Assessing the corrosion protection property of coatings loaded with corrosion inhibitors using the real-time atmospheric corrosion monitoring technique

The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties of organic coatings.This study compared a bare epoxy coating with one containing zinc phosphate corrosion inhibitors,both applied on ACM sensors,to observe their corrosion protection properties over time.Coatings with artificial damage via scratches were exposed to immersion and alternating dry and wet environments,which allowed for monitoring galvanic corrosion currents in real-time.Throughout the corrosion tests,the ACM currents of the zinc phosphate/epoxy coating were considerably lower than those of the blank epoxy coating.The trend in ACM current variations closely matched the results obtained from regular electrochemical tests and surface analysis.This alignment highlights the potential of the ACM technique in evaluating the corrosion protection capabilities of organic coatings.Compared with the blank epoxy coating,the zinc phosphate/epoxy coating showed much-decreased ACM current values that confirmed the effective inhibition of zinc phosphate against steel corrosion beneath the damaged coating.

Broadband and high efficiency metal multi-layer dielectric grating based on non-quarter-wave coatings as a reflective mirror

This article deals with designing a broadband and high efficiency metal multi-layer dielectric grating （MMDG） used to compress and stretch an ultrashort laser pulse. The diffraction characteristics of the IVIMDG are analysed by using the rigorous coupled-wave method. The multi-layer dielectric used as the reflective mirror is made up of non- quarter-wave coatings. Taking the diffraction efficiency of the -1 order as the value of merit function, the parameters such as groove depth, residual thickness, duty cycle, and reflective mirror are optimized to obtain broadband and high diffraction efficiency. The optimized MMDG shows an ultra-broadband working spectrum with the average efficiency exceeding 97% over 160 nm wavelength centred at 1053 nm and TE polarization. The optimized MMDG should be useful for chirped pulse amplification.

Electrodeposition of multi-layer Pd-Ni coatings on 316L stainless steel and their corrosion resistance in hot sulfuric acid solution

Pd-Ni coating shows good corrosion resistance in strong corrosion environments.However,in complex aggressiveenvironments,the performance of the coatings is limited and further improvement is necessary.The effects of the applied platingcurrent density on the composition,structure and properties of Pd-Ni coatings were studied.By changing the current density in thesame bath,multi-layer Pd-Ni coatings were prepared on316L stainless steel.Scanning electronic microscopy,weight loss tests,adhesion strength,porosity and electrochemical methods were used to study the corrosion resistance of the films prepared bydifferent coating methods.Compared with the single layer Pd-Ni coating,the multi-layer coatings showed higher microhardness,lower internal stress,lower porosity and higher adhesive strength.The multi-layer Pd-Ni coating showed obviously better corrosionresistance in hot sulfuric acid solution containing Cl-.

Optimization of Multi-layer AR Coatings for GaInP/GaAs Tandem Solar Cells

The AR coatings for GaInP/GaAs tandem solar cell are simulated.Results show that,under the condition of the lack of suitable encapsulation, a very low energy loss could be reached on MgF2/ZnS system; in the case of glass encapsulation,the Al2O3/ZrO2 and Al2O3/TiO2 systems are appropriate choice; for AlInP window layer,the thickness of 30 nm is suitable.

Preparation and Characterization of TiO_2/TiN/TiO_2 Multi-layer Solar Control Coatings Deposited by D.C. Reactive Magnetron Sputtering at Different Substrate Temperature

Transparent TiO2/TiN/TiO2 multi-layer solar control coatings were prepared on normal soda-lime-silica float glass substrate by using d.c. reactive magnetron sputtering at substrate temperature ranging from room temperature to 620℃ . The dependence of optical properties of the coatings and the coating composition, on the substrate temperature was studied. The results of the optical properties show that as the substrate temperature increases, a visible transmittance as high as 65% can be obtained. When the substrate temperature is higher than 370℃ , the infrared reflectance decreases. The results of X-ray photoelectron spectroscopy (XPS) show that when the substrate temperature is higher than 520℃ in oxygen atmosphere, the formation of thin surface over-layers (TiNxOy,) on top of the TiN films can be observed. When the substrate temperature is at 62℃ , the oxynitride become TiO2, which results in the optical degradation of TiN layer in infrared reflectance.

High corrosion and wear resistant electroless Ni–P gradient coatings on aviation aluminum alloy parts

A Ni–P alloy gradient coating consisting of multiple electroless Ni–P layers with various phosphorus contents was prepared on the aviation aluminum alloy. Several characterization and electrochemical techniques were used to characterize the different Ni–P coatings’ morphologies, phase structures, elemental compositions, and corrosion protection. The gradient coating showed good adhesion and high corrosion and wear resistance, enabling the application of aluminum alloy in harsh environments. The results showed that the double zinc immersion was vital in obtaining excellent adhesion (81.2 N). The optimal coating was not peeled and shredded even after bending tests with angles higher than 90°and was not corroded visually after 500 h of neutral salt spray test at 35℃. The high corrosion resistance was attributed to the misaligning of these micro defects in the three different nickel alloy layers and the amorphous structure of the high P content in the outer layer. These findings guide the exploration of functional gradient coatings that meet the high application requirement of aluminum alloy parts in complicated and harsh aviation environments.

Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Design multifunctional Mg–Zr coatings regulating Mg alloy bioabsorption

Magnesium(Mg)alloys are widely used for temporary bone implants due to their favorable biodegradability,cytocompatibility,hemocompatibility,and close mechanical properties to bone.However,rapid degradation and inadequate strength limit their applicability.To overcome this,the direct current magnetron sputtering technique is employed for surface coating in Mg-based alloys using various zirconium(Zr)content.This approach presents a promising strategy for simultaneously improving corrosion resistance,maintaining biocompatibility,and enhancing strength without compromising osseointegration.By leveraging Mg’s inherent biodegradability,it has the potential to minimize the need for secondary surgeries,thereby reducing costs and resources.This paper is a systematic study aimed at understanding the corrosion mechanisms of Mg–Zr coatings,denoted Mg-xZr(x=0–5 at.%).Zr-doped coatings exhibited columnar growth leading to denser and refined structures with increasing Zr content.XRD analysis confirmed the presence of the Mg(00.2)basal plane,shifting towards higher angles(1.15°)with 5 at.%Zr doping due to lattice parameter changes(i.e.,decrease and increase of“c”and“a”lattice parameters,respectively).Mg–Zr coatings exhibited“liquidphilic”behavior,while Young’s modulus retained a steady value around 80 GPa across all samples.However,the hardness has significantly improved across all samples’coating,reaching the highest value of(2.2±0.3)GPa for 5 at.%Zr.Electrochemical testing in simulated body fluid(SBF)at 37℃ revealed a significant enhancement in corrosion resistance for Mg–Zr coatings containing 1.0–3.4 at.%Zr.Compared with the 5 at.%Zr coating which exhibited a corrosion rate of 32 mm/year,these coatings displayed lower corrosion rates,ranging from 1 to 12 mm/year.This synergistic enhancement in mechanical properties and corrosion resistance,achieved with 2.0–3.4 at.%Zr,suggests potential ability for reducing stress shielding and controlled degradation performance,and consequently,promising functional biodegradable materials for temporary bone implants.

Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

Surface Metallization of Glass Fiber(GF)/Polyetheretherketone(PEEK) Composite with Cu Coatings Deposited by Magnetron Sputtering and Electroplating

Surface metallization of glass fiber(GF)/polyetheretherketone(PEEK)[GF/PEEK] is conducted by coating copper using electroplating and magnetron sputtering and the properties are determined by X-ray diffraction(XRD), scanning electron microscopy(SEM), and electron backscatter diffraction(EBSD).The coating bonding strength is assessed by pull-out tests and scribing in accordance with GB/T 9286-1998.The results show that the Cu coating with a thickness of 30 μm deposited on GF/PEEK by magnetron sputtering has lower roughness, finer grain size, higher crystallinity, as well as better macroscopic compressive stress,bonding strength, and electrical conductivity than the Cu coating deposited by electroplating.

Bio-Based Waterborne Poly(Vanillin-Butyl Acrylate)/MXene Coatings for Leather with Desired Warmth Retention and Antibacterial Properties

This study presents a solvent-free,facile synthesis of a bio-based green antibacterial agent and aromatic monomer methacrylated vanillin(MV)using vanillin.The resulting MV not only imparted antibacterial properties to coatings layered on leather,but could also be employed as a green alternative to petroleum-based carcinogen styrene(St).Herein,MV was copolymerized with butyl acrylate(BA)to obtain waterborne bio-based P(MV-BA)miniemulsion via miniemulsion polymerization.Subsequently,MXene nanosheets with excellent photothermal conversion performance and antibacterial properties,were introduced into the P(MV-BA)miniemulsion by ultrasonic dispersion.During the gradual solidification of P(MV-BA)/MXene nanocomposite miniemulsion on the leather surface,MXene gradually migrated to the surface of leather coatings due to the cavitation effect of ultrasonication and amphiphilicity of MXene,which prompted its full exposure to light and bacteria,exerting the maximum photothermal conversion efficiency and significant antibacterial efficacy.In particular,when the dosage of MXene nanosheets was 1.4 wt%,the surface temperature of P(MV-BA)/MXene nanocomposite miniemulsioncoated leather(PML)increased by about 15℃ in an outdoor environment during winter,and the antibacterial rate against Escherichia coli and Staphylococcus aureus was nearly 100%under the simulated sunlight treatment for 30 min.Moreover,the introduction of MXene nanosheets increased the air permeability,water vapor permeability,and thermal stability of these coatings.This study provides a new insight into the preparation of novel,green,and waterborne bio-based nanocomposite coatings for leather,with desired warmth retention and antibacterial properties.It can not only realize zerocarbon heating based on sunlight in winter,reducing the use of fossil fuels and greenhouse gas emissions,but also improve ability to fight off invasion by harmful bacteria,viruses,and other microorganisms.

Developing an atmospheric aging evaluation model of acrylic coatings:A semi-supervised machine learning algorithm

To study the atmospheric aging of acrylic coatings,a two-year aging exposure experiment was conducted in 13 representative climatic environments in China.An atmospheric aging evaluation model of acrylic coatings was developed based on aging data including11 environmental factors from 567 cities.A hybrid method of random forest and Spearman correlation analysis was used to reduce the redundancy and multicollinearity of the data set by dimensionality reduction.A semi-supervised collaborative trained regression model was developed with the environmental factors as input and the low-frequency impedance modulus values of the electrochemical impedance spectra of acrylic coatings in 3.5wt%NaCl solution as output.The model improves accuracy compared to supervised learning algorithms model(support vector machines model).The model provides a new method for the rapid evaluation of the aging performance of acrylic coatings,and may also serve as a reference to evaluate the aging performance of other organic coatings.

Target Controllability of Multi-Layer Networks With High-Dimensional Nodes

This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.

A flexible ultra-broadband multi-layered absorber working at 2 GHz-40 GHz printed by resistive ink

A flexible extra broadband metamaterial absorber(MMA)stacked with five layers working at 2 GHz–40 GHz is investigated.Each layer is composed of polyvinyl chloride(PVC),polyimide(PI),and a frequency selective surface(FSS),which is printed on PI using conductive ink.To investigate this absorber,both one-dimensional analogous circuit analysis and three-dimensional full-wave simulation based on a physical model are provided.Various crucial electromagnetic properties,such as absorption,effective impedance,complex permittivity and permeability,electric current distribution and magnetic field distribution at resonant peak points,are studied in detail.Analysis shows that the working frequency of this absorber covers entire S,C,X,Ku,K and Ka bands with a minimum thickness of 0.098λ_(max)(λ_(max) is the maximum wavelength in the absorption band),and the fractional bandwidth(FBW)reaches 181.1%.Moreover,the reflection coefficient is less than-10 dB at 1.998 GHz–40.056 GHz at normal incidence,and the absorptivity of the plane wave is greater than 80%when the incident angle is smaller than 50°.Furthermore,the proposed absorber is experimentally validated,and the experimental results show good agreement with the simulation results,which demonstrates the potential applicability of this absorber at 2 GHz–40 GHz.

Microstructure of a Mo-Si-C-N multi-layered anti-oxidation coating on carbon/carbon composites by fused slurry

A Mo-Si-C-N multi-layered anti-oxidation coating was in-situ fabricated by introducing nitrogen atmosphere during the fused sintering of Mo-Si slurry pre-layer on carbon/carbon composites. The phase composition and microstructure of the Mo-Si-C-N coating were characterized by X-ray diffractometry, optical microscopy, scanning electron microscopy with energy dispersive spectroscopy, X-ray photoelectron spectroscopy, and transmission electron microscopy. The Mo-Si-C-N coating exhibited a three-layered structure. Besides the MoSi2/Si main-layer and the SiC bonding-layer, a surface layer of about 10 μm in thickness was synthesized on the coating surface. The surface layer mainly consisted of SiC nanowires and contained some Si3N4 and Si phases. SiC nanowires of 10 to 200 nm in diameter presented a terrace and distortion structure. Transmission electron microscopy indicated that the SiC nanowires grew along the preferred 〈111〉 direction. During oxidation test, SiC nanowires transmuted into SiO2 glass, which can play an important role in improving the oxidation resistance of C/C composites.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Multi-layer network embedding on scc-based network with motif

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Multiscale Simulation of Microstructure Evolution during Preparation and Service Processes of Physical Vapor Deposited c-TiAlN Coatings

Physical Vapor Deposited(PVD)TiAlN coatings are extensively utilized as protective layers for cutting tools,renowned for their excellent comprehensive performance.To optimize quality control of TiAlN coatings for cutting tools,a multi-scale simulation approach is proposed that encompasses the microstructure evolution of coatings considering the entire preparation and service lifecycle of PVD TiAlN coatings.This scheme employs phase-field simulation to capture the essential microstructure of the PVD-prepared TiAlN coatings.Moreover,cutting simulation is used to determine the service temperature experienced during cutting processes at varying rates.Cahn-Hilliard modeling is finally utilized to consume the microstructure and service condition data to acquaint the microstructure evolution of TiAlN coatings throughout the cutting processes.This methodology effectively establishes a correlation between service temperature and its impact on the microstructure evolution of TiAlN coatings.It is expected that the present multi-scale numerical simulation approach will provide innovative strategies for assisting property design and lifespan prediction of TiAlN coatings.