Yuming Tang

This study investigates the degradation of a zinc-rich epoxy/acrylic polyurethane system iwhen exposed to a simulated marine-industrial atmosphere (0.05% NaCl + 0.35% (NH ₄ ) ₂ SO ₄ ) under three conditions: constant 25 °C, and cyclic temperatures of 35/25 °C and 45/25 °C (with a 12 h interval for each stage). The results show that degradation rate follows the order of 45 °C > 35 °C > 25 °C. Elevated temperatures were found to accelerate electrolyte penetration and saturated water uptake, while concurrently reducing chemical stability through the cleavage of urethane and ester bonds within the topcoat, as well as the hydrolysis of ether bonds in the primer. Moreover, thermal cycling further compromises coating integrity by inducing expansion–contraction stresses, which lead to increased porosity. On the basis of low-frequency impedance extrapolated using the GM(1,1) model, the predicted service life under 35 °C cycling is estimated to be 20–30% of that at 25 °C, and merely ∼10% under 45 °C cycling. These findings underscore the big influence of temperature on the long-term durability of organic protective coatings.

The long-term degradation of organic coatings is governed by coupled effects of material and environmental factors, which poses great difficulties for accurate service performance prediction. Electrochemical impedance spectroscopy (EIS) is a common technique for evaluating coating aging and failure. However, conventional EIS empirical models have poor generalization and limited capacity to describe dynamic degradation processes, while existing data-driven neural networks cannot effectively capture temporal dependencies or support reliable long-term extrapolation. To solve these problems, this study develops a modified time-embedding transformer (TET) model to predict coating degradation and remaining service life using only EIS data available up to the prediction time. The embedded time-embedding module enhances temporal feature extraction, and the self-attention structure captures long-range dependencies in impedance time series. The low-frequency impedance modulus (|Z|_{0.01 Hz}) was adopted as the primary model input, and the model was built and checked separately for nine epoxy coating–environment combinations. The results demonstrate that, when each coating system was modeled and checked separately, the TET model presents high prediction accuracy and stability across the studied systems. Compared with typical neural networks, the proposed method reduces prediction errors and effectively overcomes the limitations in dynamic degradation description and long-term trend extrapolation. This study provides an efficient and accurate method for coating durability evaluation to support the maintenance decision-making of anticorrosive organic coatings.

Organic protective coatings are extensively employed to mitigate metal corrosion, yet accurate quantitative evaluation of their performance degradation during service still poses a considerable challenge. This study aims to develop a multi-view deep learning framework for five-grade quantitative assessment of organic coating protective performance using routine electrochemical and mechanical parameters. This study aims to develop a multi-view deep learning framework for five-grade quantitative assessment of organic coating protective performance using routine electrochemical and mechanical parameters. Based on abundant laboratory-accelerated corrosion test data, independent single-view sub-models were constructed for three complementary descriptors, including the mid-frequency phase angle (θ_{10 Hz}), open-circuit potential (OCP), and adhesion strength (A_s). Prediction outputs from individual sub-models were fused through correlation-weighted voting, where weighting factors were determined by quantitative parameter-degradation correlations across various coating systems. The proposed framework achieves reliable five-level grading (excellent, good, fair, poor, failure) of coating protective performance. This methodology provides an effective data-driven framework for the predictive assessment of organic coating protective performance in practical engineering applications.

The degradation process of a red iron oxide epoxy coating on three kinds of metals under a periodic cycling exposure to 3.5 wt% NaCl solution (45 °C 12 h + 25 °C 12 h) was comparatively studied using electrochemical impedance spectroscopy (EIS), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD) methods. The influence of the metal substrates (carbon steel, brass, and Al alloy) on the protection performance of the coating was analyzed using variations in the electrochemical and chemical parameters. The failure criteria of the coating were discussed. The results show that the coating on the three substrates presents different failure times, with the coating on steel presenting the shortest time and the coating on Al alloy the longest time. The characteristics of metal substrates and their corrosion products influence the coating failure behavior. The corrosion products with loose and hygroscopic properties of steel and brass have promoting effects on the diffusion of water through the coating. The passive film of the Al alloy substrate and the formation of salt film containing Cl⁻ have corrosion-inhibiting effects on the substrate. Evaluation of the coating performance by |Z|_0.01Hz should consider the characteristics of the metal substrates.

Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based methods adopt a fixed backbone to sequential tasks by learning task-specific prompts. However, existing prompt-based methods heavily rely on strong pretraining (typically trained on ImageNet-21k), and we find that their models could be trapped if the potential gap between the pretraining task and unknown future tasks is large. In this work, we develop a learnable Adaptive Prompt Generator (APG). The key is to unify the prompt retrieval and prompt learning processes into a learnable prompt generator. Hence, the whole prompting process can be optimized to reduce the negative effects of the gap between tasks effectively. To make our APG avoid learning ineffective knowledge, we maintain a knowledge pool to regularize APG with the feature distribution of each class. Extensive experiments show that our method significantly outperforms advanced methods in exemplar-free incremental learning without (strong) pretraining. Besides, under strong pretraining, our method also has comparable performance to existing prompt-based models, showing that our method can still benefit from pretraining. Codes can be found at https://github.com/TOM-tym/APG

The electrochemical impedance spectroscopy and weight gain tests were performed on carbon fiber/vinyl ester and carbon fiber/bismaleimide composites in 3.5% NaCl solution to study the electrochemical and water absorption behaviors. The microstructure morphology and the flexural property of the composites in the long-term exposure process were analyzed with the scanning electron microscope and four-point bending tests. The results revealed that after long-time immersion (>200 d), the water absorption of the two composites is less than 0.5%. This has little effect on the microstructural integrity, only with slight damage on the fiber/resin interfaces, but results in a significant decrease (about 84%) in the composite flexural property. The variation of the water absorption percentage shows good consistency with that of the resin capacitance (Q_c) and is negatively related to the variation of the resin resistance (R_po) and the low-frequency impedance (|Z|_0.01Hz) of the composites. A good linear relationship exists between the variations of phase angles in the middle-frequency range (0.1−10 Hz) and the |Z|_0.01Hz. The phase angle at 10 Hz (θ_10Hz) may be suggested as a suitable parameter to rapidly evaluate the performance of carbon fiber-reinforced polymer composites, just like for evaluating the protective performance of polymer-coated metals in the literature.

The degradation process and the electrochemical behavior of two anti-corrosion and anti-fouling coating systems (FW-1 and FW-2) in a simulated diurnal cycling immersion environment (3.5% NaCl, 35 °C 12 h + 25 °C 12 h) were investigated by electrochemical impedance spectroscopy (EIS) technology. Combined with the coating gloss, color difference, adhesion strength and scanning electron microscopy (SEM) tests, the micro morphologies and the variations of the performance parameters were comparatively analyzed. The results showed that in the 160 days of immersion, with the hydrolysis of the FW-1 topcoat resin and some pigments dissolved and released, the surface micro-morphology of the coating changes from rough to smooth, thereby increasing the gloss. While, for the FW-2 topcoat, the occurrence of micro pores and tiny cracks results in an increase in the roughness and a decrease in the gloss. The release of the copper ion particles in the antifouling topcoat has an influence on the color, manifesting as obvious rise in the color difference of the coating. The low-frequency impedance (|Z|_{0.01 Hz}) values of both coating samples decreases slowly, presenting a very good shielding to the carbon steel substrate. The self-polishing of the topcoat has no big effect on the electrochemical performance of the whole anti-corrosion and anti-fouling coating system; the protective performance of the coating system mainly depends on the integrity of the primer and the intermediate paint.

The degradation processes of two self-polishing antifouling coatings containing copper-based agents (CuSCN and Cu₂O) in 3.5% NaCl solution and the protection effect of the coating systems were studied by electrochemical impedance spectroscopy (EIS), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM/EDS) methods. The results demonstrate that after immersion for 1525 d at room temperature, the two coating systems still have very good protection property for the 5083 Al alloy substrate, manifesting by the high value of the low-frequency impedance. Alternate high and low temperature immersion test (45 °C 12 h + 25 °C 12 h) leads to serious damage to the antifouling topcoat, and the failure is mainly manifested by many micro-pores and micro-cracks. Because the CuSCN antifouling agent particle has bigger diameter and slightly higher solubility than that of Cu₂O agent, the micro-pores established after the agents dissolved and released during the hydrolysis process of the antifouling coating are relatively larger, which results in more decrease in the impedance and a worse protective property of the coating system for the substrate.

The inhibition effect and mechanism of cerium tartrate (CeTar) as a pigment in epoxy coating on AA2024-T3 aluminum alloy in 3.5% NaCl solution were studied. Two kinds of coatings were applied on the substrate, including a single-layer epoxy coating with CeTar distributed uniformly and a double-layer coating composed of an inner layer doped with CeTar and an outer layer with no CeTar. The protective performances of the coatings were assessed by a Machu test and an Electrochemical impedance spectroscopy (EIS) technique. The corrosion inhibition mechanism of CeTar in the coating was analyzed with X-ray photoelectron spectroscopy (XPS), inductively coupled plasma mass spectrometry (ICP-MS) and Fourier transform spectroscopy (FTIR). The results show that the addition of CeTar can evidently improve the protective performance of the epoxy coating for a long time (>520 d). This might have relationship with the modification effect on the epoxy coating by cerium salts, and also may be due to the synergistic inhibitory effect by tartrate group and cerium ions on the alloy substrate after their continuous releasing to the coating/alloy interface and forming of a protective film. The double-layer coating provides similar protective properties to the single-layer coating. This suggested that creating a protective film on the aluminum alloy substrate could result in a greater contribution to improving the protection performance of the coating.