Liquid-Repellent Metal Oxide Photocatalysts

Metal oxide photocatalysts (MOPCs) decompose organic molecules under illumination. However, the application of MOPCs in industry and research is currently limited by their intrinsic hydrophilicity because MOPCs can be wetted by most liquids. To achieve liquid repellency, the surface needs to possess a low surface energy, but most organic molecules with low surface energy are degraded by photocatalytic activity. Herein, current methods to achieve liquid repellency on MOPCs, while preventing degradation of hydrophobic coatings, are reviewed. Classically, composite materials containing MOPCs and hydrophobic organic compounds possess good liquid repellency. However, composites normally form irregular coatings and are hard to prepare on surfaces such as those that are mesoporous or nanostructured. In addition, the adhesion of composites to substrates is often weak, resulting in delamination. Recent studies have shown that the direct grafting reaction of polydimethylsiloxane (PDMS) from silicone oil (methyl-terminated PDMS) under illumination results in a stable polymer brush. This easy and simple grafting method allows us to create stable liquid-repellent surfaces on MOPCs of various types, structures, and sizes. In particular, super-liquid-repellent drops with an underlying air layer can be created on PDMS-grafted nano-/microstructured MOPCs. Potential applications of surfaces combining liquid repellency and photocatalytic activity are also discussed; thus offering new ways of using MOPCs in a wider range of applications.     

Effect of irradiation for sterilization on beverage reference materials for the analysis of food additives

Accreditation and Quality Assurance - Reference materials for proficiency testing (PT) were prepared for 6 years. The target analytes were food additives, sorbic acid, benzoic acid, and...     

Vapoluminescent Behavior and the Single-Crystal-to-Single-Crystal Transformations of Chloroform Solvates of [Au2(μ-1,2-bis(diphenylarsino)ethane)2](AsF6)2

The mono- and di-chloroform solvates of [Au₂(μ-1,2-bis(diphenylarsino)ethane)₂](AsF₆)₂ undergo single-crystal-to-single-crystal transformations that result in gain (over 12 hours) or slow loss (over five years) of only one chloroform molecule. The change in solvation results in changes in the structure and luminescence of the digold cation. The cation consists of a pair of slightly bent As-Au-As units that are connected through the two bridging dpae ligands and by aurophilic interactions with Au⋅⋅⋅Au contacts of 3.05152(15) Å in the disolvate or 2.9570(5) Å in the monosolvate.     

The Supposition for the Kochen and Specker Theorem Using Sum rule and Product rule

We investigate a hidden-variable theory introduced by Kochen and Specker. The “hidden” results of measurements are either 1 or − 1. We suppose the validity of Sum rule and Product rule. Kochen and Specker suppose the two operations Sum rule and Product rule commute with each other. It is shown that the two operations Sum rule and Product rule do not commute with each other when we want to avoid the Kochen and Specker paradox. Otherwise we encounter the Kochen and Specker paradox. We mention the supposition for Greenberger, Horne, and Zeilinger paradox. It is discussed that only Product rule is necessary for the paradox. We give up the two paradoxes if (1) Sum rule and Product rule do not commute with each other and (2) Product rule is not valid.

     

Experimental investigation and modeling of electrical properties for phenol red thin film deposited on silicon using back propagation artificial neural network

A heterojunction of a thin film of phenol red and silicon was constructed by applying the thermal evaporation technique. The current-voltage (I-V) characteristics of phenol red/Si heterojunction were studied experimentally and theoretically in the temperature range from 303 to 353 K. The thermionic emission mechanism for the conduction was established from the forward bias current density-voltage characteristics. The behavior of both the ideality factor and series resistance was deduced. The barrier height (φ) was determined as 0.26 eV. The reverse bias voltage exposed a thermally motivated behavior. Modeling of electric current in terms of voltage and temperature was carried out using an artificial neural network (ANN model) that depends on the experimental data. The learning ANN method was applied based on trial and error technique. The optimum network architect which represents the most satisfactory performance was obtained. The simulation and prediction results of the ANN model offered a high-reaching accuracy in comparison with experimental data. The ANN model utilized as an active tool to estimate the electrical properties of heterojunction for organic compounds.