Surface Defects Research Articles

Core-shell structure and high rate performance of Ce-doped Li4Ti5O12 for lithium-ion battery anode materials

Lithium titanate (LTO) can be a very promising anode material for lithium-ion batteries (LSBs) due to its inherent ability to inhibit the growth of lithium dendrites as well as its unique “zero-strain” properties. Unfortunately, the low electronic conductivity of LTO leads to serious shortcomings in higher electrochemical demands. In this work, the Ce3+-doped C@Li4Ti5-xCexO12 (x = 0, 0.1, 0.15 and 0.2) anode materials synthesized by the hydrothermal method using carbon spheres as templates showed more significant improvement in both structural and electrochemical properties. The results demonstrate that electronic conductivity, lithium-ion diffusion rate, discharge specific capacity, discharge rate capability, and significant improvement stability of C@Li4Ti5-xCexO12 (x = 0.1, 0.15 and 0.2) electrodes. Among them, C@Li4Ti4.85Ce0.15O12 electrode exhibits the highest initial discharge specific capacity (250.86 mAh/g) at 0.1C, which is 1.28-fold that of C@ Li4Ti5O12 (195.94 mAh/g), and initial discharge capacity from 205.96 mAh/g to 170.39 mAh/g after 500 cycles, corresponding to 82.7 % of the initial stable discharge capacity. The outstanding performance of C@Li4Ti4.85Ce0.15O12 can be attributed to the lower interfacial impedance, higher electronic conductivity, high oxygen vacancy concentration, and moderate amount of Ce3+ doping can enhance the electrochemical activity. In addition, carbon sphere surface defects shown to be effective in improving lithium-ion storage. This work demonstrates that Ce3+ doping is an effective method to improve the electrochemical performance of LTOs and provides a more effective guide for designing and optimizing anode electrode materials for lithium-ion batteries

Research on corrosion behavior and biocompatibility of NiTi alloy processed with combined dealloying and annealing treatment

This paper presents a novel method to prepare hydrophilic nanoporous functional surface with combined dealloying and annealing treatment to improve the corrosion resistance and biocompatibility of NiTi components. Surface modification mechanism is discussed with microscopic characterization of NiTi nanoporous structures. Hydrophilic and biocompatible performance is achieved through specifically tailoring the dimensions and chemical compositions of nanoporous layer. The corrosion resistance of NiTi samples is investigated in 0.9 wt% NaCl solution and evaluated with potentiodynamic polarization and electrochemical impedance spectroscopy tests. The results indicate that material corrosion resistance is significantly enhanced with TiO2 formation during dealloying treatment, while annealing promotes the oxidation and densification of dealloyed layer raising the energy barrier for ion transportation during corrosion. Furthermore, as the dealloying temperature increases, the surface defects of the sample decrease resulting in better corrosion resistance. The research can provide guidance for NiTi surface modification to enhance its biomedical applications.

Nanocasting synthesis of mesoporous CeO2 particle abrasives from mesoporous SiO2 hard templates for enhanced chemical mechanical polishing performance

Mesoporous abrasive particles exhibit superior chemical mechanical polishing/planarization (CMP) performance, as compared with the corresponding bulk materials. Nevertheless, the lack of control over the shape and particle size or distribution extremely restricts the development of mesoporous abrasives and their potential applications. To overwhelm these drawbacks, pure and ytterbium-doped mesoporous ceria (mCeO2 and mCeYbO2) spheres with well-defined morphology and good uniformity were synthesized via a straightforward nanocasting strategy using high-quality mesoporous silica spheres as hard templates and metal nitrates as precursors. The resulting samples were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, selected area electron diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy, UV–vis diffuse reflectance spectroscopy, photoluminescence spectroscopy, Zeta potential, and N2 adsorption-desorption measurements. The as-prepared mCeO2 and mCeYbO2 products exhibited enriched surface defects (trivalent Ce, vacancy oxygen, hydroxyls) and polycrystalline porous frameworks with a surface area of up to 160 m2/g and pore size of 4–7 nm. Benefiting from these synergistic attributes, the flexible and defective particle abrasives enabled significant improvements in both surface quality and removal efficiency during oxide-CMP, as compared to commercial CeO2. A possible CMP mechanism was proposed on the basis of the physical contact state, adsorption and adhesion behavior, as well as tribochemical interaction occurred at CeO2–SiO2 interfaces. This work demonstrates highly promising abrasive materials advancement combining architecture and defect engineering towards high-performance oxide-CMP.

Autonomous Rail Surface Defect Identification Based on an Improved One-Stage Object Detection Algorithm

Autonomous Rail Surface Defect Identification Based on an Improved One-Stage Object Detection Algorithm

Synergistic impact of lanthanum and cerium co-doping ZnO for improved photocatalytic rhodamine B degradation efficiency under UV light

In this study, the influence of different La and Ce co-doping amounts on the structural, morphological, optical, and photodegradation properties of ZnO nanostructures was evaluated. Zn1-2xLaxCexO nanoparticles (NPs) were prepared using sol-gel auto-ignition process, where x = 0.00 (pure ZnO), 0.01 (LC1), 0.03 (LC3), and 0.05 (LC5). The X-ray diffraction (XRD) and Raman results revealed that the prepared ZnO NPs crystallize in the hexagonal wurtzite structure. The size of crystallites is affected by the increment of La and Ce and it decreased from 24.29 nm to 10.40 nm with the increase in the concentration of La and Ce. Transmission electron microscopy (TEM) showed that the samples exhibit an irregular distribution of NPs with a dominant spherical shape morphology. The simultaneous incorporation of La and Ce in ZnO NPs led to a slight variation in the absorption edge and a slight shift in the values of the band gap energy from 3.20 to 3.24 eV. Furthermore, a reduction in the recombination rate of photogenerated charge carriers and the creation of fewer additional defects upon the appropriate insertion of Ce and La ions are highlighted by the analysis of photoluminescence spectra, Nyquist plots, and transient photocurrent measurements. The photocatalytic activity of samples was evaluated against rhodamine B organic dye under UV light irradiation. The analysis showed that LC1 sample exhibited superior photocatalytic performance with 99.1% degradation efficiency and a degradation rate constant of 0.0762 min-1. This enhancement has been ascribed to the surface defects and the optimized crystallite size achieved, which help to generate reactive entities such as •OH and O2•− in addition to the great role of holes (h+) and impede the recombination of charge carriers e-/h+.

Development and evaluation of STRC coating for cooling asphalt pavement and mitigating urban heat island effects

To mitigate the negative impacts of urban heat island (UHI) effects on modern cities, this study developed and evaluated a novel cool coating, solar thermal reflective coatings (STRC), for reducing asphalt pavement temperatures. Firstly, the optical properties were characterized using UV-Vis-NIR spectroscopy, and the thermal performance of eight STRC formulations was assessed using a custom-built testing apparatus. Subsequently, the microstructure and elemental composition were examined using scanning electron microscopy (SEM) and energy dispersive spectrometer (EDS). Finally, the optimized coating was applied to asphalt concrete, and its long-term performance and stability were confirmed through durability tests, including water immersion test, temperature variation endurance test, and radiation cycle test. The results showed that chromium sesquioxide (Cr₂O₃), a critical pigment of STRC, exhibited a high near-infrared reflectance of 54.76 % and a lower visible reflectance of 10.22 %. Among the eight STRC formulations, STRC5 demonstrated balanced performance with a visible reflectance of 35.69 % and a near-infrared reflectance of 71.85 %. The microstructure of STRC5 was uniform with minimal surface defects. When STRC5 was applied to asphalt concrete, it achieved significant cooling effects, maintaining a mean temperature of 49.51 °C compared to 60.07 °C for uncoated samples, resulting in a maximum temperature difference of 10.56 °C after 4 h of light exposure. Durability test results confirmed the long-term performance and stability as the coating withstood extreme temperature fluctuations from −20 °C to 60 °C and 72 h of water immersion. In conclusion, STRC5 demonstrated great potential to effectively cool asphalt pavement and mitigate UHI effects.

On the performance and thermal stability of solar cell based on CIGS-Sb2S3 combination with >35% power conversion efficiency

This study presents a solar cell design utilizing a CIGS (copper-indium-gallium-selenide) absorber and Sb2S3 (antimony trisulfide) back surface field (BSF) layers, targeting high photovoltaic (PV) performance with an emphasis on minimum possible effect of ambient temperature. We simulated a solar cell design consisting of zinc oxide, surface defect layer, zinc sulfide, CIGS layer, and an Sb2S3 layer using SCAPS-1D software. Our findings indicate that 200 nm Sb2S3 layer and a 1600 nm CIGS layer is the preferred combination for achieving high PV performance and appropriate J-V characteristics of the proposed solar cell. For this design, the achieved values of VOC (open circuit voltage), JSC (short circuit current density), PCE (power conversion efficiency), and fill factor (FF) are 1.069V, 42.08 mA/cm2, 35.94%, and 80.31%, respectively. The PV performance of the proposed solar cell is substantially better than the solar cell design without BSF layer as well as recently reported (2023-24) solar cell designs. This study further examines the impact of elevated ambient temperatures (295–360 K) on the PV performance. Our simulation results show that operating the proposed solar cell at moderately elevated temperatures is not a significant issue owing to small power temperature coefficient (-0.034% per K), which is comparable to that of commercially available solar cells. These findings are expected to contribute to the ongoing advancement of PV solar cells, aiming for higher PCE and improved stability, including thermal stability. Proposed solar cell with low PTC and high PCE can be suitable for space applications where temperature fluctuation is severe, as well as in tropical areas.

Detection and classification of surface defects on hot-rolled steel using vision transformers

This study proposes a vision transformer to detect visual defects on steel surfaces. The proposed approach utilizes an open-source image dataset to classify steel surface conditions into six fault categories namely, crazing, inclusion, rolled in, pitted surface, scratches and patches. The defect images are first subject to resizing and then fed into a vision transformer subject to different hyperparameter configurations to determine the most optimal setting to render highest classification performance. The performance of the model is evaluated for different hyperparameter configurations, and the most optimal configuration is examined using the associated confusion matrices. It was observed that the proposed model presents a high overall accuracy of 96.39 % for detection and classification of steel surface faults. The study presents a descriptive insight into the vision transformer architecture and in addition, compares the performance of the current model with the results of other approaches suggested for application in literature. Vision transformers can serve as standalone approaches and suitable alternatives to the widely used convolution neural networks (CNNs) by actuating complex defect detection and classification tasks in real-time, enabling efficient and robust condition monitoring of a wide range of defects.

Augmenting human-guided progressive learning with machine vision systems for robust surface defect detection

Machine vision systems commonly utilize Convolutional Neural Networks (CNNs) for in-line surface defect detection of manufactured components. The prediction abilities of vision-based inspection systems deteriorate with time as the defect detection model trained on fixed image datasets fails to accommodate deviations. This paper proposes a human-guided progressive learning approach that systematically imparts learning of new features to the CNN-powered vision-based defect detection system. The approach augments the surface defect detection model with human intelligence, using an intuitive user interface to address model drift. The human expert monitors the trained model performance under specific conditions leading to the change of characteristics during implementation, identifies misclassifications, and initiates re-training. The algorithm accumulates misclassified data till a pre-defined threshold level is reached or a human expert terminates inspection. The misclassified results merge with the original datasets for progressive re-training using a strategy similar to the base model development. The present work utilizes pre-trained CNN Efficientnet-b0 to develop the surface defect detection model for tapered roller inspection through transfer learning. It is concluded that the progressive re-training improves defect detection performance and reduces misclassifications. The Matthews Correlation Coefficient (MCC) score, derived from the confusion matrix, showed improvement from 0.6 to 0.82 after four iterations. A cross-model benchmarking study is also performed to show the versatility of the proposed approach. The present work demonstrated that the human-guided progressive learning approach can provide adaptability to vision-based surface defect detection utilizing deep learning algorithms and enhance system performance during real-time implementation.

RFA-YOLOv8: Steel Plate Surface Defect Detection Algorithm Based On Improved YOLOv8

Abstract To address the issues in existing steel plate surface defect detection algorithms, such as low accuracy, numerous model parameters, and high computational complexity, this paper introduces a novel algorithm named RFA-YOLOv8, which is built on an enhanced version of YOLOv8n. The approach enhances the C2f module within the backbone network and incorporates a receptive field attention (RFA) mechanism to refine the convolution layer, leading to the development of the C2f_RFAConv structure. This advancement significantly boosts feature extraction capabilities in complex scenarios. Experimental results demonstrate that RFA-YOLOv8 achieves a 74.9% accuracy on the NEU-DET dataset, surpassing YOLOv8n by 1.8%, with minimal changes in model parameters and floating-point operations. Moreover, improvements in both recall and precision rates confirm that RFA-YOLOv8 delivers high detection accuracy and efficiency, making it valuable for industrial quality inspections.

FDADNet: Detection of Surface Defects in Wood-Based Panels Based on Frequency Domain Transformation and Adaptive Dynamic Downsampling

The detection of surface defects on wood-based panels plays a crucial role in product quality control. However, due to the complex background and low contrast of defects in wood-based panel images, features extracted by traditional deep learning methods based on spatial domain processing often contain noise and blurred boundaries, which severely affects detection performance. To address these issues, we have proposed a wood-based panel surface defect detection method based on frequency domain transformation and adaptive dynamic downsampling (FDADNet). Specifically, we designed a Multi-axis Frequency Domain Weighted Information Representation Module (MFDW), which effectively decoupled the indistinguishable low-contrast defects from the background in the transform domain. Gaussian filtering was then employed to eliminate noise and blur between the defects and the background. Additionally, to tackle the issue of scale differences in defects that led to difficulties in accurate capture, we designed an Adaptive Dynamic Convolution (ADConv) module for downsampling. This method flexibly compressed and enhanced features, effectively improving the differentiation of the features of objects of varying scales in the transform space, and ultimately achieved effective defect detection. To compensate for the lack of data, we constructed a dataset of wood-based panel surface defects, WBP-DET. The experimental results showed that the proposed FDADNet effectively improved the detection performance of wood-based panel surface defects in complex scenarios, achieving a solid balance between efficiency and accuracy.

Entropy Stacked Autoencoder based Diffusion Model (ESADM) and Fuzzy Clustering with Semi Supervised Fuzzy Graph Convolutional Network (FC-SSFGCN) for Rail Surface Defect Detection

Rails, fasteners, and other parts of railway track lines eventually develop flaws due to continuous strain from train operations and direct exposure to the environment; these faults directly affect the safety of train operations. Detecting defects on rail surfaces presents a formidable challenge due to the wide array of possible flaws and their unpredictable nature. However, Defect identification errors, massive variances, inadequate training sample availability, and weak contrast between faults and the surrounding background all contribute to the complexity of this procedure. In this work, rail surface and fastener flaws may be detected non-destructively using a multi-crack detection approach based on the Entropy Stacked Autoencoder Diffusion Model (ESADM). A novel approach, ESAFM, has been developed, combining a rail surface image encoder with a multi-layer, Stacked Autoencoder to extract latent materials from images showcasing different types of cracks. This integration reduces the need for significant processing resources by integrating easily into the conventional physically-based image workflow. Additionally, the Zero Shot-Semi Supervised Fuzzy Class Knowledge Graph (ZS-SSFCKG) method proposes Class Knowledge Graph Construction (CKGC), which constructs a CKG to elucidate the connection between defects and non-defects. Class features are learned by using a Fuzzy Clustering with Semi Supervised Fuzzy Graph Convolutional Network (FC-SSFGCN). The method employs a transformer encoder to capture distant relationships, allowing for the extraction of features from defect samples. This facilitates the acquisition of distinct defect image characteristics, as industrial defects vary in shape and size. The experimental results on the public Railway Track Fault Detection (RTFD)and Rail Surface Defect Datasets (RSDDs) with rail surface defects are collected from rail tracks surface defect detection.

A Surface Defect Detection Method for Cold-rolled Strip Steel Based on Improved YOLOv8

Cold-rolled strip steel has excellent mechanical properties, and it is widely used in the fields such as automotive manufacturing, construction industry, aircraft manufacturing and electronic product manufacturing. However, many different processing defects occur in the manufacturing process of cold-rolled strip steel, and this article proposes a visual detection method based on YOLOv8 to identify the surface defects on cold-rolled strip steel more accurately. Firstly, several common types of defects in cold-rolled strip steel were analyzed. Then, in order to enrich the dataset for subsequent detection, wavelet transform was used for filtering, denoising, and canny edge detection in the image processing stage for obtaining the clearer input model image with the more prominent graphics. Lastly, the CA attention mechanism was added into the YOLOv8 which is useful to improve the features recognition ability of the model, at the same time, the original standard convolution was replaced by DCS convolution and the loss function was optimized which is useful to get the lightweight models. The improved YOLOv8 model was validated on the NEU-DET dataset, the overall detection performance of the model such as the recall, the accuracy, and the average precision has significantly improved.

Expanding the Notch Region by Adjusting the Copper Growth Profile for High-Efficiency Flexible Cu(In,Ga)Se2 Solar Cells.

Flexible CIGS solar cells, with their adjustable band gap for future flexible tandem solar cells and flexibility for roll-to-roll manufacturing, have the potential to be used in a wide range of applications. However, flexible CIGS solar cells are always manufactured at relatively low temperatures, where Cu diffusion has a substantial impact on the CIGS surface state and defect formation. To address these issues, we designed a new CIGS growth profile in this work by carefully examining the effects of different locations of excess Cu in the third stage of the CIGS deposition profile. The results showed that adding more Cu to the middle part of the third stage can enhance the crystal quality, expand the GGI grading notch region, move the GGI minimum to the CdS side, cause a JSC rise, achieve proper band alignment at the interface, and then enhance the FF. By taking advantage of these advantages, the photoelectric conversion efficiency (PCE) is significantly raised. An impressive improvement of 32.6% over the initial efficiency of 13.8% has been attained, yielding a high efficiency of 18.3% and providing a strong basis for the development of flexible CIGS solar cells.

Surface Lattice Engineering Enables Efficient Inverted Perovskite Solar Cells

AbstractState‐of‐the‐art inverted perovskite solar cells (PSCs) have exhibited considerable promise for commercialization due to their prospective stability. However, the intricate crystallization of halide perovskite, especially for multi‐component perovskites, not only distorts the surface lattice from its ideal form but also introduces numerous unsaturated dangling bonds to form surface defects, which can easily lead to reduced stability and poor performance. Herein, a surface lattice engineering is developed by coupling surface unsaturated ions and regulating ion bonding lengths/angles to achieve efficient and stable inverted PSCs. The renovated surface lattice not only eliminates shallow/deep level defects on the surface of perovskite, but also enhances photo/thermal stability of the materials. Moreover, the surface lattice engineering contributes to uniform potential surface, and improves energy‐level alignment at the interfaces of the perovskite and C60 carrier transport layer, enhancing charge carrier extraction and transportation. Finally, the champion PSC delivers an impressive efficiency of 25.82% (certified 25.5%). Moreover, these PSCs exhibit excellent operational stability, retaining 94% initial efficiency after more than ≈1 000h maximum power point test.

Improvement of X-ray response of CZT films by post-annealing

CdZnTe (CZT) epitaxial films are promising for X-ray flat panel detectors due to their ability for large area fabrication, high absorption efficiency, and excellent detective quantum efficiency (DQE). However, challenges like image lag, ghosting, and reduced DQE due to long response times and high dark currents persist. This study introduces a novel post-in-situ annealing technique to enhance CZT film sensitivity and transient response. Results show that a brief 20 s annealing treatment increased surface Zn content, raising detector resistivity from 3.5 × 109 Ω·cm to 3.3 × 1010 Ω·cm while reducing leakage current at high voltages. This treatment also significantly reduced surface defects, improving CZT film detectors’ X-ray response time. Moreover, the electrons mobility-lifetime product (μτ)e improved from 2.30 × 10−5 cm2/V to 2.17 × 10−4 cm2/V, indicating enhanced charge carrier dynamics crucial for high-speed X-ray detection. Notably, the X-ray response sensitivity achieved a commendable 126 μC G−1· cm−2 a bias of 30 V. In conclusion, in-situ annealing represents a remarkable step forward in the development of CZT technology for X-ray flat panel imaging, addressing critical issues and potentially broadening its applications in medical and industrial imaging systems.

Novel intelligent defects detection of boiler water walls in thermal power plants based on OFM_SSD

The boiler is a critical component of conventional thermal power systems, where surface flaws in boiler water walls can significantly compromise safety and availability, potentially leading to substantial loss of life and property. Traditional detection methods, whether manual or based on machine learning, often prove costly, inefficient and time-consuming, failing to meet the stringent requirements for water wall inspection. Therefore, a novel surface defect detection model integrating an improved single shot multibox detector (SSD) with the optical flow method (OFM) (OFM_SSD) is proposed. The OFM enhances data sample diversity by augmenting the dataset derived from thermal power plants, while the incorporation of deconvolution techniques improves the model receptive field, thereby enhancing its ability to detect and classify small defects. Comprehensive experiments demonstrate that the OFM_SSD outperforms several existing models including the SSD model based on traditional expanded datasets (T_SSD), you only look once (YOLO), ordinary SSD, Regions with the CNN(R_CNN), and Deconvolution-only SSD (DSSD) in terms of accuracy in defect localization and classification. This advancement of the OFM_SSD not only reduces operational costs but also enhances detection capabilities, ultimately contributing to safer and more efficient operations within thermal power plants.

Single Atom‐Engineered 2D Catalytic Nanosheets for Sonocatalytic Suppression of Orthotopic Clear Cell Renal Cell Carcinoma

AbstractClear cell renal cell carcinoma (ccRCC), a prevalent malignant tumor, exhibits resistance to chemotherapy, necessitating advanced therapeutic approaches. Sonocatalytic therapy has emerged as a promising non‐invasive treatment for solid tumors due to its ability to penetrate deep tissue. Herein, the development of single atomic Au‐doped 2D TiO2 nanosheets are presented with oxygen vacancies (Au‐SA/Def‐TiO2, ASDT) for sonocatalytic treatment of ccRCC. Surface defects on TiO2 nanosheets effectively stabilize Au single atomic sites via the Ti‐Au‐Ti structure, enhancing catalytic properties and facilitating reactive oxygen species (ROS) generation by reducing energy barriers and improving electron (e−) and hole (h+) separation. Additionally, ASDT exhibits peroxidase‐like activities, further augmenting ROS production, leading to significant inhibition of tumor growth in subcutaneous and orthotopic ccRCC models under ultrasound irradiation. This study underscores the efficacy of defect engineering for stabilizing single atoms to boost ROS production and achieve effective tumor eradication by the enhanced sonocatalytic effect.

Understanding intra-pore electrolyte resistances and distributed capacitances in carbon electrodes used in supercapacitors using a robust modeling process to separate the charge-storage occurring into macro-, meso-, and micro-pore structures

In this study, a Modified Generic Electrochemical (MGE) model is introduced for studying rough/porous carbon-based electrode materials employed in electrical double-layer capacitors (EDLC) which contain different types of surface defects. Instead of adopting the common theoretical approach involving the use of transmission line models or ad hoc equivalent circuits, a rational equivalent circuit model has been devised, which permitted us to derive the different equations for each electrochemical technique. The MGE representing the overall electrochemical behavior of EDLCs obtained using the different techniques (e.g., cyclic voltammetry, chronoamperometry, chronopotentiometry, and impedance) is formally derived based on a simple premise considering that the different inhomogeneous surface defects, which are geometrically arranged under equipotential conditions, are not electrochemically equivalent. In this sense, the presence of hierarchically distributed charge-storage processes with different time constants, accounting for the ionic accumulation at the electrode/solution interface, can be ascertained. We verified that is imperative to avoid the determination of the specific capacitances and resistances in EDLCs using a single electrochemical technique or several techniques whose model equations are based on different theoretical premises. The use of the MGE model unequivocally revealed that isolated measurements of the specific capacitance in the absence of the associated intra-pore resistances are not sufficient to ensure the presence of rapid charge storage allied with high-power characteristics. With the appropriate choice of carbon-based materials with very low intra-pore resistances, substantial progress can be made in ushering in the next generation of EDCLs with very high-power characteristics.

STRUCTURE FORMATION OF Ni−P ALLOY ELECTROCOATINGS WITH INCREASED CORROSION RESISTANCE

Purpose of research. The main factors that reduce the protective properties of electrocrystallized coatings are the formation of volumetric defects − pores, as well as the occurrence of microgalvanic couples at grain boundaries, that is, on surface defects of the crystalline structure. Creating a non-porous structure in electrocoatings, eliminating the formation of surface defects in metal/alloy layers, as well as the possibility of replacing chromium coatings, which are obtained from toxic electrolytes, are urgent tasks. Materials and methodology. In the work, it was proposed to use electrocoatings with Ni−P alloy as protective layers on products that increase corrosion resistance. An X-ray determination of the phase composition of Ni−P electrochemical deposits was carried out. The surface morphology and corrosion resistance of coatings were studied. Results. Samples of electrochemical coatings of pure nickel and nickel-phosphorus alloy were studied. It has been established that during the formation of nickel coatings with the addition of phosphorus ions, the surface morphology has practically no pores, amorphization of deposits occurs, due to which the protective properties, namely, corrosion resistance, are improved. Scientific novelty. Factors have been identified that influence the increase in corrosion resistance of coatings − the absence of a porous structure and the formation of an amorphous state. Conclusions. The influence on the structure and properties of electrochemical coatings of a nickel-phosphorus alloy has been studied. It has been established that the addition of phosphorus ions to the nickel plating electrolyte leads to the formation of an amorphous structure in the coatings, and layers of the Ni−P alloy appear practically without the presence of pores. It has been established that the effect of increasing the corrosion resistance of the electrochemical coating with a nickel-phosphorus alloy (by 30 %) is due to the formation of an amorphous structure of the solidifying metal liquid while preventing the crystallization process during electrochemical deposition.