Enhancing corrosion resistance of mild steel in hydrochloric acid with Chiquita banana sap extract.

The corrosion of metals is still a huge challenge for various industries, and the pursuit of effective treatments ensures environmental sustainability. In this study, we utilized Chiquita banana sap-water extract (BSWE) to prevent mild steel from electrochemical corrosion in a 0.1 M HCl at room temperature. Corrosion resistance was assessed using various electrochemical methodologies, combining with surface characterization techniques. The results showed a high level of effectiveness when the corrosion current density decreased from 3292.67 µA cm-2 (for the sample immerged in the blank solution) to 187.33 µA cm-2 after 24 hours of immersion in the solution containing BSWE at a 2000 ppm concentration, equivalent to corrosion efficiency of 94.32%. Surface characterization revealed diminished corrosion on the inhibited steel surface due to the formation of a protective layer. X-ray photoelectron spectroscopy results demonstrated the presence of BSWE ingredients combining with iron oxides and hydroxides to form a smooth protective layer. Furthermore, theoretical calculations also indicated that the addition of BSWE can reduce steel surface damage when exposing to corrosive environment. The inhibitor based on banana sap extract can be referred to as a sustainable protective coating since it is biodegradable, abundantly available in banana plants and free of other harmful substances.

Role of SnO2 Nanoparticles for a Self-Forming Barrier Layer on a Mild Steel Surface in Hydrochloric Acid Medium Containing Piper betle Leaf Extract.

The self-formation of a porous organic thin-film via corrosion inhibitor supports wide applications of carbon steel in industry. Unfortunately, serious damages could be concentrated to the pinhole and/or pore locations in the porous organic film, resulting in the localized corrosion even when an optimal concentration of organic corrosion inhibitors is used. In this work, SnO2 nanoparticles are used for producing the more robust barrier layer via the self-migration of nanoparticles, resulting in a higher corrosion resistance, smooth and uniform protective layer, as well as the existence of SnO2 in the protective layer that could directly affect the high inhibition performance. Therefore, the work suggests a new way to make a more robust thin film that could extend the use of organic corrosion inhibitors.

Substrate temperature dependence of the crystalline quality for the synthesis of pure-phase MoTe2 on graphene/6H-SiC(0001) by molecular beam epitaxy.

MoTe2 has two stable solid phases. 2H-MoTe2 is semiconducting while 1T' is semimetallic. The selective synthesis of pure-phase thin films is still challenging. In this study, we have investigated the growth temperature dependence of MoTe2 synthesized by molecular beam epitaxy and have identified the optimum temperature for growing the stoichiometric films. It is confirmed that the crystalline quality of MoTe2 strongly depends on the substrate temperature. Post-growth annealing of grown layers at 400 °C stabilizes the semiconducting phase. The structural properties and the phase change in our materials are analyzed in details by reflection high energy electron diffraction, low energy electron diffraction, auger electron spectroscopy, x-ray photoemission spectroscopy, and scanning tunneling microscopy.

The Random Cluster Model for Robust Geometric Fitting.

Random hypothesis generation is central to robust geometric model fitting in computer vision. The predominant technique is to randomly sample minimal subsets of the data, and hypothesize the geometric models from the selected subsets. While taking minimal subsets increases the chance of successively "hitting" inliers in a sample, hypotheses fitted on minimal subsets may be severely biased due to the influence of measurement noise, even if the minimal subsets contain purely inliers. In this paper we propose Random Cluster Models, a technique used to simulate coupled spin systems, to conduct hypothesis generation using subsets larger than minimal. We show how large clusters of data from genuine instances of the model can be efficiently harvested to produce accurate hypotheses that are less affected by the vagaries of fitting on minimal subsets. A second aspect of the problem is the optimization of the set of structures that best fit the data. We show how our novel hypothesis sampler can be integrated seamlessly with graph cuts under a simple annealing framework to optimize the fitting efficiently. Unlike previous methods that conduct hypothesis sampling and fitting optimization in two disjoint stages, our algorithm performs the two subtasks alternatingly and in a mutually reinforcing manner. Experimental results show clear improvements in overall efficiency.