Copper corrosion prevention in 3.5% NaCl solution by Spartium junceum petals extract as an eco-friendly bio-inhibitor: kinetic and thermodynamic studies.

During the last decades, numerous studies were performed to introduce green corrosion inhibitors. So, various materials were utilized due to their being bio-degradable, available, and inexpensive. Because of the aforementioned aspects, Spartium Junceum petals extract (SJPE) was used as a new bio-inhibitor for the prevention of copper corrosion in a 3.5 wt% NaCl solution. This extract was obtained in water as a non-toxic solvent. Also, gas chromatography-mass spectrometry (GC-MS) confirmed the presence of organic molecules containing O, N, and F heteroatoms in SJPE, which are important for inhibitors. Also, Fourier-transform infrared spectroscopy (FT-IR) was used to identify the functional groups of the inhibitor molecules. Moreover, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization techniques were used which verified the great effect of this procedure to promote resistance corrosion of copper substrate. In addition, scanning electron microscopy (SEM) exhibited the surface morphology of copper substrate in the presence of SJPE which demonstrated the improvement of corrosion resistance in comparison to the absence of this bio-inhibitor. It should be noted that by increasing the amount of SJPE, the inhibition efficiency was ameliorated up to 87.8%. Furthermore, variation in temperature magnitude between 298 and 338 K implied that SPJE can improve inhibition behavior in higher temperatures. By applying temperature effect study data, the value of activation energy in the presence of the inhibitor was calculated (37.9 kJ mol-1), and the adsorption isotherm was Langmuir. Also, these data showed the inhibition mechanism is physical adsorption. The proposed inhibitor can be used as an efficient, eco-friendly, and inexpensive bio-inhibitor for the prevention of copper corrosion.

Detection performance of pixelated lutetium-yttrium oxyorthosilicate (LYSO) scintillators for high-resolution photon-counting CT imaging.

High-resolution photon-counting detector (PCD) computed tomography (CT) imaging is increasingly used for several applications. Recent technological advances in CT instrumentation have introduced various types of radiation detectors. Therefore, this work aims at evaluating the lutetium-yttrium oxyorthosilicate (LYSO) scintillator for use in PCD CT from a detector point of view. To do so, a mini-CT prototype was designed and constructed based on the pixelated LYSO blocks. The detector comprises four 10 × 10 linearly arranged LYSO blocks coupled with four position-sensitive photomultiplier tubes. The prototype utilizes a point gamma-ray source along with a cone-beam collimator. An in-home MATLAB-based data processing software package was also developed for storing the list-mode data, event positioning, and energy windowing. A set of experiments were conducted to assess the performance of the constructed energy-resolved LYSO:Ce detector for mini-CT imaging. The results show good crystal identification for all blocks with a maximum peak-to-valley ratio of 3.48. In addition, the findings confirm that the developed detector is position-sensitive. The 20% energy window provides an optimal performance by simultaneously providing good crystal identification and a scatter removal factor of 0.71. A 96% uniformity was also observed when the detector was irradiated with a uniform flood. The spatial resolution of the mini-CT prototype in the x- and y-directions was calculated to be 0.9 and 0.93 mm, respectively, corrected for a magnification factor of 2.5. It is concluded that the pixelated LYSO crystal is a promising alternative to the current detectors and would be the scintillator of choice for high-resolution PCD CT imaging tasks.

Detecting and tracking multiple mobile radioactive sources by data fusion of a surveillance camera and a sodium iodide (NaI) detector.

Nowadays, the use of radioactive materials has increased due to recent progress in nuclear technology, but concerns about the missing or smuggling of radioactive materials have persisted. Nuclear threats such as terrorist attacks or the malicious use of radioactive materials out of regulatory control (MORC) are always severe problems that can result in adverse environmental, health, economic, and security effects. Applying new technologies to monitor and track MORC will prevent radioactive materials from being illegally transported across borders or from one place to another. In this research, we have constructed a detection system to automatically and remotely localize multiple mobile gamma-emitting radiation sources among other objects by combining an IP camera and a sodium iodide detector. An algorithm for the detection system has been developed to identify the objects' paths from camera data and correlate radiation data with the paths to detect contaminated objects. We evaluated the system using two weak radioactive sources (Co-60, Cs-137), which were hidden on moving objects, and succeeded in finding harmful targets. The results have also shown that by increasing the number of contaminated sources from one to two, the detection accuracy decreases by about 2.5 times. However, by doubling the speed of mobile targets, the detection accuracy improved by about 30%. Detecting and tracking MORC with a single detection system is limited to small regions. By equipping many surveillance cameras in a city with relatively inexpensive radioactive detectors, a network sensing system is established to find radioactive hotspots in a smart city.

An intelligent gamma-ray technique for determining wax thickness in pipelines.

Measuring the wax deposition inside pipelines is one of the critical parameters in the oil, gas and petrochemical industries to control the flow through the pipelines. This paper presents a novel method using artificial neural networks to measure the thickness of the wax. This method was based on counting the backscattered gamma-ray from different thicknesses of the wax inside the pipes with different diameters. For this purpose, the system was simulated by MCNPX code and the designed setup was optimized. The main analyses were based on the simulation results but the verification was performed using a real experimental setup. The results showed a good agreement between the simulation results and the experimental data with a root mean square error less than 1%. Response of the detector was simulated for a standard industrial nominal pipe ranged from 2 to 4.5 inches and for radiation sources 137Cs and 60Co. Using these data, a multilayer perceptron for different energy sources was trained. The best prediction of the wax thickness was obtained for the case of using two radiation sources, simultaneously. The output of the trained neural network showed that the proposed method is capable of measuring the wax thickness inside the pipe with a good accuracy.

A gamma-ray tomography system to determine wax deposition distribution in oil pipelines.

Monitoring wax deposition in pipelines is of particular importance for oil extraction companies. In this work, a new gamma-ray tomography system based on using a rod CsI(Na) scintillator and 137Cs gamma source was developed to view the cross section of oil pipelines. The system can estimate the interaction position of gamma-rays by measuring light attenuation in the rod scintillator using two photomultiplier tubes coupled to its ends. Therefore, despite the simple structure of the employed detector, it can act as a position-sensitive instrument. In order to test the proposed tomography system, a selection of desired polyethylene round-bar pieces (as different wax thicknesses) was prepared to be placed in iron pipes, and their projections at different angles were acquired to construct cross-sectional images as well. The results showed that the designed system had the ability to scan the interior of the pipes and determine wax thickness with an error between 2.4% and 11.4%.