Hydrogenated Graphene Based Organic Thin Film Transistor Sensor for Detection of Chloride Ions as Corrosion Precursors

Corrosion monitoring and management has been at the center of structural health monitoring protocols due to its damaging effects on metallic structures. Current corrosion prevention and management programs often fail to include environmental factors such as Cl− ions and surface wetness. Early detection of these environmental factors can prevent the onset of corrosion and reduce repair and maintenance-related expenses. There is growing interest in creating solution-processed thin film environmental sensors with high sensitivity to corrosion precursors, low-cost fabrication, and small footprint, rendering them viable candidates for investigation as potential corrosion sensors that could be easily integrated into existing structures and screen printed or patterned directly into surface coatings. In this work, we have implemented C60-based n-type organic thin film transistors (OTFTs) with functionalized graphene oxide for humidity sensing and functionalized graphene nanoparticles for Cl− ion detection, using low-cost solution processing techniques. The reduced graphene oxide (rGO)-coated OTFT humidity sensor is designed for the qualitative estimation of surface moisture levels and high levels of humidity, and it exhibits a relative responsivity for dry to surface wetness transition of 122.6% to surface wetness, within a response time of 20 ms. We furthermore implemented an in-house synthesized hydrogenated graphene coating in conjunction with a second OTFT architecture for Cl− ions sensing which yielded a sensitivity of 4%/ppm to ultrafine ionic concentrations, over an order of magnitude lower than the range identified to cause corrosion in aircraft structures.

Tendency of Change in Water Quality of the Main Shirvan Collector Over a Long Period

The article examines the tendency of changes in the hydrochemical regime of the water of the Main Shirvan collector over a long period of time and the suitability of the collector water for irrigation. Continuous laboratory analyzes performed between 2004 and 2019 were compared to 1986. According to experimental data, it was determined that the hydrochemical regime of the collector water is gradually improving, and the salt content is decreasing. The degree of mineralization of the collector water decreased by 2.3 times, the total hardness by 2.2 times, the number of chloride ions by 10.5 times, the number of calcium ions by 1.3 times, the number of magnesium ions by 2.8 times, the total number of cations sodium and potassium decreased by 3.9 times. For 2004–2019 biochemical oxygen consumption in collector water increased 7.9 times, chemical oxygen demand increased 7.5 times, and the number of suspended particles increased 9 times. The amount of iron ions in water decreased by 2 times, while the amount of aluminum and zinc did not change. The suitability of collector water for irrigation was determined according to 7 internationally accepted assessment criteria. Collector water is considered suitable for irrigation in accordance with 6 assessment criteria — the degree of salinity, irrigation coefficient, sodium sorption coefficient, potential salinity, water alkalinity index and percentage of sodium, as well as 1 criterion not suitable for irrigation — the percentage of magnesium. Collector water can be used to irrigate crops.

Measuring anion binding at biomembrane interfaces

Understanding non-covalent molecular recognition events at biomembrane interfaces is important in biological, medicinal, and materials chemistry research.1 Despite the crucial regulatory roles of anion binding/transport processes at biomembranes, no information is available regarding how strongly anions can bind to naturally occurring or synthetic receptors in lipid bilayer environments compared to their well-established behaviour in solutions.2 To bridge this knowledge gap, we synthesised a flat macrocycle that possesses a record aqueous SO42– affinity among neutral receptors and exploited its unique fluorescence response at interfaces. We show that the determinants of anion binding are extraordinarily different in organic solvents and in lipid bilayers. The high charge density of dihydrogen phosphate and chloride ions prevails in DMSO, however in lipids they fail to bind the macrocycle. Perchlorate and iodide hardly bind in DMSO but show significant affinities for the macrocycle in lipids. Our results demonstrate a surprisingly great advantage of large, charge-diffuse anions to bind to a lipid-embedded synthetic receptor mainly attributed to their higher polarisabilities and deeper penetration into the bilayer, beyond the common knowledge of dehydration energy-governed selectivity. The elucidation of these principles enhances our understanding of biological anion recognition functions in membranes and guides the design of ionophores and molecular machines operating at biomembrane interfaces.

Experimental Investigation on Deterioration Mechanisms of Concrete under Tensile Stress-Chloride Ion-Carbon Dioxide Multiple Corrosion Environment

The adverse effects of a hostile marine environment on concrete structures inevitably result in great economic loss and may contribute to catastrophic failure. There is limited information on the durability of concrete in a tensile stress-chloride ion-carbon dioxide (TCC) multiple-corrosion environment. The objective of this study is to explore the impact of a TCC multiple-corrosion environment on concrete considering three coupled factors of compressive strength, Cl− penetration, and carbonation. Dry–wet cycle tests were conducted to determine the strength degradation and Cl− penetration concentration of concrete in a hostile multiple-corrosion marine environment. The results show that the effects of water-soluble chloride ions (Cl−), carbon dioxide (CO2), and tensile stress on concrete are not a simple superposition, but involve obvious interaction. The compressive strength of a concrete specimen first increases and then decreases in chlorine salt-carbon tests. The Cl− concentration and tensile stress affect the carbonation depth of concrete, which increases with an increase in Cl− concentration, and with the application of tensile stress. The Cl− concentration has an obvious effect on the carbonation depth. In addition to experimental observations, a stepwise regression equation was established based on the multiple linear regression theory. A correlation analysis considering different factors was conducted to reflect the corrosion results more directly.

Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology

Chemical enterprises are presently confronted with several difficult issues, including high power consumption, dangerous risk evaluation, and environmental regulation, all of which push industrial and academic institutions to develop new technologies, catalysts, and materials. Chlorinated polyethylene (CPE) is a polymer made by replacing H2 molecules in high density-(C2H4)n with chloride ions. CPE elastomers are made from a high density-(C2H4) backbone, and it was chlorinated using a free radical aqueous slurry technique. However, such fundamental polymer characteristics are insufficient to explain the performance characteristics of chlorinated polyethylene elastomers. Artificial intelligence (AI) has had a massive effect on all sections of the chemical sector, with tremendous potential that has revolutionized value supply chains, enhanced efficiency, and opened up new ways to the marketplace. As a result, in this research, we offer a methodology for the performance characterization of chlorinated polyethylene based on artificial intelligence (AI) and wireless network technology. The AI tools can search through enormous databases of known compounds and their attributes, leveraging the data to generate new possibilities. The dataset is first gathered. The chemical characterization is classified using the K -nearest neighbor (KNN) technique. This program was created to examine molecule structures and forecast the outcomes of new chemical reactions. Bayesian optimization is used to improve characterization performance. The proposed method will contribute to the future usage of AI in the chemical sector.

Experimental Research on Mechanical and Permeability Properties of Nylon Fiber Reinforced Recycled Aggregate Concrete with Mineral Admixture

Plain concrete’s major two drawbacks are its low tensile strength and high carbon footprint. Joint adding of fibers and recycled/waste materials in concrete might assist to resolve these problems. In the present study, a novel technique is planned to improve the recycled aggregate concrete (RAC) mechanical behavior and durability performance by joint incorporation of silica fume (SF) and nylon fibers (NF). In this research paper, different properties of concrete samples are examined for example flexural strength, compressive strength, split tensile strength, penetration of chloride ions, acid resistance, and water absorption. It was noted that adding nylon fibers as individual components enhances the recycled aggregate concrete mechanical characteristics and resistance to acid exposure. The inclusion of nylon fibers improved the behavior of the recycled aggregate concrete; however, it also increased the chloride penetration and water absorption by only 18% and 8% respectively. Up to 26% of mechanical strength of concrete was improved when silica fume was added in comparison to reference concrete, silica fume also assisted in controlling the loss of durability because of adding recycled aggregate concrete and nylon fibers. Silica fume improved the bond between binder matrix and nylon fibers. The study revealed that the combination of 50% RCA, 0.5% nylon fibers and 20% silica fume are optimum for the joint incorporation into concrete that can assist in developing sustainable, durable, and ductile recycled aggregate fiber reinforced concrete.

Durability Prediction Method of Concrete Soil Based on Deep Belief Network

To truly reflect the durability characteristics of concrete subjected to multiple factors under complex environmental conditions, it is necessary to discuss the prediction of its durability. In response to the problem of durability prediction of traditional concrete structures, there is a low prediction accuracy, and the predicted time is long, and a concrete structural durability prediction method based on the deep belief network is proposed. The influencing factors of the concrete structural durability parameters are analyzed by two major categories of concrete material and external environmental conditions, and the transmission of chloride ions in the concrete structure is described. According to the disconnection of the steel bars, the durability of the concrete structure is started, and the determination is determined. The concrete structural antiflexural strength, using a deep belief network training concrete structural antiflexural strength judgment data, constructs a concrete structural durability predictive model and completes the durability prediction of the concrete structure based on the deep belief network. The proposed prediction method based on the deep belief network has a high prediction accuracy of 98% for the durability of concrete column structures. The simulation results show that the concrete structural durability’s prediction accuracy is high and the prediction time is short. The prediction of concrete durability discussed here has important guiding significance for the improvement of concrete durability test methods and the improvement of concrete durability evaluation standards in China.