Investigation on the corrosion resistance of epoxy resin coatings modified by high-entropy oxides

Non-destructive

Investigation Testingon the corrosion resistance of Insulatingepoxy Materialsresin Defectscoatings Basedmodified onby Terahertzhigh-entropy Time-domainoxides

Spectroscopy System

Xiaoyu Luoa, Boxin Yanb,Yan¹, Chao Wangb,*Wang^1,2, MingYihui NieaLiu¹

^a1Hubei ElectricKey Power Research InstituteLaboratory of GuangdongAdvanced PowerTechnology Gridfor Co.,Ltd.Automotive GuangzhouComponents 510080,& China

Hubei

bCollaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, WuhanWuhan, Hubei, 430070, Hubei, China

Abstract²Corresponding -author’s Insulatinge-mail: materialswchao@whut.edu.cn

are

Abstract: widelyHigh-entropy used in fields suchoxides, as poweran grids,emerging electricalclass engineering,of ceramic materials, exhibit exceptional high-temperature stability, superior corrosion resistance, and automotiveexcellent manufacturing.hardness However,and defectsstrength, suchrendering them promising candidates for surface protection applications. In this study, high-entropy oxide filler Y₂(Ti_0.2Zr_0.2Hf_0.2Ce_0.2V_0.2)₂O₇ was synthesized via solid-state reaction and incorporated as bubblesa nanofiller to modify epoxy resin, thereby fabricating composite coatings. The influence of varying high-entropy oxide contents on the anticorrosion performance of the composite coatings was systematically investigated, and inclusionsthe oftenunderlying arisecorrosion duringprotection theirmechanism processing and service, which not only affect performance but may also pose potential safety hazards. Terahertz technology, with its low radiation and high penetration characteristics, has become a new method forof the non-destructivehigh-entropy testing of insulating materials. However, its imaging is limited by hardware and optical diffraction constraints, leading to issues such as low resolution, high noise, and low contrast. This study focuses onoxide-modified epoxy resincoatings insulatingwas materialselucidated. and employs a reflective terahertz time-domain spectroscopy system to perform point-by-point sampling, obtaining waveforms and images for detecting internal defects such as bubbles and inclusions, and analyzing the differences in defect characteristics. To address the issue of inadequate image quality, a preprocessing method combining non-local means filtering and contrast-limited adaptive histogram equalization is proposed to suppress noise and enhance local contrast. Additionally, the SRCNN network is applied for super-resolution reconstruction to improve image details and resolution. ExperimentalThe results demonstrate that the proposedcomposite methodcoatings effectivelyincorporating alleviateshigh-entropy issuesoxide suchexhibit outstanding anticorrosion properties, with a corrosion inhibition efficiency of 99.39% derived from polarization curve analysis. Even after immersion in 3.5wt% NaCl solution for 10 days, the corrosion inhibition efficiency remained at 95.57%. Impedance efficiency, as lowdetermined resolutionfrom Nyquist plots, reached 98.63%, and lowretained contrast91.75% after 10 days of immersion.

1. Introduction

Metallic materials play a crucial role in terahertzindustrial images,development. significantlyTheir improvingcorrosion defectnot detectiononly accuracyaffects the national economy and reliability.

personal

Keywords:safety, insulatingbut material,also terahertz,poses non-destructivesignificant testing,environmental imageimpacts. enhancement,Among super-resolutionexisting reconstruction

strategies,

Introduction

applying

Insulationprotective materialscoatings serveon asmetal surfaces is currently one of the foundation for the development of electrical products and aremost widely used inand numerouseffective fields,corrosion-prevention approaches [1]. However, traditional organic coatings often fail to meet the demands of modern industry, and the incorporation of functional anticorrosive fillers has proven to be an effective method to enhance coating performance [2].

One-dimensional nanomaterials exhibit size-dependent effects [3] and barrier properties, which can effectively improve the performance of epoxy coatings by mitigating microcracks and voids formed during the curing process [4]. High-entropy oxides represent a novel class of ceramic materials [5], composed of multiple metallic elements that form a highly disordered structure. This unique configuration imparts exceptional properties, including powersuperior grids,thermal electricalstability, engineering,outstanding automobilecorrosion manufacturing,resistance, and aerospace.excellent Forhardness instance,and mechanical strength. In this study, one-dimensional high-entropy oxide nanofillers were synthesized and incorporated into epoxy resin to develop composite coatings. The objective is commonlyto employedfill asmicro-voids a supporting insulation component in high-voltage equipment such as GIL/GIS and cable terminals[1], which can reduce installation time by 80% and maintain partial discharge levels below 5pC. However, internal defects such as bubbles, cracks, impurities, and aging inevitably arisegenerated during the productioncoating fabrication process, thereby achieving a composite coating with enhanced hardness and servicecorrosion life of insulation material components. These defects not only compromise the material’s normal operation but may also lead to functional failures, severe accidents,resistance, and significantsubsequently economicinvestigating losses.its Inanticorrosion practicalperformance.

engineering,

2. there is a demand to detect potential internal defects in insulation materials without damaging power grid equipment, thereby assessing the extent of degradation. Non-destructive testing of insulation materials not only ensures equipment safetyMethods and extends service life but also improves operational efficiency and reduces costs. For example, the cost of timely repair for partial discharge in cable joints is merely 10%-20% of the expense required for post-failure replacement.

The common non-destructive testing methods currently used in the power grid field include infrared inspection, ultrasonic testing, and X-ray detection. Infrared inspection can only detect surface or near-surface defects and is highly sensitive to environmental temperature, limiting its effectiveness. Ultrasonic testing faces difficulties when detecting curved components and has poor penetration ability for high attenuation materials. X-ray inspection generates ionizing radiation, posing certain safety risks. Terahertz waves refer to electromagnetic waves with frequencies between 0.1 THz and 10 THz, corresponding to wavelengths ranging from approximately 0.03 to 3 mm. Terahertz waves have good penetration capabilities, easily passing through most non-polar materials and dielectric materials. They are also highly safe, as the energy of terahertz photons is very low compared to X-rays and does not cause damage to the material being tested due to ionizing radiation. Furthermore, terahertz waves have unique fingerprint spectral characteristics, which can be used for the exclusive identification and analysis of materials. Therefore, terahertz non-destructive testing technology is suitable for internal non-destructive inspection of insulating materials.

Terahertz imaging is achieved through point-by-point scanning of a sample. By extracting the time-domain and frequency-domain characteristics of the terahertz reflected waves from the sample, pixel values are assigned and converted into grayscale image data to generate the terahertz image. However, due to limitations such as terahertz wavelength, signal attenuation, noise interference, and hardware constraints of imaging systems, current terahertz images still suffer from issues like low spatial resolution, poor contrast, and blurred contours. To address these challenges, two primary solutions exist: first, enhancing hardware precision to improve terahertz image resolution, and second, using a super-resolution reconstruction [2] algorithm post-processing to refine acquired terahertz images for higher resolution. Notably, the second approach proves more cost-effective. To enhance imaging quality, improve defect recognition accuracy, and precisely characterize internal defects in insulation materials, this paper proposes a preprocessing method combining non-local mean filtering with contrast-limited histogram equalization, alongside an improved SRCNN (Super-Resolution Convolutional Neural Network) for terahertz image super-resolution reconstruction. The effectiveness of this methodology is validated through experimental testing on epoxy resin insulation material defect samples.

2. Basic principlesMaterials

2.1 PrinciplePreparation of TerahertzHigh-Entropy TimeOxide Domain Spectroscopy SystemY₂(Ti_0.2Zr_0.2Hf_0.2Ce_0.2V_0.2)₂O₇

The workinghigh-entropy principleoxide (HEO) was synthesized using the solid-state reaction method. The main preparation steps are as follows: oxide powders of the reflectivecorresponding terahertzelements time-domainwere spectroscopyweighed system[3]according isto shownthe designed chemical composition of the high-entropy oxide with a molar ratio of Y:T:Zr:Hf:Ce:V = 5:1:1:1:1:1. The powders were then mixed using a planetary ball mill for 12 hours. After milling, the resulting slurry was transferred into centrifuge tubes, centrifuged, and the lower precipitate was collected and dried in Figurea 1.vacuum Aoven femtosecondfor laser8 generateshours. laserThe pulses,dried whichmixture arewas splitsubsequently intoground twoand beams after passingsieved through a half-wave200-mesh platesieve. The sieved powder was placed in an alumina crucible, compacted with a spatula, and sintered in a polarizer.muffle Thefurnace reflectedat beam1000 serves°C asfor 4 hours. Finally, the pumpsintered light,product whilewas the refracted beam is the probe light. The pump light passes through another half-wave plateground and polarizer, then is delayed optically before being focused onto a photoconductive antenna to generate terahertz pulses. After being reflected by a set of parabolic mirrors, the terahertz pulses are directed towards the sample under test. The probe light is focused onto the opposite end of the detection antenna after passingpassed through a mirror200-mesh andsieve ato lens. The probe light generates photo-induced carriers inobtain the focalhigh-entropy regionoxide of the detector, which are used to detect the instantaneous electric field amplitude of the terahertz pulse, thereby enabling terahertz detection and obtaining the terahertz time-domain waveform.

Figure 1 Schematic diagram of the reflective terahertz time-domain spectroscopyfiller (THz-TDS) system

Figure 2 illustrates a terahertz time-domain spectroscopy scanning imaging system. The epoxy resin sample is placed on a two-dimensional scanning translation stage, with the mainframe and PC turned on, and testing begins after the mainframe has warmed up. The height of the translation stage is adjusted to maximize the peak-to-peak difference of the time-domain signal[4], in order to prevent diffraction effects from adversely affecting the terahertz image. The stepper motor driver, controlled by the PC, drives the stepper motor to move the stage, and a reflected terahertz signal is obtained for each pixel movement. The information from the obtained terahertz signal is used to assign values to the pixels, and these values are then converted into grayscale to produce the sample’s original terahertz image[5]HEO). When defects are present along the optical path of the terahertz wave, the received terahertz wave undergoes distortion due to reflection at the defect interface. In the time domain, this manifests as changes in the pulse peak size, flight time, and signal envelope area, while in the frequency domain, it primarily manifests as variations in the amplitude of frequency components and changes in spectral energy.

Figure 2 Reflective terahertz time-domain spectroscopy (THz-TDS) scanning imaging system

2.2 PrinciplePreparation of TerahertzComposite Image Super-Resolution ReconstructionCoatings

The2g terahertzof imageepoxy super-resolutionresin reconstructionwas model useddissolved in this study consists8mL of twoacetone, parts:followed by the firstaddition partof is40mg of high-entropy oxide filler and ultrasonic dispersion for 15min. The slurry was then heated and stirred at 50°C to remove the preprocessingacetone. module,Subsequently, 0.6g of curing agent T-31 was added, and the secondmixture partwas isstirred at a constant speed for 15min. The coating was uniformly applied onto the SRCNNpretreated network.6061 aluminum alloy surface using a wire-wound rod coater with a wet film thickness of 200μm. The firstprepared partsamples aimswere tocured performat denoisingroom temperature for 2 days, followed by heating at 60°C for 4h, yielding an epoxy resin composite coating with a high-entropy oxide incorporation of 2wt%, designated as HEO-2.

Following the same procedure, composite coating samples with filler mass fractions of 1wt% and image3wt% enhancementwere onprepared theand originaldesignated terahertzas image,HEO-1 whileand theHEO-3, secondrespectively. partAdditionally, aimsa topure improveepoxy theresin spatialcoating resolutionwithout any filler was fabricated and designated as EPa² + b² = c² .

$$\left( x + a \right)^{n} = \sum_{k = 0}^{n}{\left( \frac{n}{k} \right)x^{n - k}a^{k}}$$

The morphological features of the terahertzEP, image.HEO-1, The training network is shown in the figure. The first part mainly implements denoisingHEO-2, and contrastHEO-3 enhancementsamples throughwere non-localobserved meanusing filteringscanning electron microscopy (SEM).

The electrochemical impedance spectroscopy (EIS) and contrast-limitedpolarization histogram equalization, while the second part uses the SRCNN network, which consists of feature extraction layers, nonlinear mapping layers, and high-resolution reconstruction layers to achieve resolution enhancement.

Due to the physical characteristics of terahertz waves and the point-by-point scanning imaging method, the resulting terahertz images have pixels that are relatively isolated from each other. This leads to issues such as high noise, low contrast, and blurry edges, requiring preprocessing. This paper primarily employs the non-local mean filtering algorithm to remove background noise and uses contrast-limited histogram equalization to enhance the contrastcurves of the terahertzcoatings image.

were

Tomeasured addressusing an electrochemical workstation. EIS measurements were conducted at open-circuit potential over a frequency range from 100,000Hz to 0.01Hz, with an AC amplitude of 10mV. Polarization curves were recorded at a scan rate of 5mV/s. The corrosion inhibition efficiency [6] and impedance efficiency [7] of the problemcoated ofsamples isolatedwere pixelscalculated and unclear textures inusing the originalfollowing terahertz images, the non-local mean filtering method[6] is applied for denoising, with the algorithm schematic shown in Figure 3. This algorithm requires calculating the similarity between all pixels and the current pixel. In practice, a large search window and a small neighborhood window are first defined, with the neighborhood window sliding within the search window. The weights are determined based on the similarity between the neighborhoods. The formula for non-local mean filtering is as follows:equations.

		(1)（1）
		（2）

Here,In Equation (1), P represents the weight,corrosion indicatinginhibition efficiency, j_i denotes the similaritycorrosion betweencurrent pixelsdensity of the coated sample (A/cm²), and j₀ is the corrosion current density of the blank sample (i.e., pure epoxy resin) (A/cm²).

In Equation (2), η represents the impedance efficiency, R_i denotes the impedance value of the coated sample (Ω/cm²), and R₀ is the impedance value of the blank sample (Ω/cm²).

3. Results and Discussion

3.1 Microstructural Analysis

The morphologies of the EP, HEO-1, HEO-2, and HEO-3 samples were examined using scanning electron microscopy (SEM), as shown in Figure 1. Numerous large bubbles are present inside the EP sample (Figure 1a). This is attributed to the high viscosity of the pure epoxy system, which traps air during stirring, and the entrapped air is unable to escape easily. The bubble control in the originalHEO-1 image;sample (Figure 1b) is thesimilar searchto windowthat in EP, with a considerable number of pixelbubbles ;observed. This is due to the insufficient filler content, which leads to limited dispersion in the resin and isan inability to effectively suppress bubble formation during mixing and curing. The HEO-2 sample (Figure 1c) exhibits the filteredfewest image.

and

Figuresmallest 3internal Schematicbubbles, illustrationwith the most uniform and complete structure. This improvement is attributed to the optimal filler content, which moderates the viscosity of the non-localcoating, meansfacilitates bubble escape, and suppresses bubble generation. The HEO-3 sample (NLM)Figure filtering1d) principlealso demonstrates good bubble control but remains inferior to HEO-2. This is because the excessive filler addition increases slurry viscosity and reduces fluidity, hindering bubble removal and leading to partial bubble retention.

Figure 1. SEM images of the composite coating²:(a) EP; (b) HEO-1; (c) HEO-2; (d) HEO-3.

3.2 Polarization Curve Analysis

The methodpolarization curves and corresponding data are presented in Figure 2 and Table 1. As shown in Figure 2 and Table 1, compared with the pure epoxy coating, the coatings containing HEO fillers exhibit a positive shift in corrosion potential and a decrease in corrosion current density, indicating improved anticorrosion performance. Among them, the HEO-2 coating shows a more positive corrosion potential and the lowest corrosion current density, demonstrating the highest anticorrosion efficiency. Even after prolonged exposure to the corrosive medium, the HEO-2 sample maintains excellent anticorrosion performance, suggesting that 2wt% HEO filler achieves good dispersion within the coating and effectively inhibits the penetration of corrosive species.

Figure 2. Polarization curves of samples with different coatings without immersion (a) and immersed for determining10 the similarity between two neighborhood windows is to calculate the mean squared errordays (MSE)b).

between

Table them:1. Polarization curve fitting data of different coating samples.

Coating sample	Immersion time/day	j /(A/cm-2)	E/V	P (%)
EP	0	1.730×10-7	-1.019	-
	(2)10	8.013×10-7	-1.021	-
HEO-1	0	1.308×10-8	-0.757	92.43
	10	1.416×10-8	-0.517	91.82
HEO-2	0	1.059×10-9	-0.191	99.39
	10	7.669×10-9	-0.416	95.57
HEO-3	0	1.424×10-9	-0.294	99.17
	10	1.422×10-8	-0.744	91.78

3.3 Electrochemical Impedance Analysis

Here,The electrochemical impedance data are presented in Figure 3 and Table 2. Figure 3 and Table 2 clearly illustrate the performance differences among the coatings. Among the composite coatings, HEO-2 exhibits the highest impedance efficiency, reaching 98.63% initially and remaining at 91.75% after 10 days of immersion. Compared with the other samples, HEO-2 consistently shows the best impedance values and efficiency before and after immersion, indicating that the incorporation of 2wt% HEO provides superior anticorrosion performance, effectively protecting the substrate from corrosion and extending the coating service life. The HEO-1 coating demonstrates an initial impedance efficiency of 91.26%, which decreases significantly to 40.69% after 10 days of immersion. This suggests that 1wt% HEO is insufficient to notably enhance the corrosion protection capability of the coating. Combined with the SEM observations, it can be inferred that the low filler content leads to inadequate dispersion, resulting in limited reinforcement and modification effects. The HEO-3 coating shows an initial impedance efficiency of 96.71% and retains 90.62% after 10 days of immersion, indicating that 3wt% HEO significantly improves anticorrosion performance. However, compared with HEO-2, excessive filler addition leads to diminished efficiency gains and potential material waste, suggesting that 2wt% is the normalizationoptimal factor,loading whichfor isachieving thebalanced sumperformance and economic efficiency.

Figure 3. Nyquist plot of allsamples weights;with isdifferent thecoatings meanwithout squaredimmersion error;(a) isand theimmersed filteringfor coefficient.10 Thedays larger(b).

the

Table value2. Electrochemical impedance data of ,different thecoating better the denoising effect, but the image becomes more blurred. Conversely, the smaller the value of , the worse the denoising effect, but the less distortion after denoising.

The basic idea of histogram equalization is to perform a nonlinear mapping of image pixel values to make their grayscale distribution more uniform. However, traditional histogram equalization globally stretches the grayscale histogram of the entire image, which may excessively enhance noise in low-contrast areas and lead to the loss of details in high-brightness regions. When histogram equalization is applied to terahertz images, which are low signal-to-noise ratio (SNR) images, the noise is significantly amplified, resulting in salt-and-pepper noise or artifacts. Contrast-limited adaptive histogram equalization (CLAHE)[7] is an improved version of histogram equalization that prevents the issue of excessive noise enhancement. CLAHE limits the contrast within each local region after dividing the image into blocks, thereby avoiding the introduction of excessive noise caused by overly high contrast.

CLAHE sets a threshold for the original image’s histogram, and if the grayscale of a certain bin exceeds this threshold, it is clipped. The excess values are then evenly distributed across the available grayscale levels, as shown in Figure 4. Additionally, to avoid blocky discontinuities caused by image segmentation, bilinear interpolation is used to reconstruct the pixel grayscale values.

Figure 4 Schematic illustration of CLAHE principle

As shown in Figure 5, the background of the terahertz image processed by NLM and CLAHE is significantly cleaner, with a substantial reduction in noise. The readability of weak signal areas has been notably improved; low-contrast features such as bubbles, inclusions, and cracks are more pronounced. Additionally, edge and texture information is better preserved, avoiding the blurring issues typically associated with traditional filtering methods.

Figure 5 Preprocessing workflow diagram

The SRCNN network in the second part is primarily used to enhance the preprocessed images. Unlike the traditional SRCNN[8], the SRCNN in this study uses a 7×7×64 convolutional kernel and a Rectified Linear Unit (ReLU, Max(0, x)) in the first layer to extract features and obtain low-resolution features. In the second layer, a 5×5×32 convolutional kernel is employed to nonlinearly map the low-resolution features to 32 sets of high-resolution features. In the third layer, a 5×5×1 convolutional kernel is used to integrate the high-resolution features and generate the final high-resolution image.

In this study, low-resolution terahertz images with c channels and height and width dimensions of h and w, respectively, are used as the input to the SRCNN, as shown in Figure 6. The first convolutional layer (Conv1) is used to extract low-resolution features, with a convolutional kernel size of f1×f1, n1 filters, and a ReLU activation function. To ensure that the image size remains unchanged after convolution, padding should be applied during the convolution process, with the padding size set to padding = f1//2. After Conv1, the output feature map has a size of n1×h×w.samples.

Coating sample	Immersion time/day	R/(Ω)	η(%)
EP	0	8.653×105	-
	(3)10	5.573×105	-
HEO-1	0	9.902×106	91.26
	10	1.459×106	40.69
HEO-2	0	6.324×107	98.63
	10	1.049×107	91.75
HEO-3	0	2.637×107	96.71
	10	9.226×106	90.62

3.4 Hydrophobicity Test

The secondwater layerabsorption data obtained by the gravimetric method are presented in Figure 4. As shown in Figure 4, the water absorption rates of the SRCNNepoxy network,composite Conv2,coatings iscontaining usedHEO tofillers establishare thesignificantly mappinglower relationshipthan between low-resolution and high-resolution features. It maps the n1 low-resolution feature vectors from Conv1 to n2 high-dimensional feature vectors, which represent the featuresthat of the high-resolutionpure image.epoxy The convolutional kernel size of Conv2 is f2×f2, with n2 filters,coating, and the activationincrease functionin water absorption over time is ReLU.also Paddingmuch slower. This improvement is applied with a size of padding = f2//2. After passing through Conv2, the output feature map has a size of n2×h×w.

		(4)

The third layer of the SRCNN network, Conv3, is used to reconstruct the high-resolution image. Conv3 can be understood as a set of linear filters, and the average of the filtered results represents the predicted image, with the output being the final reconstructed image. The convolutional kernel size of Conv3 is f3*f3, with c filters, and padding is applied with a size of padding = f3//2. After passing through Conv3, the output is a high-resolution image with a size of c×h×w.

		(5)

Unlike the traditional SRCNN, in this study, the convolutional kernel size in the first layer is f1×f1 = 7×7, with padding = 3. In the second layer, the convolutional kernel size is f2×f2 = 5×5, with padding = 2. The convolutional kernel size in the third layer is the same as that in the traditional SRCNN, f3×f3 = 5×5. This design is relatedattributed to the particularitiesincorporation of terahertzHEO images.

fillers,

Figurewhich 6:partially SRCNN Network Diagram

3. Material and method

The terahertz time-domain spectroscopy (THz-TDS) imaging system employed in this study, provided by Qingdao Qingyuan Fengda Terahertz Technology Co., Ltd., consists of four primary components: a computer, main unit, imaging module, and a two-dimensional scanning platform. The system operates within a frequency bandwidth of 0.1-4.5 THz, with three selectable pulse scanning speeds (120ps@25Hz, 90ps@30Hz, and 300ps@10Hz). It demonstrates a lateral resolution of 1 mm, thickness detection range of 30 μm-9 mm, thickness measurement accuracy of ±2 μm, and an imaging field of view (FOV) of 100mm×100mm.

Epoxy resin, recognized for its high dielectric strength and corrosion resistance, has found extensive industrial applications as a critical insulating material in power grid systems. This study selected epoxy resin asfill the targetmicro-voids insulating material. Specimens containing artificially induced defects (gas bubbles and embedded inclusions) were prepared through controlled gas injectiongenerated during the curing process of the epoxy matrix, resulting in a denser coating structure that effectively inhibits water penetration and deliberateenhances inclusionhydrophobic performance. Furthermore, the high hardness, excellent corrosion resistance, and outstanding chemical stability of foreignHEO particles.contribute Theto specimensgreater wereresistance fabricatedagainst withintrusion dimensionsby corrosive molecules, thereby maintaining the structural integrity of 50mm×50mm×3mm.the Thecoating experimentaland setupfurther involvedimproving positioningits hydrophobicity.

Figure 4. Water absorption of different coating samples onafter asoaking liftingfor platformdifferent andtime.

acquiring

3.5 terahertzAnticorrosion signalsMechanism through transmission-mode THz-TDS. A two-dimensional scanning platform performed raster scanning with a step size of 0.2mm, yielding 272×272 pixel images.

Analysis

The acquisitionanticorrosion mechanism of terahertzthe imagesepoxy presentscomposite notablecoating challenges,is including prolonged imaging durations (requiring no less than 40 minutes per specimenillustrated in thisFigure study).5. Furthermore,The terahertzcorrosion nondestructiveprotection testingprimarily forarises insulating materials remains in its nascent stages with insufficient data accumulation. These constraints makefrom the establishmentphysical ofbarrier large-scaleeffect terahertz image training datasets impractical. To address this limitation, we utilized the public DIV2K dataset for network training while reserving 10 acquired terahertz images for testing.

Following model architecture development and dataset preparation, network training was implemented using PyTorch 2.4.0 framework under Python 3.7 environment. Training computations were executed on an Intel Xeon 6133 processor coupled with an NVIDIA RTX 4080S GPU. The super-resolution reconstruction network was configured with 4× magnification factor, initial learning rate of 10-4, and batch size of 16. The model underwent 500 training epochs using Adam optimization algorithm for parameter updates.

4. Results and analysis

The following image demonstrates the experimental results of image super-resolution reconstruction. Figure 7(a) shows the original terahertz image, Figure 7(b) displays the image processedprovided by the classicalanticorrosive SRCNNfillers. [7]Pure network,epoxy Figureresin 7(c)has presentshigh viscosity, which facilitates air entrapment during mixing with the imagecuring processedagent, byleading to the SRGANformation [9]of network,micropore. andIn Figureaddition, 7(d)solvent showsevaporation theduring imageepoxy processedcuring byalso thecontributes SRCNNto networkmicropore proposed in this paper.formation. As shown in Figure 7(5(a), when corrosive species penetrate the originalcoating, terahertzthey imagecan reach the metal substrate through these micropore, initiating corrosion reactions upon contact. Therefore, pure epoxy resin exhibits noticeablerelatively artifacts,poor corrosion resistance in electrochemical tests. Figure 5(b) presents the schematic diagram of the corrosion protection mechanism after incorporating HEO fillers. As an anticorrosive filler, HEO possesses high hardness, strong corrosion resistance, and excellent chemical stability owing to its lattice distortion effect, sluggish diffusion effect, and high-entropy effect. The appropriate addition of HEO not only suppresses bubble formation and fills the voids in the epoxy matrix, but thesealso artifactsacts areas a physical barrier, creating a “tortuous path” or “maze effect” that significantly improveddelays the permeation of corrosive molecules toward the substrate.

Figure 5. Schematic diagram of corrosion resistance mechanism of composite coatings (a) pure epoxy resin coating (b) epoxy resin composite coating with HEO.

4. Conclusion

Electrochemical measurements demonstrated that the HEO-2 coating exhibits excellent corrosion resistance. Before immersion, the corrosion protection efficiency reached 99.39%. After immersion in Figures3.5wt% 7(b)NaCl andsolution (d).for In10 Figure 7(c),days, the sharpnesscoating andretained contrasta athigh theprotection imageefficiency edgesof are95.57%. visiblyThe enhanced,impedance butefficiency some distortion and noise are introduced after SRGAN processing. Subjectively, the difference between Figures 7(b) and 7(d) is small, butobtained from the zoomed-inNyquist view,plots was 98.63%, and remained at 91.75% after 10 days of immersion.

Hydrophobicity tests further confirmed that the textureHEO-2 epoxy composite coating possesses outstanding water-repellent properties. The initial water absorption rate was 2.37%, and only slightly increased to 2.72% after 10 days of immersion in Figure3.5wt% 7(d)NaCl issolution, clearerindicating thanthat the HEO-2 composite coating has a denser microstructure, resulting in Figureimproved 7(b). In contrast, Figure 7(b) exhibits some mosaic effects, which negatively impact its overall quality.

Figure 7: Terahertz Images of Epoxy Resin Samples

(a) Original Image(b) SRCNN(c) SRGAN(d) Ours

The aforementioned three algorithms have each demonstrated a degree of enhancement on terahertz images. In addition to subjective evaluation[10], a quantitative comparison of their effectiveness is required. To objectively compare the performance of these algorithms, Peak Signal-to-Noise Ratio (PSNR)hydrophobicity and Structuralenhanced Similaritycorrosion Index (SSIM) were selected as evaluation criteria for assessing enhancement quality. PSNR, one of the most widely used metrics in image processing, represents the ratio between the maximum possible signal power and the power of corrupting noise that affects fidelity. A higher PSNR value indicates superior image quality. SSIM, another prevalent image similarity metric, emphasizes structural information in quality assessment, aligning with human visual perception. Thus, SSIM better approximates human judgment of image quality, where values closer to 1 indicate lower distortion.protection.

As showna inhigh-entropy Figureoxide, 8,Y₂(Ti_0.2Zr_0.2Hf_0.2Ce_0.2V_0.2)₂O₇ exhibits excellent structural stability, high hardness, and superior corrosion resistance. Its incorporation into the proposedepoxy algorithmmatrix achievessignificantly smoother textures and more pronounced edges. In contrast, images processed byenhances the conventional SRCNN algorithm exhibit mosaic artifacts that degrade visual quality. Meanwhile, the SRGAN network, with its more complex architecture, was trained exclusively on open-source optical image datasets, making it more suitable for optical image reconstruction. Comprehensive analysis of Figure 8 and Table 1 leads to the conclusion that the improved SRCNN achieves optimal enhancement performance.

The superioranticorrosion performance of the proposedcomposite algorithmcoating. stemsThe fromcombination twoof architecturalinorganic innovations:fillers First,with organic coatings represents an important research direction for advanced protective coatings, offering broad application prospects and warranting further investigation.

References

1. McMahon Matthew E, Santucci Raymond J. Jr, Glover Carol F, Kannan Balaji, Walsh Zachery R, Scully John R (2019) A Review of Modern Assessment Methods for Metal and Metal-Oxide Based Primers for Substrate Corrosion Protection. Frontiers in Materials.,6:190.

2. Sepideh P, Ebrahim G, Alimorad R, Mohammad R V (2018) Corrosion protection properties of novel epoxy nanocomposite coatings containing silane functionalized graphene quantum dot. Journal of Alloys and Compounds.,731:1112-1118.

3. H Liu, A Tang, W Xu, et al. (2025) Effect of carbon-based filler dimensions on the adoptionanti-corrosion performance for epoxy composite coating. Inorganic Chemistry Communications., 180(1): 114913.

4. Y Li, S Liu, F Feng, et al. (2024) Preparation and Characterization of aGraphene 7×7Oxide/Carbon convolutionalNanotube/Polyaniline kernelComposite inand theConductive initialand layerAnticorrosive reduces overfitting risks while enabling focused extractionProperties of medium-scaleIts features.Waterborne Second,Epoxy theComposite implementationCoatings. Polymers., 16(18): 264.

5. Almishal S.S.I, Furst M, Tan Y, et al. (2025) Thermodynamics-inspired high-entropy oxide synthesis. Nat Commun.16: 8211.

6. J Chen, X Li, et al. (2024) Influence of acorrosion 5×5inhibitors convolutionalon kernelaging in the subsequent layer expands the receptive field[11], facilitating reconstruction of intricate textures. This dual optimization effectively mitigates checkerboard artifacts and improves edge continuity compared to conventional approaches.

   Figure 8: Magnified Terahertz Image Details of Epoxy Resin Samples (a) Original Image(b) SRCNN(c) SRGAN(d) Ours

   Table 1: Comparison of Reconstruction Results for Three Networks

   Project	PSNR/dB	SSIM
   SRCNN	27.62	0.925
   SRGAN	21.32	0.754
   Ours	27.39	0.928

   5. Conclusions

   This paper proposes a novel super-resolution reconstruction network that integrates traditional image processing algorithms with SRCNN to address the challenges of high noise, low contrast, and limited resolution in terahertz images, thereby enhancing the precision and accuracy of non-destructive testing for insulating materials in engineering applications. Terahertz imagesmechanism of epoxy resin samples containing bubble and inclusion defects were employed as experimental inputs. The framework first preprocesses the images using NLM and CLAHE algorithms. The preprocessed images are subsequently fed into the modified SRCNN networkcoatings for super-resolutioncopper reconstruction.62 Experimentalalloy resultsin demonstratesimulated thatmarine theenvironment. proposedCorrosion algorithmReviews., effectively reduces artifacts, enriches image details, and improves spatial resolution compared to conventional approaches.43(4):457-467.

   Acknowledgement

7. ThisGata workJoseph wasA. supported(2023) byMethodology thefor Science and Technology Projectdevelopment of Chinasmart Southernepoxy Powercoatings Gridincorporated Co.,with Ltd.Ethylenediamine-N, N'-disuccinic ac-id (GrantEDDS) No.layered GDKJXM20222546).

   double

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