Search

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To study the polarization reflection characteristics of metal surfaces, a parameter optimization method for the polarization bidirectional reflection distribution function (pBRDF) model of metal surfaces based on the improved strawberry algorithm has been proposed. Firstly, the light scattering characteristics of metal surfaces were analyzed and a multi-parameter pBRDF model was constructed. Then, the working mechanism of the strawberry optimization algorithm was investigated and improved by introducing the chaotic mapping and Levy flight strategy to overcome the shortcomings, such as low convergence rate and easily falling into local optimum. Finally, the method proposed in this paper was validated by simulating open-source data from references and the obtained ones with a self-built experimental platform. The results show that the proposed method outperforms those by nonlinear least squares, particle swarm optimization and the original strawberry algorithm in fitting the detected degree of polarization (DOP) data, indicating the modeling accuracy was significantly improved and better suited to characterize the polarized reflection properties of metal surfaces. [ABSTRACT FROM AUTHOR]

The growing demand for superior metallic components in several industries has emphasized the necessity for effective and dependable inspection techniques. Conventional manual inspection procedures are lengthy, based on personal judgment and susceptible to human mistakes. In this study, we introduce a dynamic multi-layered auto-encoder with a robust deep neural network (DMAE+DNN) system for inspecting flaws in metallic surfaces.We acquired images of the metal surface defects. The neural network design is improved by incorporating a dynamic multi-layered auto-encoder, enabling the system to obtain highly detailed representations of surface data. The results demonstrate the improved performance of the suggested system, showcasing higher levels of recall, precision and F1-score in comparison to conventional defect detection methods. This technological development has the possibility to completely transform quality control procedures by minimizing the need for manual inspections and improving the overall quality of products that heavily rely on metal elements.

ObjectiveThis study focuses on the deposition characteristics of oil mist on metal and fabric surfaces in ship cabin. MethodsOil mist is released in an experimental cabin and allowed to deposit on the target surfaces. The mass difference before and after deposition on the surfaces is weighed to determine the deposition mass, and the oil mist deposition characteristics of surfaces with different orientations and materials are compared. The deposition on the horizontal downward surface is reasonably assumed to be gas phase deposition. Its contribution is then estimated and the particle phase deposition velocity is calculated. ResultsThe deposition mass on the horizontal upward surface of the metal is 3.98 times and 4.66 times greater than that on the vertical surface and horizontal downward surface. For fabric surfaces, the deposition mass on the horizontal upward surface is 1.08 times and 1.20 times greater than that of the vertical surface and horizontal downward surface. For material comparison, the deposition mass on the horizontal upward, vertical and horizontal downward surfaces of the fabric is 49.1 times, 169.8 times and 155.5 times greater than that of the metal surface facing in the same direction respectively. Estimates show that the contribution of phase deposition to the metal horizontal and vertical surfaces are 21.5% and 79.2% respectively. The contribution of phase deposition to the fabric surfaces is about 90%. ConclusionsThe oil mist deposition on fabric surfaces is much stronger than that on metal surfaces. The deposition on the metal horizontal upward surface is much greater than that on the vertical surface and horizontal downward surface. The deposition differences between the fabric surfaces in the three orientations are not significant. Regardless of orientation or material, gas phase deposition cannot be ignored as it plays a dominant role in ship cabin environments.

Cyanogen chloride (CNCl) is a highly toxic agent, but there is currently a lack of research on the catalytic hydrolysis reaction of CNCl on metal surfaces. The low efficiency and unclear mechanism of CNCl hydrolysis reaction have hampered the development and enhancement of protective equipment. In this work, we focused on investigating the adsorption behavior and hydrolysis mechanism of CNCl on copper and chromium surfaces through first‐principle methods. Our findings revealed the most stable adsorption configuration of CNCl on metal surfaces and its terminal flipping behavior during the hydrolysis process. Subsequently, we designed and studied a series of CNCl hydrolysis reaction pathways, primarily involving C‐terminal intermediates. Notably, the adsorption energy of CN* displayed strong linear scaling relationships with that of other key reaction intermediates, directly influencing the activity and selectivity of CNCl hydrolysis. By employing the adsorption energy expression based on the intrinsic properties of substrates and adsorbates, we extended them to identify the adsorption energies of various complex reaction intermediates involved in CNCl hydrolysis. This comprehensive understanding of the reaction mechanism on metal surfaces provides a theoretical foundation and innovative perspective for the further exploration of CNCl hydrolysis catalysts. [ABSTRACT FROM AUTHOR]

The growing demand for superior metallic components in several industries has emphasized the necessity for effective and dependable inspection techniques. Conventional manual inspection procedures are lengthy, based on personal judgment and susceptible to human mistakes. In this study, we introduce a dynamic multi-layered auto-encoder with a robust deep neural network (DMAE+DNN) system for inspecting flaws in metallic surfaces. We acquired images of the metal surface defects. The neural network design is improved by incorporating a dynamic multi-layered auto-encoder, enabling the system to obtain highly detailed representations of surface data. The results demonstrate the improved performance of the suggested system, showcasing higher levels of recall, precision and F1-score in comparison to conventional defect detection methods. This technological development has the possibility to completely transform quality control procedures by minimizing the need for manual inspections and improving the overall quality of products that heavily rely on metal elements. [ABSTRACT FROM AUTHOR]

The detection of surface defects on metal products during the production process is crucial for ensuring high-quality products. These defects also lead to significant losses in the high-tech industry. To address the issues of slow detection speed and low accuracy in traditional metal surface defect detection, an improved algorithm based on the YOLOv7-tiny model is proposed. Firstly, to enhance the feature extraction and fusion capabilities of the model, the depth aware convolution module (DAC) is introduced to replace all ELAN-T modules in the network. Secondly, the AWFP- Add module is added after the Concat module in the network's Head section to strengthen the network's ability to adaptively distinguish the importance of different features. Finally, in order to expedite model convergence and alleviate the problem of imbalanced positive and negative samples in the study, a new loss function called Focal-SIoU is used to replace the original model's CIoU loss function. To validate the effectiveness of the proposed model, two industrial metal surface defect datasets, GC10-DET and NEU-DET, were employed in our experiments. Experimental results demonstrate that the improved algorithm achieved detection frame rates exceeding 100 fps on both datasets. Furthermore, the enhanced model achieved an mAP of 81% on the GC10-DET dataset and 80.1% on the NEU-DET dataset. Compared to the original YOLOv7-tiny algorithm, this represents an increase in mAP of nearly 11% and 9.2%, respectively. Moreover, when compared to other novel algorithms, our improved model demonstrated enhanced detection accuracy and significantly improved detection speed. These results collectively indicate that our proposed enhanced model effectively fulfills the industry's demand for rapid and efficient detection and recognition of metal surface defects. [ABSTRACT FROM AUTHOR]

To study the polarization reflection characteristics of metal surfaces, a parameter optimization method for the polarization bidirectional reflection distribution function (pBRDF) model of metal surfaces based on the improved strawberry algorithm has been proposed. Firstly, the light scattering characteristics of metal surfaces were analyzed and a multi-parameter pBRDF model was constructed. Then, the working mechanism of the strawberry optimization algorithm was investigated and improved by introducing the chaotic mapping and Levy flight strategy to overcome the shortcomings, such as low convergence rate and easily falling into local optimum. Finally, the method proposed in this paper was validated by simulating open-source data from references and the obtained ones with a self-built experimental platform. The results show that the proposed method outperforms those by nonlinear least squares, particle swarm optimization and the original strawberry algorithm in fitting the detected degree of polarization (DOP) data, indicating the modeling accuracy was significantly improved and better suited to characterize the polarized reflection properties of metal surfaces.

The detection of surface defects in metal materials has been a challenging issue in the industrial domain. The existing algorithms for metal surface defect detection are limited to a few specific types of defects and exhibit low performance with detection of defects of varying scales. A novel detection method based on the Information Enhancement YOLOv5 Network (IE-YOLOv5) for surface defects in metal parts is proposed, to realize efficient detection, which introduces a lightweight Federated Fusion Slim Neck module (FF-Slim-Neck) and a Parameter-free Spatial Attention mechanism (PSA) in YOLOv5 network. Comparative experiments were conducted using the NEU-DET dataset. The experimental results indicate that the proposed algorithm for detecting defects on metal surfaces achieves an average precision of 96.7% when identifying six different types of surface imperfections: crazing, inclusions, patches, pitting, scaling, and scratches. This represents a 2.4% enhancement in precision compared to the YOLOv5 algorithm. The measured processing velocity of this approach stands at 46.17 frames per second (FPS), highlighting its remarkable qualities of resilience, precision, and real-time capability. [ABSTRACT FROM AUTHOR]

We calculate the free energies of adsorption between a solid wall and various arginine derivatives in water using molecular dynamics simulations. We vary the hydrophobic group size of the amino acids in two ways and the hydrophilicity of the solid wall in five ways. The free energy of adsorption decreases by increasing the hydrophilicity of the solid wall, regardless of the size of the hydrophobic group. Amino acids with small hydrophobic groups are adsorbed on the solid wall with the strongest hydrophilicity, but the free energy of the amino acids with large hydrophobic groups is relatively small. The free energy of adsorption in benzene solvent is increased by increasing the hydrophilicity of the solid wall; however, analogous to results in water solvent, it is independent of the size of the hydrophobic group. We attribute these differences in free energy to the effect of solvent molecules localized on the solid wall. Thus, these results may modulate peptide adsorption on a solid surface by the size of the peptide's hydrophobic groups. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Nanjing University of Aeronautics & Astronautics is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background. Osseointegration of medical equipment, including implants, into the bone is one of the main conditions for a successful outcome of surgical treatment. To improve the reliability of the functioning of medical equipment products by improving the osseointegrative properties, the authors have developed a unique technology for developing the surface of the implanted component. Thus, the aim of the work is to develop a technology for developing the surface of medical equipment products based on laser metal foaming. Materials and methods. Experimental studies have shown that the surface geometry and structure of the surface layer after laser foaming meet the requirements for medical equipment products for better osseointegration into the patient's bone. The results were confirmed by optical and electron microscopy data. Results. Studies have shown that the composition of the gaseous medium has the most significant effect on the structure of the surface layer. This factor determines the appearance in the surface layer of a large number of chemical compounds that significantly change the properties of the material. During the experiment, this factor was varied, and the rest were stabilized at a constant level. It has been established that the increase in the chemical inhomogeneity of the surface layer increases significantly with the introduction of air into the composition. When Argon is used as a working medium, the surface structure is normalized to the level of the material base. Conclusions. In the process of work, a developed structure was created on the selected surface area without changing the chemical composition with an increase in the area of osseointegrated structures up to 100 % of the original surface. For processing, it is recommended to use an inert gas medium, and the medium pressure must be above 8 bar. The feed speed of the laser head should be at least 5000 mm/min, but not exceed 10 000 mm/min.

Surface treatment of metals by pulsed laser radiation in the range of colored oxide structure formation makes it possible to obtain a quasi-amorphous nanostructured layer. Metal surface oxide films of different chemical and mechanical structure are formed under laser action. They determine, in addition to surface microgeometry, its coloring. Currently, there is no single opinion about the composition of oxide films formed on the surface of metals as a result of laser processing. Therefore, determination of the conformity of oxide film composition to a certain color formation is of scientific and practical interest. The object of research is specified areas of a 12Kh18N10T steel surface, painted in contrasting colors as a result of laser marking with different parameters. As a result of the study, the chemical and phase composition of oxide films for coloring a metal surface are determined with prescribed laser marking. [ABSTRACT FROM AUTHOR]

The process of metal-atom sputtering during a corona discharge is considered. When an electron moves in a medium at some velocity, charge screening occurs with a delay in space and time, which leads to the emergence of a wake potential. Excited oscillations of the wake charge lead to the appearance of additional forces. The energy loss of a moving particle per unit path is determined by the work of the deceleration force that acts on the particle from the side of the wake potential it creates in the medium. The paper considers the effect of the wake potential on the ions (atoms) of the lattice matrix. A well-known expression is used for the wake potential excited by a charged particle moving with an energy greater than the Fermi energy. An expression for the sputtering cross section of metal atoms under the action of the wake potential excited by an electron beam is obtained. It is shown that the result of sputtering does not depend on the charge sign of the incident particle (electron or ion). [ABSTRACT FROM AUTHOR]

Purpose: To estimate the effects of the thermal cycling (TC) process on the metal surfaces of Locators, as well as retention loss, and the correlation between them. Materials and methods: Twenty‐five new Locator R‐Tx were included in the study. Four areas were marked on each Locators' patrix metal surface and scanned using a confocal scanner (μsurf explorer; NanoFocus). Three surface roughness parameters were measured in the scans: Sa (average distance of peaks from the central plain of the area), Vmp (volume of the peaks in the area), and Spc (mean curvature of the peaks describing the degree of their sharpness). Retention test was performed using Instron® 4500 compression tension tensile tester at a speed of 10 mm/min. The retention tests were done using a working model made of two acrylic blocks in which the Locator system parts were inserted. The surface parameters measurements and the retention tests were performed 2 times, once before and once after TC. The Locators were subjected to 15,000 TC cycles by investing them into 2 tubs with different water temperatures, 55°C and 5°C. During each 60‐second cycle, the Locators were submerged in each tub for 20 seconds, with a 10 second transition time between the tubs. The post‐TC retention and surface parameters measurements were compared with those prior to TC and the prior to TC measurements served as controls. Changes in parameters before and after TC were analyzed by a two‐way ANOVA nested model with random intercept and slope by restricted maximum likelihood method. Correlation between retention and surface parameters was quantified and examined using Kendall's correlation test. The findings were considered statistically significant if p < 0.05. Results: There was a significant decrease in retention of 16.6N at the second retention test (p < 0.001). A significant statistical decrease in surface parameters were measured after TC process, Sa and Vmp (18 ×10–3 μm, p = 0.041 and 0.94 ×10–3 1/μm, p = 0.001, respectively). A significant statistical increase in Spc of 6.4 ×10–3 μm3/μm2 (p = 0.023) was noticed. The correlation between retention decreases and surface changes was not statistically significant. Conclusion: The TC process causes a substantial reduction in retention to the Locator system over time. In addition, TC causes significant but minor changes to the Locator surface area. Most of the changes are in the horizontal dimension. [ABSTRACT FROM AUTHOR]

Laser-based sensors are taking over their counterparts, fluorescence-based sensors, in the field of bioengineering and space communication due to their high amplification, narrow emission beam, low signal-to-noise ratio, and nonlinearity. In this work, we investigated the chemical growth of ZnO nanorods on different metal surfaces (Au, Pt, Ti, and Al) to serve as random laser mediums. Different analytical techniques were used to investigate the optical, electrical, and morphological properties of the fabricated devices. A lasing threshold of 27.68 mJ/cm² with a spectral width of 2.18 nm centered at 385.06 nm is observed when Ti is used as the growth surface. Al surface produces ZnO nanorods with a lasing threshold of 24.21 mJ/cm² and a spectral width of 1.81 nm. Growing ZnO nanorods on metal surfaces using the chemical bath deposition method paves the way for fabricating different random lasing-based biosensors. [ABSTRACT FROM AUTHOR]

The detection of surface defects in metal materials has been a challenging issue in the industrial domain. The existing algorithms for metal surface defect detection are limited to a few specific types of defects and exhibit low performance with detection of defects of varying scales. A novel detection method based on the Information Enhancement YOLOv5 Network (IE-YOLOv5) for surface defects in metal parts is proposed, to realize efficient detection, which introduces a lightweight Federated Fusion Slim Neck module (FF-Slim-Neck) and a Parameter-free Spatial Attention mechanism (PSA) in YOLOv5 network. Comparative experiments were conducted using the NEU-DET dataset. The experimental results indicate that the proposed algorithm for detecting defects on metal surfaces achieves an average precision of 96.7% when identifying six different types of surface imperfections: crazing, inclusions, patches, pitting, scaling, and scratches. This represents a 2.4% enhancement in precision compared to the YOLOv5 algorithm. The measured processing velocity of this approach stands at 46.17 frames per second (FPS), highlighting its remarkable qualities of resilience, precision, and real-time capability.

Computer vision and deep learning approaches have an important role in industrial inspection systems. Computer vision technology is essential for fast, defect-free control of products in the production line. The importance of the computer vision concept is recognized when the problems of the classical methods are taken into consideration. Metallic defect detection is a challenging problem as metal surfaces are easily affected by environmental factors such as lighting and light reflection. Since traditional detection algorithms are inefficient in complex problems, we propose a novel method to detect and classify metal surface defects, such as cracks, scratches, inclusion, etc. The type and location of defects were detected by the Faster Regional Convolutional Neural Network (Faster R-CNN), combined with the Shape From Shading (SFS) method, which can extract surface characteristics. The Northeastern University (NEU) surface defect database was used for defective samples. The proposed algorithm has also been tested on an unlabeled dataset (KolektorSDD2/KSDD2) to show labeling performance. The results on both labeled and unlabeled datasets have demonstrated state-of-the-art performance in automatic defect detection, classification, and labeling. The proposed method has satisfactory results for the detection of defects on the metal surface, and the mean average precision is 0.83. The average precision of crazing, pitted surface, patches, scratches, inclusion, and rolled-in scale are 0.98, 0.81, 0,90, 0.79, 0.88, and 0.62, respectively.

Background: Acinetobacter spp., emerging pathogens equipped with the competence to establish multitudinous severe infections in immunocompromised hosts, are grievous threats to human health. To tackle the enormous burden of disease caused by Acinetobacter spp., the headlong discovery and the advancement of novel therapies are of the essence at this juncture. The present study attempted to determine the antimicrobial effects of copper on the clinical isolates of Acinetobacter spp. (Iso-03 and Iso-04). Methods: The potential deployment of copper-based antibacterial strategies against Acinetobacter spp. was assessed by exposing the isolates to the increasing concentrations of CuSO4 (from 2.5 to 1.5 mM) in liquid culture (M9 minimal medium) for 6 h and also through the exposure of them on solid metal surfaces (stainless steel and copper coupons) for 75 min, wherein the copper sensitivity and resistance of the clinical isolates of Acinetobacter spp. were determined. Results: There was no interference with the growth of the isolates at the low concentrations, whereas the bacterial growth was affected by the high concentrations of CuSO4 at different levels. During the exposure on the solid metal coupons, no loss of viability of isolates was observed on stainless steel, however, the rapid death of isolates was discernible on copper surface, leading to a dramatic decrease in the number of colony-forming units (CFUs), eventually to the limit of detection (3 CFUs per coupon). Conclusion: This study substantiated that copper possesses antimicrobial properties which can be deployed in novel therapies for the prevention of the infections caused by Acinetobacter spp. and other emerging pathogens. Further studies on the sensitivity and resistance of Acinetobacter spp. to copper at the molecular genetics levels can open the door to better exploitation of this metal for the inhibition of the vigorous growth of drug-resistant Acinetobacter spp.

Metal corrosion has always been a serious problem in industry. There has always been a need to increase the number of possible, cost-effective corrosion inhibitors. However, many commercially available corrosion inhibitors have both high efficiency and high toxicity, which has led environmental authorities to ban their use. As a result, there is growing interest in scientific research into the use of environmentally friendly compounds. Surfactants and biocompatible corrosion inhibitors are special types of chemicals suitable for long-term industrial use. Molecules with unique hydrophilic and hydrophobic properties can be used in a wide range of applications to solve solubilisation problems and improve extraction processes. The use of surfactant-based products to prevent corrosion on metallic surfaces is a new approach in the field of chemical science. This review article addresses the mechanism of corrosion on metal surfaces and discusses in detail the use of environmentally friendly, cost-effective and readily available surfactants as corrosion inhibitors. The properties and applications of different types of surfactants are also discussed. [ABSTRACT FROM AUTHOR]

Experimental data on particle generation in the corona-discharge zone are obtained. The size distribution of these particles is measured as a function of the discharge parameters. The particle-size spectra are shown to slightly depend on the polarity of the corona point and are only determined by the potential difference and current strength. Various materials such as iron, copper, silver, molybdenum, tungsten, and graphite are the objects studied. The measurements are carried out under normal conditions in various gaseous atmospheres such as air, nitrogen, and argon. A theoretical model for the interaction of corona-discharge plasma with a metal surface is developed. The extraction of atoms from lattice sites supposedly occurs as a result of collective excitations of the electron gas in the metals. This theoretical model is based on the resonant excitation of a plasmon in the surface layer of a metal upon the inelastic scattering of charged particles of a corona discharge on the electrons of a metal sample. It is shown using the electrostatic-imaging method that the Coulomb interaction of a negative-charge-density wave on the surface with a surface ion of the crystal lattice is capable of extraction the ion from the metal. The interaction cross section of this process is estimated. These results are in qualitative agreement with the experimental data, which confirms the validity of the chosen interaction model. [ABSTRACT FROM AUTHOR]

Background: Acinetobacter spp., emerging pathogens equipped with the competence to establish multitudinous severe infections in immunocompromised hosts, are grievous threats to human health. To tackle the enormous burden of disease caused by Acinetobacter spp., the headlong discovery and the advancement of novel therapies are of the essence at this juncture. The present study attempted to determine the antimicrobial effects of copper on the clinical isolates of Acinetobacter spp. (Iso-03 and Iso-04). Methods: The potential deployment of copper-based antibacterial strategies against Acinetobacter spp. was assessed by exposing the isolates to the increasing concentrations of CuSO4 (from 2.5 to 1.5 mM) in liquid culture (M9 minimal medium) for 6 h and also through the exposure of them on solid metal surfaces (stainless steel and copper coupons) for 75 min, wherein the copper sensitivity and resistance of the clinical isolates of Acinetobacter spp. were determined. Results: There was no interference with the growth of the isolates at the low concentrations, whereas the bacterial growth was affected by the high concentrations of CuSO4 at different levels. During the exposure on the solid metal coupons, no loss of viability of isolates was observed on stainless steel, however, the rapid death of isolates was discernible on copper surface, leading to a dramatic decrease in the number of colony-forming units (CFUs), eventually to the limit of detection (3 CFUs per coupon). Conclusion: This study substantiated that copper possesses antimicrobial properties which can be deployed in novel therapies for the prevention of the infections caused by Acinetobacter spp. and other emerging pathogens. Further studies on the sensitivity and resistance of Acinetobacter spp. to copper at the molecular genetics levels can open the door to better exploitation of this metal for the inhibition of the vigorous growth of drug-resistant Acinetobacter spp. [ABSTRACT FROM AUTHOR]

Recently, applying artificial intelligence technologies in various fields of production has drawn an upsurge of research interest due to the increase for smart factory and artificial intelligence technologies. A great deal of effort is being made to introduce artificial intelligence algorithms into the defect detection task. Particularly, detection of defects on the surface of metal has a higher level of research interest compared to other materials (wood, plastics, fibers, etc.). In this paper, we compare and analyze the speed and performance of defect classification by combining machine learning techniques (Support Vector Machine, Softmax Regression, Decision Tree) with dimensionality reduction algorithms (Principal Component Analysis, AutoEncoders) and two convolutional neural networks (proposed method, ResNet). To validate and compare the performance and speed of the algorithms, we have adopted two datasets ((i) public dataset, (ii) actual dataset), and on the basis of the results, the most efficient algorithm is determined. [ABSTRACT FROM AUTHOR]

We re-investigate the image charge model of Iori and Corni (Iori and Corni, J. Comput. Chem. 2008, 29, 1656). We find that a simple symmetrization of their model allows to obtain quantitatively correct results for the electrostatic interaction of a water molecule with a metallic surface. This symmetrization reduces the magnitude of the electrostatic interaction to less than 10% of the total interaction energy. © 2017 Wiley Periodicals, Inc.

The article provides an analysis of the physical processes underlying the operation of the measuring transducer, with a time based information presentation. A mathematical model is developed that describes the process of free oscillation attenuation excited in the LC-contour of primary measuring transducer, and analyzes and evaluates the influence of external factors that influence the measurement results. The ways of elimination of their influence on the results of measuring control are offered.

A cohesive energy model for the nanostructure formation by the impact of slow highly charged ions (charge q ≫ 1 , velocity v ≤ 0. 4 a.u.) on a metal surface is formulated. The first step of the given model calculates the neutralization energy and deposited kinetic energy. We use the time-symmetrized quantum approach (two-state vector model) and the micro-staircase model in the analysis of the cascade neutralization above the surface. We consider the elastic collisions below the surface using the charge dependent ion-target atom interactions. Based on the critical velocities defined within the model we discuss the shape of the formed nanostructures; the model predicts the appearance of the nanohillocks for the velocities lower than the critical one and the nanocraters for higher velocities. In the second step of the model, we assume that the total energy deposition results in an alteration of the cohesive energy of the active volume of the solid. In the case of hillock formation, the neutralization, as a dominant process, reduces the strength of the metallic bonds; the rearrangement of atoms leads to the rise of the volume above the surface. In the process of crater formation the atomic collisions below the surface play the most significant role. The strength of the metallic bonds inside the crater volume approaches zero and a number of atoms are ejected from the surface. The proposed mechanisms enable us to predict a simple relation between the initial and final cohesive energies dependent on the metallic crystal structures and to obtain a general expression for the nanostructure diameters. According to the model, we get a nontrivial q and v dependence of the diameters for both nanostructure types. The results are in good agreement with the experimental data obtained for hillocks and craters formed on the titanium and gold targets (nanolayers) by the impact of Xe q + ions. • Cohesive energy model formulation for nanostructure creation on metal induced by slow HCI • The effect of various metal targets and ionic charges on particular nanostructures • The first report of the general expression of nanostructure diameters. • {q, v}-dependence of the nanostructure diameters [ABSTRACT FROM AUTHOR]

The generalized results of studying the processes of the formation of nanolayers of silicon, tantalum, aluminum, and chromium oxides obtained by the method of molecular layering (atomic layer deposition) on the surface of metal matrices—tantalum, aluminum and chromium—are presented. The conditions for the layer mechanism of the formation of oxide nanostructures are determined. Their dielectric characteristics are assessed. [ABSTRACT FROM AUTHOR]

A 2D barcode is a reliable way to provide lifetime traceability of parts that are exposed to harsh environments. However, there are considerable challenges in adopting mobile cameras to read symbols directly marked on metal surfaces. Images captured by mobile cameras are usually of low quality with poor contrast due to the reflective surface of 2D barcode symbols. To deal with this problem, a novel laser-marked Data Matrix symbols reading method based on deep learning is proposed for mobile phone captured images. Utilizing the barcode module features, we train different convolutional neural network (CNN) models to learn the colors of two adjacent modules of a Data Matrix symbol. Depending on whether the colors of the two adjacent modules are the same or not, an edge image is transformed from a square grid, which is the same size as the barcode. A correction method based on the KM algorithm is used to get a corrected edge image, which helps to reconstruct the final barcode image. Experiments are carried out on our database, and the results show that the proposed algorithm outperforms in high accuracy of barcode recognition. [ABSTRACT FROM AUTHOR]

The current trend in automated optical inspection (AOI) systems employs deep learning models to detect defects on a metal surface. The setback of deep learning models is that they are time-consuming because the images obtained after every lighting adjustment must be used to train the deep learning models again and confirm whether the detection results have improved. To save the time spent using datasets to train deep networks, we proposed a comprehensive assessment score that combines defect visibility, visibility distribution, and overexposure based on the operation principles of convolution neural networks. It can be used to assess whether the training image dataset can improve the defect detection rate of the deep learning model such as You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster Region-based Convolutional Neural Network (Faster R-CNN) without training defect image datasets. We collected all of the weight combinations with the right prediction results and used linear regression to obtain the optimal weight coefficients. We found that visibility and overexposure had a greater impact on the comprehensive assessment score. We compared the proposed approach with existing image quality assessment methods, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), natural image quality evaluator (NIQE), perception-based quality evaluator (PIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). The experiment results indicated that our proposed comprehensive assessment score is more correlated to the F2-score of the detection models than the IQA methods by the verification methods of Spearman Rank Correlation Coefficient (SRCC), Pearson Correlation, and Kendall Correlation. Thus, referring to this index during the collection of image data and choosing datasets with the highest score to train the model will produce better detection accuracy.

The chemical and mechanical methods of preparing metal surfaces before gluing have been examined. The strength test results for shear of adhesive bonds of aluminum and steel samples, the surfaces shot of which were prepared by grinding, shot-blasting with copper slag and chemical treatment in aqueous solutions of acids before gluing. The best results for shear strength of adhesive bonds are observed for samples whose surfaces were subjected to shot blasting with a copper slag, the worst ones are after chemical method of preparing in aqueous solutions of acids. [ABSTRACT FROM AUTHOR]

One of the important characteristics of the surface properties of metal parts subjected to friction is hardness. Hardness measurements are important for determining the operational characteristics of parts and monitoring the technological regimes of surface modification. However, hardness measurements of thin modified layers made by different methods can lead to differences in measurement results. The aim of the article was to study the hardness of a metal surface modified with ultrafine particles of minerals by two different methods (instrumental indentation and Vickers hardness measurement) and a comparative analysis of the measurement results obtained by these methods.Standard Vickers hardness measurements at loads of 0.025, 0.1 and 0.5 kgf showed a qualitative difference between the hardness values of the two samples modified with different mixtures of ultrafine particles of minerals and a large heterogeneity of the hardness values over the area. By the method of instrumental hardness, standard measurements were performed without preliminary selection of the indentation site (at a load of 1.05 N) and measurements during indentation into even sections (at low loads of 10 mN).It is noted that the high precision of measurements implemented by instrumental indentation, due to the large roughness of the samples, leads to large values of the error in calculating the measurement results. An additional difference in the results of measurements performed by two methods at shallow indentation depths may be due to the fact that the object under study has a complex structure consisting of a metal matrix and particles distributed over the depth of the sample. A possible way out of the situation lies in the transition from the use of hardness measures when calibrating instruments to standard samples of properties for which the constancy of mechanical properties in the measured range of indentation depths will be ensured, but which are not yet available in research practice. Therefore, at present, when carrying out work related to the search for optimal conditions for obtaining thin wear-resistant layers on the surface of metals modified with ultrafine particles of minerals, comparative measurements performed by one measurement method are recommended.

One of the important tasks is to deliver to the consumer a calibrated high-quality metal in a saleable condition, which requires protection from atmospheric corrosion and adverse climatic factors, protection from contamination and mechanical damage for the period of transportation to the consumer by motor transport, and storage at intermediate warehouses before going into production. A comprehensive system approach is needed to preserve the quality and appearance of the products on the way from the manufacturer to the consumer. The use of modern packaging materials, such as paper with corrosion inhibitor and polypropylene fabric reinforcement, will allow to obtain permanent guaranteed protection of the rolled surface and expands the possibilities of transportation and sales markets of metal products. The testing stage of the corrosion inhibitor included the determination of its protective properties. It is established that the advantage of packaging of rolled products in anticorrosive paper is that it, combining the functions of packaging means and means of preservation, allows you to completely abandon the expensive and time-consuming preservation of metal products with oils and greases. The use of a volatile corrosion inhibitor in the composition of the paper provides full protection of rolled products.

Quartz crystal microbalance (QCM) is one of the powerful tools for the studies of molecular recognition and chiral discrimination. Its efficiency mainly relies on the design of the functional sensitive layer on the electrode surface. However, the organic sensitive layer may easily cause dissipation of oscillation or detachment and weaken the signal transfer during the molecular recognition processes. In this work, we reveal for the first time that the bare metal surface without the organic selector layer has the capability for chiral recognition in the QCM system. During the adsorption of various chiral amino acids, relatively higher selectivity of D‐enantiomers on gold (Au) surface was shown by the QCM detection. Based on analyses of the surface crystalline structure and density functional theory calculations, we demonstrate that the chiral nature of Au surface plays an important role in the selective binding of specific D‐amino acids. These results may open new insights on chiral detection by QCM system. It will also promote the construction of novel chiral sensing systems with both efficient detection and separation capability. [ABSTRACT FROM AUTHOR]

Consistency between density functional theory calculations and X-ray photoelectron spectroscopy measurements confirms our predications on the undercoordination-induced local bond relaxation and core level shift of alkali metal, which determine the surface, size and thermal properties of materials. Zone-resolved photoelectron spectroscopy analysis method and bond order-length-strength theory can be utilized to quantify the physical parameters regarding bonding identities and electronic property of metal surfaces, which allows for the study of the core-electron binding-energy shifts in alkali metals. By employing these methods and first principle calculation in this work, we can obtain the information of bond and atomic cohesive energy of under-coordinated atoms at the alkali metal surface. In addition, the effect of size and temperature towards the binding-energy in the surface region can be seen from the view point of Hamiltonian perturbation by atomic relaxation with atomic bonding. [ABSTRACT FROM AUTHOR]

Considerably slow H 2 O 2 evaporation leads to a decrease in the oxidation ratio of NO through H 2 O 2 thermal decomposition. Therefore, the metal surfaces of Copper h62, Aluminium 5052, Steel 201, and Steel q235 were used to promote H 2 O 2 thermal decomposition and NO oxidation. The effects of reaction temperature, H 2 O 2 solution concentrations, H 2 O 2 :NO ratios, and metal surface inclination angles on NO oxidation were explored through H 2 O 2 thermal decomposition. The evaporation and decomposition rates of H 2 O 2 significantly influenced NO oxidation. After the addition of metal surfaces to the reactor, the NO oxidation ratio considerably enhanced, and NO concentration fluctuations disappeared under most conditions. The hydrophilicity of the metal surfaces can help the H 2 O 2 solution spread rapidly on metal surfaces. The liquid film thickness decreased, which led to an increase in the H 2 O 2 evaporation rate. Consequently, NO oxidation ratios improved. Metal surfaces promoted H 2 O 2 decomposition to produce OH, and relatively more NO was oxidised. The highest NO oxidation ratio was 87 %, which was acquired using the 15 % H 2 O 2 solution at 500 ℃. When temperature was higher than 300 ℃, NO oxidation ratios on different metal surfaces were similar. The inclination angle θ of metal surfaces can considerably affect NO oxidation ratios. When the metal surface was horizontal, the NO oxidation ratio was higher than that when metal surface was placed at other angles, and the oxidation effect of NO was relatively more stable. The longer the droplets stayed on metal surfaces, the smaller were the NO concentration fluctuations. [ABSTRACT FROM AUTHOR]

A recent experiment has reported that body-centered cubic (bcc)-structured Ag is realized by bending face-centered cubic (fcc)-structured Ag nanowires (Sun et al., 2022). However, the bcc phase has been observed only near the Ag surface. Here, we explore how the bcc phase is stabilized near the surface by compressions. Our first-principles calculations for noble metals, Cu, Ag, and Au, indicate that body-centered tetragonal (bct) rather than bcc structure is preferred due to the surface effect. The bct-fcc boundary treated as a fixed boundary condition is necessary to thermodynamically stabilize the bct phase of Ag nanowire. The correlation between crystal structure and electron density-of-states is also discussed for three noble metals. [Display omitted] [ABSTRACT FROM AUTHOR]

To prepare durable superhydrophobic coatings with low-cost is of great importance for its practical application. How to improve the mechanical endurance by a facile method is rarely mentioned in reports. Achieving thicker coating with multi-layer micro-nano structures is critical for obtaining the robust superhydrophobic coating due to the high abrasion resistance to mechanical damage. Herein, a superhydrophobic coating with a multi-layer self-similar structure was fabricated by a facile blending and spraying method. Epoxy resin, neutral silicone glue, polysiloxane and nano-SiO 2 are the main composition of the ESPS coating which are important for the multi-layer micro-nano structure construction and adhesion stability between the coating and the substrate. The water contact angle (WCA) and sliding angle (SA) of the ESPS coating were 162.5 ± 0.5° and 4.6 ± 0.4°, respectively. Notably, the ESPS coating with four layers can be abraded with 1500-grid sandpaper under the load of 400 Pa for 24 m. In addition, the prepared samples were put in an outdoor environment for 180 days and exhibited excellent durability and stability. These outstanding properties could be attributed to the hierarchical self-similar structure of the composited coating and the good resistance and adhesion stability provided by polysiloxane. Additionally, the ESPS coating could restore its superhydrophobic characteristic by self-healing realized by the neutral silicone glue in the composition. The good stability and operability of the ESPS coating make it applicable to a wide range of applications. • A multi-layer self-similar superhydrophobic coating was prepared by a simple, cheap and harmless method. • The mechanical durability of the superhydrophobic coating can be improved by increasing the longitudinal thickness. • Plasticizer was used to improve the ductility of epoxy resin which is important for its superhydrophobic performance. • Self-healing property of the coating could be realized by the neutral silicone glue in the composite. [ABSTRACT FROM AUTHOR]

The modern industrial production of the Republic of Belarus is characterized by the absence of its own raw material base and significant dependence on imported energy carriers and material resources supplied at world prices. When working in such conditions, production efficiency can be achieved through all-round economy and creation of modern energyand resource-saving technologies. However implementation of such technologies, for example, laser cutting and welding, cladding, flame spraying, painting, etc. directly depends on quality of cleaning metal surfaces from corrosion. Theoretical and experimental studies conducted at the Department of Shipbuilding and Hydraulics of the Belarusian National Technical University have shown that it is very economical to remove corrosion products from metal surfaces using new technology of reverse jet cleaning. The reverse jet cleaning technology is based on a physical principle which presupposes that a jet of working fluid (pulp based on river sand or bentonite clay) rotates 180º when it hits the surface to be cleaned and it leads to an increase in jet impact on the surface to be cleaned by 1.5–2 times due to occurrence of a reactive component. In order to ensure a marked jet reversal an original case design has been developed which is characterized by a patent novelty. One of the main elements in this design is a confuser-shaped stream-forming device. Theoretical investigations on pressure losses of working fluid in a confuser channel which are based on the study of functional at the extremum have made it possible to obtain a dependence for calculation of an optimal cone angle at a turbulent mode of motion within the range of Reynolds numbers 4000

The need for environmentally friendly chemical products in daily needs encourages the production of its. The green-chemistry concept is using the process and produces chemical products that are ecofriendly. Including ecofriendly chemical products are base oil and additives for lubricants, grease, and fuels. The production is expected to reduce the consumption of mineral and synthetic base oils, so it will be biodegradable and renewable. This study compares the results of analysis of metallic surfaces immersed in the mixture of mineral and vegetable base oil, with the addition of rice bran oil bioadditive, ie epoxidized methyl ester (EME) and hydroxyl alkylbenzene sulphonic acid ester (HASE). The research method consists of preparing HASE; analyzing the effect of HASE and EME bioadditives addition on the mixture of base oil to the changing of metallic weight immersed in the mixture; determining the inhibition efficiency of the EME and HSAE additions; analyzing the metal surface using SEM-EDX (Scanning Electron Microscope) / (Energy Dispersive X-ray Spectrometry) to find images of microstructure and chemical compounds contained in specimens, and testing the metal difractogram immersed in base oil mixtures with bioadditive using XRD (X-Ray Diffraction). SEM test results of carbon steel immersed in a mixture of base oil and bioadditives show corrosion in which the metal surface color immersed in EME bioadditive mixtures is brighter. EDX spectra of metal sample surfaces immersed in a mixture of base oil, EME and HASE contain carbon (C) and iron (Fe). The carbon content in carbon steel samples immersed in the mixtures and HASE is higher. XRD test results show Fe2O3 phases in carbon steel samples immersed in the HASE bioadditive mixture are higher than in EME. While Fe3O4 phases in carbon steel samples immersed in the EME bioadditive mixture are higher than Fe2O3 phases in samples immersed in HASE bioadditive mixture.

Cases of a particle in a one-dimensional square well – infinitely deep and with finite depth – are also analyzed in detail. As an example, the adsorption of a hydrogen atom on a metal surface by a qualitative and accurate solution of the problem is considered. [ABSTRACT FROM AUTHOR]

In this paper, a widely applicable method that has promise in endowing various metals with oil-water separation ability by femtosecond laser processing is reported. We take an iron sheet as an example to show how to use a femtosecond laser to prepare underwater superoleophobic microstructure on metal substrates and then achieve oil-water separation. An array of through microholes was previously prepared on the iron sheet by a mechanical drilling process. Then, rough nanostructures were created on the surface of the porous sheet by femtosecond laser ablation, resulting in excellent superoleophobicity in water. When the mixture of water and oil was poured onto the porous underwater superoleophobic metal sheet, only the water in the mixture could pass through the sheet while the oil was intercepted, thus the oil/water mixture was successfully separated with high efficiency. Such a novel preparation process and separation manner can extend to different metal substrates. We believe that a wide range of metals like iron sheet can be potentially endowed with oil-water separation ability by femtosecond laser processing because femtosecond laser can process almost all of the metals.

A 2D barcode is a reliable way to provide lifetime traceability of parts that are exposed to harsh environments. However, there are considerable challenges in adopting mobile cameras to read symbols directly marked on metal surfaces. Images captured by mobile cameras are usually of low quality with poor contrast due to the reflective surface of 2D barcode symbols. To deal with this problem, a novel laser-marked Data Matrix symbols reading method based on deep learning is proposed for mobile phone captured images. Utilizing the barcode module features, we train different convolutional neural network (CNN) models to learn the colors of two adjacent modules of a Data Matrix symbol. Depending on whether the colors of the two adjacent modules are the same or not, an edge image is transformed from a square grid, which is the same size as the barcode. A correction method based on the KM algorithm is used to get a corrected edge image, which helps to reconstruct the final barcode image. Experiments are carried out on our database, and the results show that the proposed algorithm outperforms in high accuracy of barcode recognition.