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Photocatalytically Active Superhydrophilic/Superoleophobic Coating.

ABSTRACT: Hydrophilic materials are easily fouled by organic contaminants owing to their high surface energy, and this oil-fouling problem severely hinders their use in practical applications. To address this challenge, herein, a hydrophilic coating with oil repellency and photocatalytic activity is developed by a spray-casting process. In the air surrounding, a water droplet spreads over the coating surface completely, while oil droplets exhibit contact angles more than 150° and moving on the coating freely. The water-wetted coating still had oil repellency, as the water layer on the coating surface can act as a lubricant to repel oil. Although methylene blue aqueous solution contaminates the coating by wetting it completely, these water-soluble organic molecules can be removed by UV illumination, due to the photocatalytic activity of the coating. Exploiting its water-attracting and oil-repelling properties, the coating deposited on a copper mesh is applied as a multiplatform for oil-water separation with high separation efficiency. This study provides a novel and efficient way to solve the oil-fouling problem of hydrophilic materials.

SUBMITTER: Lu J

PROVIDER: S-EPMC7254502 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Photocatalytically Active Superhydrophilic/Superoleophobic Coating.

Lu Jingwei J Zhu Xiaotao X Miao Xiao X Song Yuanming Y Liu Li L Ren Guina G Li Xiangming X

ACS omega 20200512 20

Hydrophilic materials are easily fouled by organic contaminants owing to their high surface energy, and this oil-fouling problem severely hinders their use in practical applications. To address this challenge, herein, a hydrophilic coating with oil repellency and photocatalytic activity is developed by a spray-casting process. In the air surrounding, a water droplet spreads over the coating surface completely, while oil droplets exhibit contact angles more than 150° and moving on the coating fre ...[more]

PMID: 32478233

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Project description:In this current study, facile, one-pot synthesis of functionalised nanocomposite coating with simultaneous hydrophilic and oleophobic properties was successfully achieved via the sol-gel technique. The synthesis of this nanocomposite coating aims to develop a highly efficient, simultaneously oleophobic-hydrophilic coating intended for polymer membranes to spontaneously separate oil-in-water emulsions, therefore, mitigating the fouling issue posed by an unmodified polymer membrane. The simultaneous hydrophilicity-oleophobicity of the nanocoating can be applied onto an existing membrane to improve their capability to spontaneously separate oil-in-water substances in the treatment of oily wastewater using little to no energy and being environmentally friendly. The synthesis of hybrid chitosan-silica (CTS-Si)/halloysite nanotube (HNT) nanocomposite coating using the sol-gel method was presented, and the resultant coating was characterised using FTIR, XPS, XRD, NMR, BET, Zeta Potential, and TGA. The wettability of the nanocomposite coating was evaluated in terms of water and oil contact angle, in which it was coated onto a polymer substrate. The coating was optimised in terms of oil and water contact angle using Response Surface Modification (RSM) with Central Composite Design (CCD) theory. The XPS results revealed the successful grafting of organosilanes groups of HNT onto the CTS-Si denoted by a wide band between 102.6-103.7 eV at Si2p. FTIR spectrum presented significant peaks at 3621 cm-1; 1013 cm-1 was attributed to chitosan, and 787 cm-1 signified the stretching of Si-O-Si on HNT. 29Si, 27Al, and 13H NMR spectroscopy confirmed the extensive modification of the particle's shells with chitosan-silica hybrid covalently linked to the halloysite nanotube domains. The morphological analysis via FESEM resulted in the surface morphology that indicates improved wettability of the nanocomposite. The resultant colloids have a high colloid stability of 19.3 mV and electrophoretic mobility of 0.1904 µmcm/Vs. The coating recorded high hydrophilicity with amplified oleophobic properties depicted by a low water contact angle (WCA) of 11° and high oil contact angle (OCA) of 171.3°. The optimisation results via RSM suggested that the optimised sol pH and nanoparticle loadings were pH 7.0 and 1.05 wt%, respectively, yielding 95% desirability for high oil contact angle and low water contact angle.

Project description:Light-absorbing organic molecules are useful components in photocatalysts, but it is difficult to formulate reliable structure-property design rules. More than 100 million unique chemical compounds are documented in the PubChem database, and a significant sub-set of these are π-conjugated, light-absorbing molecules that might in principle act as photocatalysts. Nature has used natural selection to evolve photosynthetic assemblies; by contrast, our ability to navigate the enormous potential search space of organic photocatalysts in the laboratory is limited. Here, we integrate experiment, computation, and machine learning to address this challenge. A library of 572 aromatic organic molecules was assembled with diverse compositions and structures, selected on the basis of availability in our laboratory, rather than more sophisticated criteria. This training library was then assessed experimentally for sacrificial photocatalytic hydrogen evolution using a high-throughput, automated method. Quantum chemical calculations and machine learning were used to visualise, interpret, and ultimately to predict the photocatalytic activities of these molecules, covering a much broader chemical space than for previous polymer photocatalyst libraries. By applying unsupervised learning to the molecular structures, we identified structural features that were common in molecules with high catalytic activity. Further analysis using calculated molecular descriptors within a suite of supervised classification algorithms revealed that light absorption, exciton electron affinity, electron affinity, exciton binding energy, and singlet-triplet energy gap had correlations with the photocatalytic performance. These trained predictive models can be used in future studies as filters to deprioritise or discard would-be low-activity candidate molecules from experiments, and to prioritize more favourable candidates. As a demonstration, we used virtual in silico experiments to show that it was possible to halve the experimental cost of finding 50% of the most active photocatalysts by using the machine learning model as an experimental advisor. We further showed that the ML advisor trained on the 572-molecule library could be used to make predictions for an unseen set of 96 molecules, achieving equivalent predictive accuracies to those in the initial training set. This marks a step toward the machine-learning assisted discovery of molecular organic photocatalysts and the approach might also be applied to problems beyond photocatalytic hydrogen evolution, such as CO2 reduction and photoredox chemistry.

Dataset Information

Photocatalytically Active Superhydrophilic/Superoleophobic Coating.

Publications

Photocatalytically Active Superhydrophilic/Superoleophobic Coating.

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