Proton transport over nanoparticle surface in insulating nanoparticle film-based humidity sensor

We study a proton transport on the surface of insulating nanoparticles for humidity sensors. We use the approach to reveal proton transfer mechanisms in humidity sensitive materials. Hydrophilic and hydrophobic ligand-terminated silica nanoparticle films are adopted for evaluating temperature dependence of the ion conductivity. According to the activation energy of the conductivity, we explain the Grotthuss (H+ transfer) and vehicular (H3O+ transfer) mechanisms are mainly dominant on hydrophilic (-OH terminated) and hydrophobic (acrylate terminated) surface of nanoparticles, respectively. This investigation gives us a clue to understand a proton transfer mechanism in solution-processed humidity-sensitive materials such as oxide nanomaterials.

Mixed Manganese Dioxide on Magnetite Core MnO2@Fe3O4 as a Filler in a High-Performance Magnetic Alginate Membrane

The process of ethanol dehydration via pervaporation was performed using alginate membranes filled with manganese dioxide and a mixed filler consisting of manganese dioxide on magnetite core MnO2@Fe3O4 particles. The crystallization of manganese dioxide on magnetite nanoparticle surface resulted in a better dispersibility of this mixed filler in polymer matrix, with the preservation of the magnetic properties of magnetite. The prepared membranes were characterized by contact angle, degree of swelling and SEM microscopy measurements and correlated with their effectiveness in the pervaporative dehydration of ethanol. The results show a strong relation between filler properties and separation efficiency. The membranes filled with the mixed filler outperformed the membranes containing only neat oxide, exhibiting both higher flux and separation factor. The performance changed depending on filler content; thus, the presence of optimum filler loading was observed for the studied membranes. The best results were obtained for the alginate membrane filled with 7 wt.% of mixed filler MnO2@Fe3O4 particles. For this membrane, the separation factor and flux equalled to 483 and 1.22 kg·m−2·h−1, respectively.

Fabrication of Environmental-Friendly Magnetite Nanoparticle Surface Coatings for the Efficient Collection of Oil Spill

Over the past few decades, there has been an increased trend for the use of natural compounds and their derivatives as alternatives to traditional chemicals and is due to their renewability, green character, and wide availability. This work aims to convert sodium alginate (S.ALG), a natural polysaccharide, into amides through its conversion to alginic acid (H.ALG). The formed H.ALG was esterified using methanol, followed by a reaction with octadecylamine (OA) and dodecylamine (DA) to produce corresponding amides, OA-ALG, and DA-ALG, respectively. The synthesized OA-ALG and DA-ALG were used as capping agents to further form hydrophobic magnetite nanoparticles (MNPs), OA-MNPs and DA-MNPs, respectively. The chemical structures, morphology, hydrophobicity, and magnetic properties of OA-MNPs and DA-MNPs were investigated using different instrumental techniques. Furthermore, the efficacy of as-synthesized MNPs as oil spill collectors were also evaluated using different ratios of MNPs:crude oil. From the analysis of results, the OA-MNPs and DA-MNPs exhibited high efficiency in the collection of oil spill even at low ratios of MNPs:crude oil.

Revising Protein Corona Characterization and Combining ITC and Nano-DSC to Understand the Interaction of Proteins With Porous Nanoparticles

The exposure of nanoparticles (NPs) to biological fluids leads to the formation of a protein coating that is known as protein corona (PC). Since PC formation is influenced by the physicochemical properties of the nanoparticles, the understanding of the interplay of the factors that participate in this process is crucial for the development of nanomaterials as cell-targeted delivery vehicles. In general, it is accepted that the PC formation is a complex and dynamic process, which depends on the composition of the medium and the properties of the NP mainly size, shape, and superficial charge. Interestingly, although the interaction between the protein and the NP is essentially a superficial phenomenon, the influence of the roughness of the nanoparticle surface has been scarcely studied. In this work, the influence of superficial roughness and porosity has been studied with the aid of nanodifferential scanning calorimetry (nano-DSC) and isothermal titration calorimetry (ITC) using mesoporous silica nanoparticles (MSNs) as an NP model. The interaction process of the proteins with the NP surface was analyzed by ITC measurements, while the stability and denaturation of the proteins was monitored by nano-DSC. Thanks to the complementarity of these two techniques, a more complete insight into the PC formation on the pores has been accomplished.

A Review of the some aspects for the development of ZnO based photocatalysts for a variety of applications

Today, one of the most important problems for humanity is the pollution of the environment with various organic compounds that worsen the health of the peoples. The most dangerous pollutants are complex compounds that do not degrade under natural conditions. One way to solve the problem of pollution is to use photocatalysis to degrade harmful compounds. Zinc oxide nanostructures exhibit attractive photocatalytic and antibacterial properties due to the increased reactivity of the nanoparticle surface, which allows the efficient decomposition of organic pollutants. In this review, various methods for enhancing the photoefficiency of ZnO nanostructures are considered. It is shown that ZnO nanoparticles with specific surfaces (spherical, nanowires, nanoflowers), which are characterized by a high surface area, have a high removal rate of various pollutants. Such methods of improving the photocatalytic properties of ZnO as the band gap engineering, doping with metal/nonmetal, the combination of ZnO with other materials, formation of hybrid structures are considered.

Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise

A deep convolutional neural network has been developed to denoise atomic-resolution transmission electron microscope image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training the network. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. Factors contributing to the performance are identified, including (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits both extended and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.