 2019;White et al, 2021;Limisiewicz and Marecek, 2021;Chen et al, 2021). These studies demonstrate many important aspects of the linguistic information are encoded in pretrained representations.…”

“…†Corresponding Author. 1 Our code is available at https://github.com/cclx/ Effective_Metrics some extent the existence of operations extracting specific information from the vectors, is a common approach for post-hoc interpretability analysis of such models Limisiewicz and Marecek, 2021;Chen et al, 2021;White et al, 2021;Immer et al, 2022). However, even though we may desire strong interpretability from a whitebox model and metrics computed by it that rigorously reflect the ability to extract specific information from a given set of vectors, it can be challenging to achieve both of these aspects simultaneously when the source model lacks inherent interpretability.…”

“…In the preceding contents, we assumed a root depth of 1 for the syntactic tree. However, in traditional structural probe (Hewitt and Manning, 2019;Limisiewicz and Marecek, 2021;Chen et al, 2021;White et al, 2021), the root depth is typically assigned as 0 due to the annotation conventions of syntactic tree datasets. From a logical perspective, these two choices may appear indistinguishable.…”

A nanoparticle-coated microfluidic chip for automated, non-destructive extraction of encapsulated DNA in data storage

“…Two main strategies to promote superhydrophobicity are surface modification by the nanostructure and surface modification with surfactants. The former includes templating 19,20 , microphase separation 21,22 , nanoparticle coating [23][24][25][26] , chemical etching 27,28 , sol-gel process 29,30 , electro-spinning 31,32 , and so on. The latter is based on matching chemistry at the surface 33,34 .…”

A Robust Superhydrophobic Smart Coating with Reversible Thermochromic and Photochromic Property

“…Deng et al [29] were the first to split superhydrophobicity and mechanical stability into two different structural scales through a decoupling mechanism and proposed the concept of microstructural "armor" to protect superhydrophobic nanomaterials from frictional wear. Chen et al [30] modified a filamentous hard silica shell on a candle ash template by chemical vapor deposition to enhance the roughness structure and for efficient and robust oil-water separation. Based on this research, making the polymer react with the nanoparticle to form a chemical bond would further improve mechanical robustness.…”