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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]

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 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]

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]

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]

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.

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., Competing Interests: The authors declare there are no competing interests., (©2024 Chen et al.)

Surface potential is rarely investigated as an independent factor in influencing tissue regeneration on the metal surface. In this work, the surface potential on the titanium (Ti) surface was designed to be tailored and adjusted independently, which arises from the ferroelectricity and piezoelectricity of poled poly(vinylidene fluoride-trifluoroethylene) (PVTF). Notably, it is found that such controllable surface potential on the metal surface significantly promotes osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) in vitro as well as bone regeneration in vivo. In addition, the intracellular calcium ion (Ca 2+ ) concentration measurement further proves that such controllable surface potential on the metal surface could activate the transmembrane calcium channels and allow the influx of extracellular Ca 2+ into the cytoplasm. That might be the reason for improved osteogenic differentiation of BMSCs and bone regeneration. These findings reveal the potential of the metal surface with improved bioactivity for stimulation of osteogenesis and show great prospects for fabricable implantable medical devices with adjustable surface potential.

• Sublattice symmetry breaking and hybridization asymmetry compensate each other. • Sublattice symmetry breaking is dominant for small graphene-metal separation. • Hybridization dominates gap opening for large/equilibrium graphene-metal separation. • Hybridization is totally asymmetric in atop structures of graphene/Cu(111). Gap opening at the Dirac point of graphene on metals has been ascribed to hybridization, sublattice symmetry breaking, or combination of both. However, how and what to extent gap opens under the influence of these effects has been a question in controversy. In this paper, we explore gap opening in graphene on metals using a simple tight-binding (TB) model for a hypothetical heterobilayer, which mimics a system of graphene/metal. A theoretical framework for the gap opening is obtained using exact solutions to the TB-model Hamiltonian through small-parameter expansions. This result is formally similar to that obtained for an ad hoc model graphene with pseudospin asymmetry originating from the spin-orbit proximity effect, which is not considered in the present analysis but is taken over by more substantial effect of orbital-hybridization indispensable for the gap opening in graphene on metals. The present result reveals, for the first time, that sublattice symmetry breaking and hybridization asymmetry compensate each other and disruptively contribute to the gap opening at the Dirac point of graphene on metals. The picture is in marked contrast to the previous ones such as hybridization-induced sublattice symmetry breaking or an additive model, in which sublattice symmetry breaking and hybridization additively contribute to the gap opening. The results obtained by the present TB-model analysis are successfully used to interpret the results of ab initio density functional theory (DFT) calculations for graphene/Cu(111) and thereby, for the first time, to discriminate sublattice symmetry breaking and hybridization asymmetry explicitly. We also find, through these analyses of the DFT calculations, that hybridization asymmetry dominates gap opening at the Dirac point for large graphene-substrate separations (d), while sublattice symmetry breaking is dominant for small d , and both are balanced with each other in between to yield a small gap, which is practically closed in the system of graphene/Cu(111). Another point revealed by those analyses is that sublattice symmetry breaking and hybridization are necessarily in combination with each other and are together large or small depending on interface structures. All these results are expected to provide a useful insight into gap opening at the Dirac point of graphene in other systems including graphene/metal superstructures resulting from lattice mismatch, misorientation, intercalation, or other structural modifications. [Display omitted] [ABSTRACT FROM AUTHOR]

Adhesion between metal surfaces and soil can have significant effects on soil-tool interaction. The cone pull-out test (CPT) and piston pull-out test (PPT) for adhesion assessment were used in this study on smectite, illite and kaolinite clays at different water contents. In the CPT, the tangential adhesion stress (TAS) and cone adhesion (CA) were measured. In the PPT, the normal adhesion stress (NAS) and piston adhesion (PA) were measured. The results showed that the NAS and TAS depended on the type of clay. The ratios of NAS to TAS for smectite and kaolinite had the largest and smallest values, respectively. For all three types, NAS and TAS decreased as the water content (W) decreased. An increase in the thickness of the water film at the soil-metal contact surface reduced the stress required to separate the metal from the soil. The relationship between CA-W and PA-W for the three clay types differed. For smectite, a change in water content had no effect on CA or PA. For illite, an increase in water content decreased the CA and PA. For kaolinite, a bell-shaped curve formed such that, as water content increased, where WLL, CA and PA decreased. For all three clay soil types, NAS and PA were higher than TAS and CA at a specific water content. This demonstrated the effect of the metal-to-soil contact geometry on the adhesion parameters. The NAS and TAS values were classified in terms of clogging potential. Clogging potential is the adhesion of soil to the cutting tools or conveying system. The selection of the use of the CPT or PPT for adhesion assessment depended on the phenomenon being studied. • The metal-to-soil contact geometry does affect on adhesion parameters. • The normal adhesion stress (NAS) was higher than the tangent adhesion stress (NAS). • The piston adhesion (PA) was higher than the cone adhesion (CA). • The NAS and TAS decreased as the water content decreased. • There is linear relationship between NAS and TAS. [ABSTRACT FROM AUTHOR]

• A color stability evaluation method for all parameter spaces without proposing reference points is proposed. • Direct laser color marking of barcodes colorized on Ti6Al4V and SS 304 surfaces are carried out. • The mechanism of color stability of laser color marking is investigated. The main problems of laser color marking are the reproducibility and stability. For this issue, the color stability of laser marking is investigated by experiments and simulations. A color stability evaluation model is established and turns out to be valid with mean squared error (MSE) of 0.67 and goodness of fit (R 2) of 0.88. The direct laser color marking of barcodes on the Ti6Al4V and stainless steel (SS) 304 surfaces are marked by a nanosecond laser, and hence the color stability evaluations are carried out by the MATLAB software. The influence of cell edge, cell area, and scanning speed on the color stability of laser marking is analyzed. The temperature distribution of the laser heating SS surface process is simulated. The results show that the comprehensive effect of the temperature and time makes the color stability fluctuate periodically with the scanning speed. The color with a large relative temperature difference has a low stability, while the color with a small relative temperature difference has a high stability. [ABSTRACT FROM AUTHOR]

• Mitigation of hydrogen effect during a fretting fatigue test in H 2 gas by the addition of carbon monoxide. • Significant reduction in tangential force coefficient by the addition of carbon monoxide to hydrogen environment. • Significant increase in the fretting fatigue strength. • Deposition of amorphous carbon by catalytic action of fretted surface. It is known that the fretting fatigue strength in hydrogen (H 2) gas is lower than that in air. The objective of this study is to mitigate the hydrogen effect during the fretting fatigue test in H 2 gas by adding carbon monoxide (CO) to the H 2 environment. The expected CO effect was reduction in the hydrogen uptake into the material following deactivation of the catalytic surface where H 2 molecules dissociate into H atoms. However, an unexpected result, which was unique in fretting, was obtained. The fretting fatigue test using JIS SUS304 austenitic stainless steel was performed in H 2 , argon (Ar), 2 vol% CO mixed H 2, and 2 vol% CO mixed Ar gases. The fretting fatigue strengths in the CO-mixed gases were significantly improved. For instance, the fretting fatigue life at σ a = 240 MPa was 8.8 × 105 cycles in the H 2 and 107 cycles or longer in the CO mixed H 2. The cause of the improved fretting fatigue strength was the formation of amorphous carbon between the contacting surfaces during the fretting. The carbon reduced the tangential force coefficient between the contacting surfaces from around 0.6 in the H 2 to 0.2 or less in the CO mixed H 2. The reduced tangential force resulted in improvement of the fretting fatigue strength. Although the detailed mechanisms of the carbon deposition were unclear, it is plausible that the catalytic action of the metal surface for the CO decomposition into C and O was activated by the effect of fretting, such as removal of the surface oxide layer and other chemomechanical effects. [ABSTRACT FROM AUTHOR]

• Computational and theoretical approaches are necessary to substantiate the experimental results. • Phytochemicals in a plant extract can be utilized to define and classify how plant-based inhibitors control corrosion. • DFT calculations supported the donor–acceptor adsorption and inhibition mechanism for plant extract. • Fukui analysis evidenced the adsorption-rich site of the atoms in the phytochemicals. • Surface orientation and adsorption of inhibitor molecules onto a metal surface can be studied using MD and MC simulations. The most significant component of metallic corrosion control in aqueous conditions is examining the adsorption mechanisms of inhibitor molecules at the metal-electrolyte interface. The use of computational techniques for metal corrosion inhibition research has progressively increased due to time, economic, and environmental constraints, and the inadequacy of experimental techniques to provide meaningful insight into metal surface-inhibitor interaction. Even though there is a wealth of research that employs computational evaluation, there is a lack of a comprehensive review, specifically to investigate the adsorption behavior of phytochemicals on metal substrates. There is still uncertainty regarding the techniques and steps required in Molecular Dynamics (MD) modeling for corrosion systems. The fundamental principles of MD simulation, adsorption mechanism, quantum calculations as well as summaries of major publications in anticorrosive studies are discussed in this paper. To substantiate the experimental results, this review discovered that computational and theoretical approaches are necessary. [ABSTRACT FROM AUTHOR]

[Display omitted] • The H 2 adsorption capacity of the studied clusters can be showed as Ru > Co > Cu > Ag. • Energetic interaction of the cluster with H 2 is more favorable than the H 2 adsorption on (1 1 1) surface of the same metals. • Experimental chemisorption finds greater adsorption of H 2 on the supported Co nanoparticle in comparison with bulk material. Dissociative adsorption of H 2 was carried out on the @ 13 cluster (@ = Ag, Co, Cu and Ru) according to the number of nH 2 molecules (n = 1–10) and it was compared with H2 adsorption on the surface (1 1 1) of the same metals using Density Functional Theory. The adsorption of H2 is energetically more favorable on the @ 13 clusters than on the surfaces for all metals studied. Charge population analysis and density of states were calculated to understand the electronic behavior of hydrogen on the @ 13 cluster and on the (1 1 1) surface. In all the cases studied, hydrogen takes more charge from the @ 13 metal cluster than on (1 1 1) surfaces. The ab-initio thermodynamics shows the favorable ranges of pressure, energy, and chemical potential for dissociative adsorption of H 2 for all the coverages studied on the metal clusters. Additionally, XPS and chemisorption results for supported nanoparticles and bulk material are presented for cobalt. [ABSTRACT FROM AUTHOR]

Understanding Extracellular Polymeric Substances (EPS) interaction on a well-defined chromium surface is of importance especially for biocorrosion processes. Adsorption of EPS extracted from Pseudoalteromonas NCIMB 2021 on Cr surfaces was investigated using in situ quartz crystal microbalance (QCM) and X-ray photoelectron spectroscopy (XPS). We show that EPS adsorption is an irreversible process. The amount of adsorbed EPS increases with increasing EPS concentration in solution. For low EPS concentration, the surface is only partially covered by EPS, whereas a continuous organic film of around 15 nm is formed at the surface for high EPS concentrations. An in-depth structuration of this organic layer is evidenced with a strong enrichment of proteins in the inner part and of polysaccharides in the outer part. Adhesion of Pseudoalteromonas NCIMB 2021 has been tested on Cr surfaces covered or not by EPS extracted from Pseudoalteromonas NCIMB 2021. EPS conditioning with a 15 nm film inhibits bacterial adhesion on Cr, showing that this organic film has efficient anti-adhesive properties. [Display omitted] • Irreversible adsorption of EPS extracted from Pseudoalteromonas on Cr surfaces. • Chemical composition of EPS not maintained after adsorption on the Cr surface. • In-depth structuration of the film: proteins inside and polysaccharides outside. • EPS conditioning with a 15 nm film inhibits bacterial adhesion on Cr. [ABSTRACT FROM AUTHOR]

[Display omitted] • The distribution of dangling OH-bonds can strongly change the adsorption stability. • There is intrinsic disorder in the orientation of dangling OH-bonds. • The competition between water-water and water-metal interactions plays a key role. Using the first-principles calculation, we have systematically investigated the orientation of dangling OH-bonds in the first adsorbed water layer on close packed surface of noble metals (gold, platinum and palladium). We find that, the distribution of up and down dangling OH-bonds can strongly change the adsorption stability. A specific distribution of dangling OH-bonds is always indispensable in various adsorption patterns for its stability. The in-plane arrangement of water molecules also has important influence on the orientation of dangling OH-bonds. More importantly, the disorder in the orientation (up or down) of dangling OH manifests a kind of zero-temperature residual entropy in some absorption structures. In essence, the residual entropy arises from the competition between water-water and water-metal interactions, which is different from the counterpart in ice crystals. [ABSTRACT FROM AUTHOR]