Corrosion Behaviour and Machine-Learning-Based Prediction of CNT and Micro- Titanium Reinforced Copper Composites

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Ten compositions (C0–C9) were fabricated by varying CNT content (0.5–1.5 wt.%) and titanium content (1–5 wt.%). Immersion corrosion tests were conducted in 0.5 N and 1 N acidic media for 24–96 hours. Results showed a clear reduction in corrosion rate with increasing reinforcement levels, with the unreinforced copper sample (C0) exhibiting the highest corrosion, while samples C8 and C9 showed the lowest values (≈ 0.001 mm/y), indicating a significant improvement in corrosion resistance. To enable predictive modelling, four regression approaches, Linear Regression, Polynomial Regression (Poly2), Support Vector Regression (SVR), and Random Forest (RF), were trained using the experimental dataset. Polynomial regression consistently provided the highest accuracy (R² >0.95 in most cases), while SVR showed poor predictive capability with negative R² values for several samples. Residual analysis and Q–Q plots confirmed that polynomial regression exhibited the most stable and normally distributed error behaviour. SEM surface morphology supported the corrosion results, revealing severe pitting and degradation in C0, whereas C8 and C9 showed smooth, minimally damaged surfaces, confirming strong corrosion protection. The study demonstrates that CNT–titanium hybrid reinforcement significantly enhances corrosion resistance in copper MMCs, and that polynomial regression offers a reliable machine-learning tool for forecasting corrosion trends. The combined experimental–computational approach provides a framework for accelerated design and screening of corrosion-resistant metal composites. Copper metal matrix composites Carbon nanotubes (CNT) Micro-titanium reinforcement Corrosion behaviour Polynomial regression Machine-learning prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Copper and its alloys are widely employed in electrical, thermal, marine, and industrial systems owing to their excellent conductivity, formability, and antimicrobial properties[ 1 ]. However, their application in aggressive service environments is often limited by their susceptibility to corrosion, particularly in chloride- and acid-rich media, where localised attack, pitting, and surface degradation lead to rapid loss of functionality and structural integrity [ 2 ], [ 3 ]. To overcome this limitation, copper-based metal matrix composites (MMCs) reinforced with ceramic and carbon-based particles have gained increasing attention, as they offer the potential to improve not only the mechanical and thermal performance of copper but also its electrochemical stability [ 4 ], [ 5 ], [ 6 ]. Among the emerging reinforcement systems, carbon nanotubes (CNTs) and titanium-based particles have shown promise due to their high aspect ratio, chemical inertness[ 7 ], [ 8 ], [ 9 ], and ability to act as diffusion barriers and passive-film promoters for corrosion[ 10 ], [ 11 ], [ 12 ]. Although several studies have reported the use of CNTs or ceramic fillers in copper composites, most investigations focus primarily on mechanical strengthening, wear resistance[ 13 ], [ 14 ], [ 15 ], [ 16 ], or electrical behaviour, while a systematic understanding of corrosion response, especially under multi-variable reinforcement combinations[ 17 ], [ 18 ], [ 19 ], remains limited. Moreover, the majority of corrosion studies rely solely on experimental immersion testing, without integrating predictive modelling techniques that could enable faster materials screening and performance forecasting[ 20 ], [ 21 ], [ 22 ], [23]. With the rapid growth of data-driven approaches in materials science, machine-learning-assisted corrosion prediction offers a powerful strategy to complement laboratory characterisation, yet its application to copper-based hybrid composites is still at an early stage. The present work addresses this gap by fabricating ten copper composite formulations (C0–C9) containing controlled combinations of CNTs (0.5–1.5 wt.%) and micro-titanium particles (1–5 wt.%), and systematically evaluating their corrosion behaviour in 0.5 N and 1 N acidic media for immersion intervals up to 96 hours. In addition to experimental weight-loss measurements, four regression models, Linear, Polynomial (Poly2), Support Vector Regression (SVR), and Random Forest (RF), were trained to predict corrosion rate as a function of composition and exposure time, allowing comparative assessment of model accuracy through R² scores, residual distribution, and Q–Q analysis. Scanning electron microscopy (SEM) was further employed to correlate surface morphology with corrosion severity and reinforcement effects. This study thus provides a dual-perspective contribution: (1) materials-based, by establishing the role of CNT and micro-titanium synergy in suppressing corrosion of copper MMCs, and (2) computational, by demonstrating the effectiveness of polynomial regression in reliably forecasting corrosion evolution compared to other machine-learning models. The outcomes are expected to support the design of next-generation copper composites with tailored corrosion resistance for marine, chemical-processing, and electro-mechanical applications, while also illustrating a transferable framework for predictive corrosion modelling in composite systems. 2. Materials and methodology The two powders shown in Fig. 1 correspond to (a) carbon nanotubes (CNTs) and (b) titanium dioxide (TiO₂), which are widely used nanomaterials in advanced engineering, especially in composites, coatings, sensors, and energy devices. Figure 1 (a), the black powder represents CNTs, which are cylindrical nanostructures composed of rolled graphene sheets. CNTs are known for their exceptional mechanical strength, high electrical and thermal conductivity, large aspect ratio, and low density. Even at very small weight fractions, CNTs can significantly improve the tensile strength, toughness, electrical conductivity, and corrosion resistance of a composite matrix. Because of their high surface area and ability to form conductive networks, they are also used in supercapacitors, batteries, biosensors, and EMI shielding materials. In image (b), the white powder corresponds to titanium dioxide (TiO₂), a ceramic oxide material commonly used in photocatalysis, pigments, corrosion-resistant coatings, self-cleaning surfaces, and biomedical implants. TiO₂ exhibits excellent chemical stability, high hardness, strong UV shielding capability, and good biocompatibility. In composite systems, it improves wear resistance, thermal stability, and surface protection. When combined with CNTs, TiO₂ nanoparticles can form hybrid nanomaterials that offer mechanical reinforcement (CNT) and chemical or photocatalytic functionality (TiO₂). Figure 2 shows a refractory-lined crucible furnace used for the melting of metallic alloys before casting. The central cavity houses the graphite or ceramic crucible, while the surrounding structure is designed to withstand prolonged exposure to elevated temperatures. The surface discolouration and deposits indicate repeated thermal cycling, oxidation of molten metal, and adherence of slag or flux residues during processing. The presence of cracks and surface erosion suggests progressive degradation of the refractory lining caused by thermal shock, chemical attack, and mechanical stress during crucible loading and metal tapping. Such furnaces are typically employed in the preparation of aluminium-based metal matrix composites, where accurate temperature control and contamination-free melting are critical for achieving uniform reinforcement dispersion and desired microstructural characteristics. Table 1 illustrates that ten copper-based compositions were prepared by varying the weight percentages of carbon nanotubes (CNTs) and micro-sized titanium particles as reinforcements. The control specimen (C) consists of 100% pure copper, while compositions C1–C9 incorporate hybrid reinforcements in different proportions. CNT content was increased in three levels (0.5%, 1.0%, and 1.5%), while micro-titanium was varied across 1%, 3%, and 5%. The balance in each case is copper, forming the primary matrix phase. This graded design enables the study of the individual and combined effects of nanocarbon reinforcement and ceramic metallic addition on the composite system's mechanical, thermal, and corrosion behaviour. Table 1 Composition details of the copper-based metal matrix composites (C0–C9) prepared with varying weight percentages of carbon nanotubes (CNTs) and micro-titanium reinforcements, with copper as the primary matrix phase. Compositions Percentage of Carbon nanotubes % Percentage of Micro titanium % Percentage of Copper % C 0 0 100 C1 0.5 1 98.5 C2 0.5 3 96.5 C3 0.5 5 94.5 C4 1 1 98.0 C5 1 3 96.0 C6 1 5 94.0 C7 1.5 1 97.5 C8 1.5 3 95.5 C9 1.5 5 93.5 Figure 3 shows three stages of a corrosion-resistance experiment conducted on cylindrical metal samples. In Fig. 3 (a), ten freshly prepared copper specimens are displayed, each labelled from 0 to 9 to distinguish different test conditions or alloy compositions. The surfaces appear clean and uniformly polished, indicating the initial uncorroded state. In Fig. 3 (b), the same samples are placed individually in beakers containing a corrosive medium, most likely an aqueous solution such as salt water or acid. Each beaker is numbered to match the corresponding specimen, ensuring traceability during the immersion test. This setup represents the exposure phase, where corrosion progression is allowed to occur under controlled conditions. Finally, in Fig. 3 (c), the samples are shown after removal from the solution and subsequent curing or coating, possibly using a cementitious or protective composite layer. The numbering is still visible on the top surface, indicating consistency in tracking. These post-treatment specimens will likely undergo further analyses, such as weight loss measurement, surface morphology study, or strength testing evaluate the effectiveness of the applied material or inhibitor in mitigating corrosion. (a) As–cast and polished specimens C0–C9 before exposure,(b) Samples immersed in corrosive solution for controlled time intervals, and (c) Post-corrosion condition of the specimens after removal, cleaning, and drying, showing visible changes in surface appearance. 3. Results and discussion 3.1 Corrosion Experimental studies MMC’s used in corrosive environments should have good mechanical properties and resistance to chemical degradation in air and acidic environments. It is essential to have a thorough understanding of the corrosion behaviour of the copper composites. The present study is focused on the corrosion of CNT and micro titanium reinforced copper metal matrix composites with NaCl and HCl solution with different normalities 0.5N, in 24, 48, 72, and 96 hours, and the results are tabulated below. Table 2 shows the variation in corrosion rate of copper specimens (C0–C9) when exposed to two different solution normalities (0.5 N and 1 N) over four immersion periods (24, 48, 72, and 96 hours). In both concentrations, the untreated specimen C0 consistently exhibits the highest corrosion rate, confirming it as the baseline or control sample. As time progresses, the corrosion rate gradually decreases for all specimens, indicating a reduction in active corrosion with prolonged exposure, possibly due to surface film formation or reduced metal reactivity once an initial layer of corrosion products forms. A clear trend is observed where samples C1 to C9 show progressively lower corrosion rates, implying the presence of corrosion-resistant additives, coatings, or alloying elements in increasing proportions. Among all, C9 demonstrates the best corrosion resistance with the lowest rate at every time interval, while the performance improves steadily from C0 through C9. Additionally, corrosion rates are higher in 1 N solution compared to 0.5 N, confirming that higher ionic concentration accelerates corrosion. The overall data strongly suggest that both immersion time and inhibitor/composite content influence corrosion behaviour, with higher protection levels significantly reducing material degradation. Table 2 Corrosion rate (mm/year) of copper-based composite samples (C0–C9) measured at different immersion durations (24–96 hours) in 0.5 N and 1 N acidic solutions. Normalit y (N) Ti me Hr s Corrosion rate(mm/y) C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 0.5 24 0.00923 0.00659 0.00527 0.00516 0.00395 0.00211 0.00211 0.00188 O.0011 6 0.00102 48 0.00822 0.0065 0.00527 0.00515 0.00395 0.0211 0.0021 0.00153 0.0011 0.00101 72 0.00711 0.00625 0.00526 0.00511 0.00394 0.00211 0.00204 0.00133 0.00104 0.00101 96 0.00639 0.00639 0.00521 0.00508 0.00393 0.00211 0.00208 0.01111 0.00101 0.001 1 24 0.0105 0.0098 0.00851 0.00621 0.00511 0.00386 0.0031 0.00256 O.00211 0.00111 48 0.00911 0.0082 0.00711 0.00634 0.00522 0.0312 0.00308 0.00278 0.00118 0.0011 72 0.00812 0.00713 0.00623 0.00541 0.00323 0.00253 0.0022 0.00196 0.00123 0.0011 96 0.00716 0.00638 0.00546 0.0051 0.00302 0.00225 0.0021 0.0157 0.00108 0.00103 Figure 4 illustrates the corrosion rate of copper samples (C0–C9) as a function of immersion time in two different acidic environments: 0.5 N and 1 N solutions. In both cases, the corrosion rate steadily decreases with increasing exposure duration (24 to 96 hours), indicating a slowing corrosion reaction over time, likely due to surface passivation or the formation of a protective corrosion product layer. The control sample C0 consistently shows the highest corrosion rate across all time points, confirming the aggressive nature of the medium in the absence of any protective treatment. As the specimen number progresses from C1 to C9, the corrosion rate reduces significantly, demonstrating improved corrosion resistance, which is most pronounced in sample C9. The trend also shows that corrosion is more severe in 1 N solution than in 0.5 N, as represented by the dashed lines (1 N) being consistently above their solid-line counterparts (0.5 N). This confirms that a higher ionic or acidic concentration accelerates metal degradation. Overall, the graphical comparison highlights two key effects: (1) increasing inhibitor or composite content reduces corrosion, and (2) higher normality increases the corrosion rate, but the treated samples still maintain better stability over time than the untreated control. 3.2 Regression analysis Figure 5 presents the comparison of prediction models for corrosion rate under two solution conditions (0.5 N and 1 N) for four selected samples: C0, C1, C2, and C3. Each subplot shows the experimentally measured corrosion data (solid markers) along with several fitted machine-learning/regression models, including Linear Regression, Polynomial Regression (2nd order), Support Vector Regression (SVR), and Random Forest (RF). In every plot, the best-performing model for a given condition is highlighted with a bold dashed line. In Fig. 5 (a) for sample C0, the polynomial regression model (black dashed line) provides the closest fit to the experimental trend, capturing the steady decrease in corrosion rate over time more accurately than the other models. For C1 in Fig. 5 (b), the polynomial model remains the most reliable for the 1 N condition, while the linear model offers an acceptable approximation in the 0.5 N case. In Fig. 5 (c) for C2, the same trend continues, with the polynomial model again showing superior fitting, especially at longer immersion times where other models deviate. For C3 in Fig. 5 (d), however, the Random Forest model becomes the best performer in the 1 N solution, handling the slight non-linearity and fluctuations better than linear or polynomial methods. The second set of model-comparison plots shows the predicted corrosion behaviour for samples C4, C5, C6, and C7 under both 0.5 N and 1 N solutions as shown in Fig. 6 , highlighting the single best-fitting regression or machine-learning model for each case. For C4 (plot e), the corrosion trend in 1 N solution displays a noticeable non-linear fluctuation, and the polynomial (Ply2) regression provides the closest match to the experimental curve, whereas the 0.5 N data remain almost constant and are best captured by a simple linear model. In C5 (Fig. 6 ), the corrosion rate steadily decreases with time in both concentrations, and the polynomial model again offers the best representation, especially for 1 N, where the decline is more pronounced. For C6 (Fig. 6 ), the linear decay pattern is very strong, particularly in the milder 0.5 N medium, and the linear model matches the experimental values better than higher-order or non-parametric models. Finally, in C7 (plot h), the corrosion curve exhibits a mild rise-and-fall pattern in 1 N solution, and the Random Forest model gives the most accurate prediction by adapting to this irregular trend, whereas the more stable 0.5 N data are once again well described using polynomial regression. C8 and C9, under both 0.5 N and 1 N solutions. Figure 7 (i) for C8, the corrosion rate in the 1 N medium shows a smooth, continuously decreasing trend as immersion time increases, and the polynomial regression model (Ply2 – highlighted with the bold dashed curve) provides the closest fit to the real data, accurately capturing the non-linear decay pattern. In contrast, the corrosion rate in the 0.5 N solution remains almost constant and very low throughout the test duration, which is why even a simple linear model is sufficient for that dataset. Figure 7 (j) for C9, which is the best-performing specimen in the entire series, the corrosion rate in the 1 N environment follows a curved profile, showing a slight rise initially and then a gradual decline. The polynomial model again emerges as the most accurate predictor, outperforming linear and machine-learning methods due to its ability to capture curvature in the trend. Meanwhile, the 0.5 N corrosion rate for C9 remains extremely low, almost flat at ~ 0.001 mm/y, indicating near-complete surface protection. Together, these results show that as inhibition efficiency increases (from C0 to C9), the corrosion curve becomes more stable and less time-dependent, especially in weaker media (0.5 N). They also confirm that polynomial regression is the most reliable model for predicting corrosion behaviour in higher-normality solutions where non-linear decay exists, whereas simpler models are sufficient once corrosion is highly suppressed. 3.3 Comparison of Regression Analysis The R² comparison chart in Fig. 8 provides a quantitative assessment of how well each prediction model, Linear Regression, Polynomial Regression (Poly2), Support Vector Regression (SVR), and Random Forest (RF), fits the corrosion-rate data across all ten samples (C0–C9) and both solution conditions (0.5 N and 1 N). A higher R² value (closer to 1.0) indicates stronger predictive accuracy. Overall, Polynomial Regression consistently achieves the highest R² scores for most samples, confirming its suitability for capturing the non-linear decay pattern in corrosion behaviour. Linear Regression performs reasonably well for samples with simple, monotonic trends (especially C6, C7, and C9), but its accuracy drops for samples showing curvature or fluctuation. Random Forest also performs well in several cases, especially where the data show irregular or non-smooth behaviour (C3, C7), but its performance is less consistent across samples, as reflected by larger error bars. In contrast, SVR shows the weakest performance, even producing negative R² values for samples like C2, C6, and C8, meaning the model performs worse than a constant baseline. This comparison reinforces that no single model is universally optimal, but polynomial regression offers the best balance between stability and accuracy, especially where corrosion-rate decay follows a curved time relationship. The trend also shows that as inhibitor efficiency increases (moving from C0 to C9), the variance in model performance becomes smaller, indicating more stable and easier-to-predict corrosion behaviour in highly protected samples. Table 3 summarises the overall corrosion performance of the ten samples by categorising them from best to worst based on their measured corrosion rates and their stability over time. C8 emerges as the most corrosion-resistant specimen, exhibiting an extremely low and almost constant corrosion rate throughout the test, followed closely by C9, which shows only a slightly higher rate but still falls within the “very low” corrosion category. Samples C7 and C6 form the next group, both demonstrating consistently low corrosion values with only minor reductions over time, confirming effective protective behaviour. The mid-range group consists of C5 and C4, which show moderate corrosion but not severe enough to be classified as high; C4 is slightly worse than C5 due to a more pronounced corrosion trend. Below them, C3 and C2 are categorised as high-corrosion samples, indicating that although some reduction occurs with time, the initial and overall corrosion levels remain significantly elevated. The most affected specimens are C1 and C0, with C0 being the worst performer, showing the highest corrosion rate in both normal conditions and demonstrating how severely the untreated control sample reacts in a 1 N acidic environment. This ranking clearly shows the progression from highly corroding untreated material (C0) to nearly corrosion-proof enhanced samples (C8 and C9), highlighting the effectiveness of the protective modifications introduced across the series. Table 3 Ranking of copper composite samples (C0–C9) based on overall corrosion performance, showing progressive improvement in corrosion resistance with increasing CNT and micro-titanium reinforcement content. Rank Sample Corrosion Level Notes 1 C8 Very Low Lowest corrosion rate, very stable over time 2 C9 Very Low Slightly higher than C8 but still among the best 3 C7 Low Consistent low corrosion, small decline with time 4 C6 Low Slightly higher than C7, still clearly resistant 5 C5 Medium-Low Moderate behaviour, not highly corrosive 6 C4 Medium Higher than C5, noticeable corrosion decline over time 7 C3 Medium Similar pattern as C4 but slightly more corrosive 8 C2 High Starts high, slower reduction, clearly corrosive 9 C1 High Higher corrosion in both concentrations 10 C0 Very High Highest corrosion rate, most affected by 1 N solution 3.4 Residual analysis The side-by-side residual plots compare the prediction errors for sample C8 in two different corrosion environments0.5 N (Fig. 9 .a) and 1 N (Fig. 9 .b), and clearly show how the behaviour of the same material changes with solution strength as well as how each regression model performs under those conditions. In both plots, the Polynomial Regression (Poly2) model displays the most balanced and smallest residuals, staying closest to the zero-error line across all immersion times, which confirms that it is the most reliable model for capturing the time-dependent corrosion trend of C8. The Random Forest (RF) model also performs reasonably well, although it shows minor under-prediction around the mid-exposure range in both cases. The Linear Regression residuals follow a distinct pattern in both solutions, positive at the start, negative in the middle, and positive again at longer times, indicating that a straight-line fit cannot fully describe the slight curvature of the real corrosion trend. The SVR model performs worst in both cases, producing the largest negative residuals (especially in 0.5 N), meaning it consistently predicts corrosion values lower than what was experimentally observed. A key observation is that the magnitude of residuals is much smaller in the 0.5 N plot, which reflects the fact that corrosion is extremely stable and nearly constant in the weaker medium. In contrast, the residuals in 1 N are larger because the corrosion behaviour becomes slightly more time-dependent in the stronger acidic environment. Overall, the comparison reinforces that C8 is a highly stable, low-corrosion specimen, and polynomial regression remains the best predictive model across both concentrations, while SVR is consistently unsuitable due to large and systematic prediction errors. 3.5 Comparison of residual analysis The residual-distribution plot in Fig. 10 compares how the prediction errors from the four models, Linear, Polynomial (Poly2), SVR, and Random Forest (RF)are spread around zero, providing insight into model bias and error consistency. A well-performing model should produce residuals that are narrow, symmetric, and centred close to zero, indicating that it neither overpredicts nor underpredicts systematically. In this plot, the Polynomial Regression (orange) shows the most compact and balanced distribution, with residuals clustered tightly around the zero line, confirming its superior predictive accuracy. The Random Forest (red) model also performs reasonably well, although its distribution is slightly wider, suggesting mild variability across time points. The Linear Regression (blue) model has a wider and more dispersed spread, indicating that its predictions deviate more frequently from the true values and exhibit a slight directional bias. The SVR (green) model performs the worst, with its residuals spread farthest from zero and skewed toward negative values, showing a consistent tendency to underestimate the corrosion rate. The vertical dashed line at zero represents the ideal bias-free condition, and the closer the model curves peak near this line, the more reliable the model. From the overlayed KDE curves, it is clear that Poly2 has the highest peak nearest to zero, followed by RF, confirming earlier model-selection results. Overall, the plot visually reinforces that polynomial regression provides the most accurate and stable predictions, while SVR is the least suitable for this dataset due to large and biased residuals. 3.6 Q-Q plots The Q–Q plots in Fig. 11 compare the normality of residuals for the four prediction models: Linear, Polynomial (Poly2), SVR, and Random Forest (RF), by plotting the ordered residual values against the theoretical quantiles of a normal distribution. A model with normally distributed residuals will produce points that closely follow the red reference line. Among the four plots, the Polynomial (Poly2) and Random Forest (RF) models show the best conformity, with their data points lying closest to the red line, indicating that their residuals follow an approximately normal distribution and therefore satisfy one of the key assumptions of reliable regression modelling. The Linear model shows moderate alignment but with slight deviations at the extremes, suggesting minor systematic error and reduced normality compared to Poly2. In contrast, the SVR model exhibits the largest deviations from the reference line, particularly at the lower quantile end, confirming that its residuals are not normally distributed and that the model contains strong prediction bias. Overall, the Q–Q analysis reinforces earlier findings: Poly2 is the most statistically valid model for this corrosion dataset, followed by RF, while SVR is the least suitable due to large departures from normality and inconsistent residual behaviour. 4. Microstructural analysis Figure 12 provides a comparative view of the surface morphology of copper samples before and after exposure to the corrosive medium, highlighting the effect of corrosion severity and protective treatment. Figure 12 (a) shows before corrosion of 0.5 wt.% cu. Figure 12 (b) displays an even more porous and fragmented structure, suggesting the presence of a thick, non-adhesive corrosion layer, characteristic of severe localised degradation where the metal surface has lost structural continuity of 0.5 wt.% cu. In contrast, Fig. 12 (c) shows a partially protected surface before corrosion of 1 wt.% cu without corrosion. Finally, Fig. 12 (d) represents the sample with the highest corrosion resistance, where the surface appears comparatively smooth and intact, showing only fine streaks and minimal surface deposits 1 wt.% cu with corrosion. The absence of major pits or cracks confirms that the protective mechanism, either through alloying, inhibitor adsorption, or composite coating, has successfully suppressed corrosion progression. 5. Conclusion The investigation demonstrated that the incorporation of hybrid reinforcements, carbon nanotubes and micro-titanium particles, significantly enhances the corrosion resistance of copper metal matrix composites when exposed to acidic environments. The gradual reduction in corrosion rate from C0 to C9 confirmed that both CNT content and titanium percentage play a cumulative role in suppressing material degradation, with samples C8 and C9 exhibiting nearly ten-fold improvement in stability compared to pure copper. The comparative regression study further revealed that corrosion behaviour is predominantly nonlinear with exposure time, and among the four predictive models evaluated, polynomial regression consistently delivered the highest accuracy and most reliable residual behaviour, outperforming linear, SVR, and random forest approaches. The agreement between experimental trends, statistical metrics, and SEM surface observations reinforces the validity of both the material design strategy and the modelling framework. Overall, the work establishes a dual contribution: (1) a scalable pathway for developing corrosion-resistant copper composites using hybrid Micro–Nano reinforcements, and (2) a data-driven methodology that leverages machine learning for corrosion forecasting, reducing dependence on long-duration experimental trials. The combined findings provide a foundation for extending this approach to other alloy systems and service environments, supporting faster materials screening and predictive corrosion engineering in future applications. Declarations Conflict of Interest The authors declare that there is no conflict of interest regarding the publication of this paper. Data Availability Statement The data supporting the findings of this study are available from the corresponding author, Ravitej Y P, upon reasonable request. Graphs, raw measurements, and model code used for regression analysis can be provided to interested researchers for further exploration or validation. Funding statement: There are no funds received for the research on this article. Authors contribution statements: Sripad Kulkarni – Conceptualization, Methodology, Experimental Design, Investigation, Data Curation, Draft Writing (Original Draft) Rajanish M – Supervision, Experimental Resources, Validation, Review & Editing PremChand R – Machine Learning Model Development, Data Analysis, Software, Visualization Mahadeva Prasad – Materials Preparation, Metallurgical Characterization, SEM Analysis Ravitej Y. P. (Corresponding Author) – Project Administration, Research Coordination, Formal Analysis, Writing – Review & Editing, Communication with Journal Ramakumar B V N – Statistical Modelling, Regression Evaluation, Residual & Q-Q Analysis Subramani N – Experimental Support, Immersion Corrosion Testing, Data Logging Srinivasan V R – Validation, Methodology Refinement, Technical Review Balachandra Halemani – Literature Survey, Data Interpretation, Figures & Tables Formatting Jayatirtha M Patil – Machine Learning Validation, Graph Generation, Software Debugging Nityanand B – Proofreading, Reference Checking, Final Manuscript Formatting References Girish S et al (2025) Evaluation of thermal properties of epoxy composites filled by nanofiller materials. 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06:58:00","extension":"html","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110662,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/49456193e023ee284dafe04f.html"},{"id":97141720,"identity":"6c65aa56-73bb-490a-9323-5dd738c5f8e1","added_by":"auto","created_at":"2025-12-01 10:06:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":526314,"visible":true,"origin":"","legend":"\u003cp\u003eReinforcement powders used for composite fabrication: (a) Carbon nanotube (CNT) powder and (b) micro-sized titanium dioxide (TiO₂) powder before mixing with copper matrix.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/e24a5ad31879ec5db3346251.png"},{"id":97114332,"identity":"181dab46-11f6-4b1b-9aab-37fe7b938285","added_by":"auto","created_at":"2025-12-01 06:57:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":371942,"visible":true,"origin":"","legend":"\u003cp\u003eRefractory-lined crucible furnace used for alloy melting during composite fabrication,\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/88c145b6b577ddee271fae57.png"},{"id":97141138,"identity":"41ae1570-ceb6-4366-9c79-9e1c48cd5200","added_by":"auto","created_at":"2025-12-01 10:06:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":752800,"visible":true,"origin":"","legend":"\u003cp\u003eStages of the corrosion experiment on copper-based composite samples:\u003cbr\u003e\n(a) As–cast and polished specimens C0–C9 before exposure,(b) Samples immersed in corrosive solution for controlled time intervals, and (c) Post-corrosion condition of the specimens after removal, cleaning, and drying, showing visible changes in surface appearance.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/f2c9ad8d8f457c99c88f3ecb.png"},{"id":97114336,"identity":"4ba56067-50af-4198-a420-398129c27ad0","added_by":"auto","created_at":"2025-12-01 06:57:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":627436,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of corrosion rate with immersion time for copper composite samples (C0–C9) in 0.5 N (solid lines) and 1 N (dashed lines) acidic solutions.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/dc6246d5f86cdbcec8cc58db.png"},{"id":97114337,"identity":"6f4ceeee-bcdf-4349-9987-4c928f659d26","added_by":"auto","created_at":"2025-12-01 06:57:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":554377,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of regression model predictions for corrosion rate of samples (a) C0, (b) C1, (c) C2, and (d) C3 under 0.5 N and 1 N solutions.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/e1cae510467b39e40a419a6b.png"},{"id":97141476,"identity":"c6bb9cf9-5ffe-452e-a0ce-038a212e1e88","added_by":"auto","created_at":"2025-12-01 10:06:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":664465,"visible":true,"origin":"","legend":"\u003cp\u003eRegression model comparison for corrosion rate prediction of samples (e) C4, (f) C5, (g) C6, and (h) C7 in 0.5 N and 1 N solutions.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/2018f17237b56dc30573e4ad.png"},{"id":97140553,"identity":"c8ede2be-ffe3-4669-a7d9-ad38cf5f84d0","added_by":"auto","created_at":"2025-12-01 10:05:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":354124,"visible":true,"origin":"","legend":"\u003cp\u003eBest-fit regression models for corrosion behaviour of samples (i) C8 and (j) C9 in 0.5 N and 1 N acidic solutions.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/5bea5300c112d23608cc6a8b.png"},{"id":97114328,"identity":"7d6f015e-69f0-4207-8bdb-4d0035a332c1","added_by":"auto","created_at":"2025-12-01 06:57:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1087485,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of coefficient of determination (R²) values for four regression models\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/224a513d5742f2a37dd522b4.png"},{"id":97114342,"identity":"c977f579-f28b-4e60-9f95-a1fe5fddd8c0","added_by":"auto","created_at":"2025-12-01 06:57:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":310657,"visible":true,"origin":"","legend":"\u003cp\u003eResidual plots for sample C8 showing the deviation between experimentally measured and model-predicted corrosion rates in (a) 0.5 N and (b) 1 N acidic media.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/297875a4b35e0110b1023c8a.png"},{"id":97114345,"identity":"e98534e8-3c01-4281-aa29-221d6732d160","added_by":"auto","created_at":"2025-12-01 06:58:00","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":309264,"visible":true,"origin":"","legend":"\u003cp\u003eResidual distribution of the four regression models Linear, Polynomial (Poly2), Support Vector Regression (SVR), and Random Forest (RF)used for corrosion rate prediction.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/a0646c5de7c7411c274ecba4.png"},{"id":97140609,"identity":"abb789d5-0cff-46e0-bdc7-7477977fab6d","added_by":"auto","created_at":"2025-12-01 10:05:24","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":93263,"visible":true,"origin":"","legend":"\u003cp\u003eQ–Q (quantile–quantile) plots showing the normality of residuals for the four regression models used in corrosion prediction: Linear, Polynomial (Poly2), Support Vector Regression (SVR), and Random Forest (RF).\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/6d77330bc5a5313c12170c35.png"},{"id":97114350,"identity":"6ea15960-fd42-44bf-9b07-0e90e473bfbc","added_by":"auto","created_at":"2025-12-01 06:58:00","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1390964,"visible":true,"origin":"","legend":"\u003cp\u003e(a) 0.5 wt.% cu without corrosion, (b) 0.5 wt.% cu with corrosion, (c) 1 wt.% cu without corrosion, (d) 1 wt.% cu with corrosion.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/8469da007bff2c48eee7386c.png"},{"id":102234282,"identity":"ef224ebc-9458-49ae-a7f1-cf1f5eb9ac06","added_by":"auto","created_at":"2026-02-09 16:09:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8150370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8045353/v1/ddf5c5f6-f766-4470-89e7-b9b6b850a94d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Corrosion Behaviour and Machine-Learning-Based Prediction of CNT and Micro- Titanium Reinforced Copper Composites","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCopper and its alloys are widely employed in electrical, thermal, marine, and industrial systems owing to their excellent conductivity, formability, and antimicrobial properties[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, their application in aggressive service environments is often limited by their susceptibility to corrosion, particularly in chloride- and acid-rich media, where localised attack, pitting, and surface degradation lead to rapid loss of functionality and structural integrity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To overcome this limitation, copper-based metal matrix composites (MMCs) reinforced with ceramic and carbon-based particles have gained increasing attention, as they offer the potential to improve not only the mechanical and thermal performance of copper but also its electrochemical stability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among the emerging reinforcement systems, carbon nanotubes (CNTs) and titanium-based particles have shown promise due to their high aspect ratio, chemical inertness[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and ability to act as diffusion barriers and passive-film promoters for corrosion[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although several studies have reported the use of CNTs or ceramic fillers in copper composites, most investigations focus primarily on mechanical strengthening, wear resistance[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], or electrical behaviour, while a systematic understanding of corrosion response, especially under multi-variable reinforcement combinations[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], remains limited. Moreover, the majority of corrosion studies rely solely on experimental immersion testing, without integrating predictive modelling techniques that could enable faster materials screening and performance forecasting[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [23]. With the rapid growth of data-driven approaches in materials science, machine-learning-assisted corrosion prediction offers a powerful strategy to complement laboratory characterisation, yet its application to copper-based hybrid composites is still at an early stage.\u003c/p\u003e\u003cp\u003eThe present work addresses this gap by fabricating ten copper composite formulations (C0\u0026ndash;C9) containing controlled combinations of CNTs (0.5\u0026ndash;1.5 wt.%) and micro-titanium particles (1\u0026ndash;5 wt.%), and systematically evaluating their corrosion behaviour in 0.5 N and 1 N acidic media for immersion intervals up to 96 hours. In addition to experimental weight-loss measurements, four regression models, Linear, Polynomial (Poly2), Support Vector Regression (SVR), and Random Forest (RF), were trained to predict corrosion rate as a function of composition and exposure time, allowing comparative assessment of model accuracy through R\u0026sup2; scores, residual distribution, and Q\u0026ndash;Q analysis. Scanning electron microscopy (SEM) was further employed to correlate surface morphology with corrosion severity and reinforcement effects. This study thus provides a dual-perspective contribution:\u003c/p\u003e\u003cp\u003e(1) materials-based, by establishing the role of CNT and micro-titanium synergy in suppressing corrosion of copper MMCs, and (2) computational, by demonstrating the effectiveness of polynomial regression in reliably forecasting corrosion evolution compared to other machine-learning models. The outcomes are expected to support the design of next-generation copper composites with tailored corrosion resistance for marine, chemical-processing, and electro-mechanical applications, while also illustrating a transferable framework for predictive corrosion modelling in composite systems.\u003c/p\u003e"},{"header":"2. Materials and methodology","content":"\u003cp\u003eThe two powders shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e correspond to (a) carbon nanotubes (CNTs) and (b) titanium dioxide (TiO₂), which are widely used nanomaterials in advanced engineering, especially in composites, coatings, sensors, and energy devices. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a), the black powder represents CNTs, which are cylindrical nanostructures composed of rolled graphene sheets. CNTs are known for their exceptional mechanical strength, high electrical and thermal conductivity, large aspect ratio, and low density. Even at very small weight fractions, CNTs can significantly improve the tensile strength, toughness, electrical conductivity, and corrosion resistance of a composite matrix. Because of their high surface area and ability to form conductive networks, they are also used in supercapacitors, batteries, biosensors, and EMI shielding materials. In image (b), the white powder corresponds to titanium dioxide (TiO₂), a ceramic oxide material commonly used in photocatalysis, pigments, corrosion-resistant coatings, self-cleaning surfaces, and biomedical implants. TiO₂ exhibits excellent chemical stability, high hardness, strong UV shielding capability, and good biocompatibility. In composite systems, it improves wear resistance, thermal stability, and surface protection. When combined with CNTs, TiO₂ nanoparticles can form hybrid nanomaterials that offer mechanical reinforcement (CNT) and chemical or photocatalytic functionality (TiO₂).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a refractory-lined crucible furnace used for the melting of metallic alloys before casting. The central cavity houses the graphite or ceramic crucible, while the surrounding structure is designed to withstand prolonged exposure to elevated temperatures. The surface discolouration and deposits indicate repeated thermal cycling, oxidation of molten metal, and adherence of slag or flux residues during processing. The presence of cracks and surface erosion suggests progressive degradation of the refractory lining caused by thermal shock, chemical attack, and mechanical stress during crucible loading and metal tapping. Such furnaces are typically employed in the preparation of aluminium-based metal matrix composites, where accurate temperature control and contamination-free melting are critical for achieving uniform reinforcement dispersion and desired microstructural characteristics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates that ten copper-based compositions were prepared by varying the weight percentages of carbon nanotubes (CNTs) and micro-sized titanium particles as reinforcements. The control specimen (C) consists of 100% pure copper, while compositions C1\u0026ndash;C9 incorporate hybrid reinforcements in different proportions. CNT content was increased in three levels (0.5%, 1.0%, and 1.5%), while micro-titanium was varied across 1%, 3%, and 5%. The balance in each case is copper, forming the primary matrix phase. This graded design enables the study of the individual and combined effects of nanocarbon reinforcement and ceramic metallic addition on the composite system's mechanical, thermal, and corrosion behaviour.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComposition details of the copper-based metal matrix composites (C0\u0026ndash;C9) prepared with varying weight percentages of carbon nanotubes (CNTs) and micro-titanium reinforcements, with copper as the primary matrix phase.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompositions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of Carbon nanotubes\u003c/p\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of Micro titanium %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage of Copper %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows three stages of a corrosion-resistance experiment conducted on cylindrical metal samples. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (a), ten freshly prepared copper specimens are displayed, each labelled from 0 to 9 to distinguish different test conditions or alloy compositions. The surfaces appear clean and uniformly polished, indicating the initial uncorroded state. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (b), the same samples are placed individually in beakers containing a corrosive medium, most likely an aqueous solution such as salt water or acid. Each beaker is numbered to match the corresponding specimen, ensuring traceability during the immersion test. This setup represents the exposure phase, where corrosion progression is allowed to occur under controlled conditions. Finally, in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (c), the samples are shown after removal from the solution and subsequent curing or coating, possibly using a cementitious or protective composite layer. The numbering is still visible on the top surface, indicating consistency in tracking. These post-treatment specimens will likely undergo further analyses, such as weight loss measurement, surface morphology study, or strength testing evaluate the effectiveness of the applied material or inhibitor in mitigating corrosion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(a) As\u0026ndash;cast and polished specimens C0\u0026ndash;C9 before exposure,(b) Samples immersed in corrosive solution for controlled time intervals, and (c) Post-corrosion condition of the specimens after removal, cleaning, and drying, showing visible changes in surface appearance.\u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Corrosion Experimental studies\u003c/h2\u003e\u003cp\u003eMMC\u0026rsquo;s used in corrosive environments should have good mechanical properties and resistance to chemical degradation in air and acidic environments. It is essential to have a thorough understanding of the corrosion behaviour of the copper composites. The present study is focused on the corrosion of CNT and micro titanium reinforced copper metal matrix composites with NaCl and HCl solution with different normalities 0.5N, in 24, 48, 72, and 96 hours, and the results are tabulated below.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the variation in corrosion rate of copper specimens (C0\u0026ndash;C9) when exposed to two different solution normalities (0.5 N and 1 N) over four immersion periods (24, 48, 72, and 96 hours). In both concentrations, the untreated specimen C0 consistently exhibits the highest corrosion rate, confirming it as the baseline or control sample. As time progresses, the corrosion rate gradually decreases for all specimens, indicating a reduction in active corrosion with prolonged exposure, possibly due to surface film formation or reduced metal reactivity once an initial layer of corrosion products forms. A clear trend is observed where samples C1 to C9 show progressively lower corrosion rates, implying the presence of corrosion-resistant additives, coatings, or alloying elements in increasing proportions. Among all, C9 demonstrates the best corrosion resistance with the lowest rate at every time interval, while the performance improves steadily from C0 through C9. Additionally, corrosion rates are higher in 1 N solution compared to 0.5 N, confirming that higher ionic concentration accelerates corrosion. The overall data strongly suggest that both immersion time and inhibitor/composite content influence corrosion behaviour, with higher protection levels significantly reducing material degradation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrosion rate (mm/year) of copper-based composite samples (C0\u0026ndash;C9) measured at different immersion durations (24\u0026ndash;96 hours) in 0.5 N and 1 N acidic solutions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNormalit y\u003c/p\u003e\u003cp\u003e(N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTi me\u003c/p\u003e\u003cp\u003eHr s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c12\" namest=\"c3\"\u003e\u003cp\u003eCorrosion rate(mm/y)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eC4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eC6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eC7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eC8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eC9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eO.0011 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.01111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eO.00211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the corrosion rate of copper samples (C0\u0026ndash;C9) as a function of immersion time in two different acidic environments: 0.5 N and 1 N solutions. In both cases, the corrosion rate steadily decreases with increasing exposure duration (24 to 96 hours), indicating a slowing corrosion reaction over time, likely due to surface passivation or the formation of a protective corrosion product layer. The control sample C0 consistently shows the highest corrosion rate across all time points, confirming the aggressive nature of the medium in the absence of any protective treatment. As the specimen number progresses from C1 to C9, the corrosion rate reduces significantly, demonstrating improved corrosion resistance, which is most pronounced in sample C9. The trend also shows that corrosion is more severe in 1 N solution than in 0.5 N, as represented by the dashed lines (1 N) being consistently above their solid-line counterparts (0.5 N). This confirms that a higher ionic or acidic concentration accelerates metal degradation. Overall, the graphical comparison highlights two key effects: (1) increasing inhibitor or composite content reduces corrosion, and (2) higher normality increases the corrosion rate, but the treated samples still maintain better stability over time than the untreated control.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Regression analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the comparison of prediction models for corrosion rate under two solution conditions (0.5 N and 1 N) for four selected samples: C0, C1, C2, and C3. Each subplot shows the experimentally measured corrosion data (solid markers) along with several fitted machine-learning/regression models, including Linear Regression, Polynomial Regression (2nd order), Support Vector Regression (SVR), and Random Forest (RF). In every plot, the best-performing model for a given condition is highlighted with a bold dashed line. In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (a) for sample C0, the polynomial regression model (black dashed line) provides the closest fit to the experimental trend, capturing the steady decrease in corrosion rate over time more accurately than the other models. For C1 in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (b), the polynomial model remains the most reliable for the 1 N condition, while the linear model offers an acceptable approximation in the 0.5 N case. In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(c) for C2, the same trend continues, with the polynomial model again showing superior fitting, especially at longer immersion times where other models deviate. For C3 in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (d), however, the Random Forest model becomes the best performer in the 1 N solution, handling the slight non-linearity and fluctuations better than linear or polynomial methods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe second set of model-comparison plots shows the predicted corrosion behaviour for samples C4, C5, C6, and C7 under both 0.5 N and 1 N solutions as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, highlighting the single best-fitting regression or machine-learning model for each case. For C4 (plot e), the corrosion trend in 1 N solution displays a noticeable non-linear fluctuation, and the polynomial (Ply2) regression provides the closest match to the experimental curve, whereas the 0.5 N data remain almost constant and are best captured by a simple linear model. In C5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the corrosion rate steadily decreases with time in both concentrations, and the polynomial model again offers the best representation, especially for 1 N, where the decline is more pronounced. For C6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the linear decay pattern is very strong, particularly in the milder 0.5 N medium, and the linear model matches the experimental values better than higher-order or non-parametric models. Finally, in C7 (plot h), the corrosion curve exhibits a mild rise-and-fall pattern in 1 N solution, and the Random Forest model gives the most accurate prediction by adapting to this irregular trend, whereas the more stable 0.5 N data are once again well described using polynomial regression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eC8 and C9, under both 0.5 N and 1 N solutions. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (i) for C8, the corrosion rate in the 1 N medium shows a smooth, continuously decreasing trend as immersion time increases, and the polynomial regression model (Ply2 \u0026ndash; highlighted with the bold dashed curve) provides the closest fit to the real data, accurately capturing the non-linear decay pattern. In contrast, the corrosion rate in the 0.5 N solution remains almost constant and very low throughout the test duration, which is why even a simple linear model is sufficient for that dataset. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (j) for C9, which is the best-performing specimen in the entire series, the corrosion rate in the 1 N environment follows a curved profile, showing a slight rise initially and then a gradual decline. The polynomial model again emerges as the most accurate predictor, outperforming linear and machine-learning methods due to its ability to capture curvature in the trend. Meanwhile, the 0.5 N corrosion rate for C9 remains extremely low, almost flat at ~\u0026thinsp;0.001 mm/y, indicating near-complete surface protection. Together, these results show that as inhibition efficiency increases (from C0 to C9), the corrosion curve becomes more stable and less time-dependent, especially in weaker media (0.5 N). They also confirm that polynomial regression is the most reliable model for predicting corrosion behaviour in higher-normality solutions where non-linear decay exists, whereas simpler models are sufficient once corrosion is highly suppressed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Comparison of Regression Analysis\u003c/h2\u003e\u003cp\u003eThe R\u0026sup2; comparison chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e provides a quantitative assessment of how well each prediction model, Linear Regression, Polynomial Regression (Poly2), Support Vector Regression (SVR), and Random Forest (RF), fits the corrosion-rate data across all ten samples (C0\u0026ndash;C9) and both solution conditions (0.5 N and 1 N). A higher R\u0026sup2; value (closer to 1.0) indicates stronger predictive accuracy. Overall, Polynomial Regression consistently achieves the highest R\u0026sup2; scores for most samples, confirming its suitability for capturing the non-linear decay pattern in corrosion behaviour. Linear Regression performs reasonably well for samples with simple, monotonic trends (especially C6, C7, and C9), but its accuracy drops for samples showing curvature or fluctuation. Random Forest also performs well in several cases, especially where the data show irregular or non-smooth behaviour (C3, C7), but its performance is less consistent across samples, as reflected by larger error bars. In contrast, SVR shows the weakest performance, even producing negative R\u0026sup2; values for samples like C2, C6, and C8, meaning the model performs worse than a constant baseline. This comparison reinforces that no single model is universally optimal, but polynomial regression offers the best balance between stability and accuracy, especially where corrosion-rate decay follows a curved time relationship. The trend also shows that as inhibitor efficiency increases (moving from C0 to C9), the variance in model performance becomes smaller, indicating more stable and easier-to-predict corrosion behaviour in highly protected samples.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarises the overall corrosion performance of the ten samples by categorising them from best to worst based on their measured corrosion rates and their stability over time. C8 emerges as the most corrosion-resistant specimen, exhibiting an extremely low and almost constant corrosion rate throughout the test, followed closely by C9, which shows only a slightly higher rate but still falls within the \u0026ldquo;very low\u0026rdquo; corrosion category. Samples C7 and C6 form the next group, both demonstrating consistently low corrosion values with only minor reductions over time, confirming effective protective behaviour. The mid-range group consists of C5 and C4, which show moderate corrosion but not severe enough to be classified as high; C4 is slightly worse than C5 due to a more pronounced corrosion trend. Below them, C3 and C2 are categorised as high-corrosion samples, indicating that although some reduction occurs with time, the initial and overall corrosion levels remain significantly elevated. The most affected specimens are C1 and C0, with C0 being the worst performer, showing the highest corrosion rate in both normal conditions and demonstrating how severely the untreated control sample reacts in a 1 N acidic environment. This ranking clearly shows the progression from highly corroding untreated material (C0) to nearly corrosion-proof enhanced samples (C8 and C9), highlighting the effectiveness of the protective modifications introduced across the series.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRanking of copper composite samples (C0\u0026ndash;C9) based on overall corrosion performance, showing progressive improvement in corrosion resistance with increasing CNT and micro-titanium reinforcement content.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCorrosion Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNotes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLowest corrosion rate, very stable over time\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSlightly higher than C8 but still among the best\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConsistent low corrosion, small decline with time\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSlightly higher than C7, still clearly resistant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium-Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate behaviour, not highly corrosive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigher than C5, noticeable corrosion decline over time\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSimilar pattern as C4 but slightly more corrosive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStarts high, slower reduction, clearly corrosive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigher corrosion in both concentrations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHighest corrosion rate, most affected by 1 N solution\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Residual analysis\u003c/h2\u003e\u003cp\u003eThe side-by-side residual plots compare the prediction errors for sample C8 in two different corrosion environments0.5 N (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.a) and 1 N (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.b), and clearly show how the behaviour of the same material changes with solution strength as well as how each regression model performs under those conditions. In both plots, the Polynomial Regression (Poly2) model displays the most balanced and smallest residuals, staying closest to the zero-error line across all immersion times, which confirms that it is the most reliable model for capturing the time-dependent corrosion trend of C8. The Random Forest (RF) model also performs reasonably well, although it shows minor under-prediction around the mid-exposure range in both cases. The Linear Regression residuals follow a distinct pattern in both solutions, positive at the start, negative in the middle, and positive again at longer times, indicating that a straight-line fit cannot fully describe the slight curvature of the real corrosion trend. The SVR model performs worst in both cases, producing the largest negative residuals (especially in 0.5 N), meaning it consistently predicts corrosion values lower than what was experimentally observed.\u003c/p\u003e\u003cp\u003eA key observation is that the magnitude of residuals is much smaller in the 0.5 N plot, which reflects the fact that corrosion is extremely stable and nearly constant in the weaker medium. In contrast, the residuals in 1 N are larger because the corrosion behaviour becomes slightly more time-dependent in the stronger acidic environment. Overall, the comparison reinforces that C8 is a highly stable, low-corrosion specimen, and polynomial regression remains the best predictive model across both concentrations, while SVR is consistently unsuitable due to large and systematic prediction errors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Comparison of residual analysis\u003c/h2\u003e\u003cp\u003eThe residual-distribution plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e compares how the prediction errors from the four models, Linear, Polynomial (Poly2), SVR, and Random Forest (RF)are spread around zero, providing insight into model bias and error consistency. A well-performing model should produce residuals that are narrow, symmetric, and centred close to zero, indicating that it neither overpredicts nor underpredicts systematically. In this plot, the Polynomial Regression (orange) shows the most compact and balanced distribution, with residuals clustered tightly around the zero line, confirming its superior predictive accuracy. The Random Forest (red) model also performs reasonably well, although its distribution is slightly wider, suggesting mild variability across time points. The Linear Regression (blue) model has a wider and more dispersed spread, indicating that its predictions deviate more frequently from the true values and exhibit a slight directional bias. The SVR (green) model performs the worst, with its residuals spread farthest from zero and skewed toward negative values, showing a consistent tendency to underestimate the corrosion rate.\u003c/p\u003e\u003cp\u003eThe vertical dashed line at zero represents the ideal bias-free condition, and the closer the model curves peak near this line, the more reliable the model. From the overlayed KDE curves, it is clear that Poly2 has the highest peak nearest to zero, followed by RF, confirming earlier model-selection results. Overall, the plot visually reinforces that polynomial regression provides the most accurate and stable predictions, while SVR is the least suitable for this dataset due to large and biased residuals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Q-Q plots\u003c/h2\u003e\u003cp\u003eThe Q\u0026ndash;Q plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e compare the normality of residuals for the four prediction models: Linear, Polynomial (Poly2), SVR, and Random Forest (RF), by plotting the ordered residual values against the theoretical quantiles of a normal distribution. A model with normally distributed residuals will produce points that closely follow the red reference line. Among the four plots, the Polynomial (Poly2) and Random Forest (RF) models show the best conformity, with their data points lying closest to the red line, indicating that their residuals follow an approximately normal distribution and therefore satisfy one of the key assumptions of reliable regression modelling. The Linear model shows moderate alignment but with slight deviations at the extremes, suggesting minor systematic error and reduced normality compared to Poly2. In contrast, the SVR model exhibits the largest deviations from the reference line, particularly at the lower quantile end, confirming that its residuals are not normally distributed and that the model contains strong prediction bias. Overall, the Q\u0026ndash;Q analysis reinforces earlier findings: Poly2 is the most statistically valid model for this corrosion dataset, followed by RF, while SVR is the least suitable due to large departures from normality and inconsistent residual behaviour.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Microstructural analysis","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e provides a comparative view of the surface morphology of copper samples before and after exposure to the corrosive medium, highlighting the effect of corrosion severity and protective treatment. Figure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e (a) shows before corrosion of 0.5 wt.% cu. Figure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e (b) displays an even more porous and fragmented structure, suggesting the presence of a thick, non-adhesive corrosion layer, characteristic of severe localised degradation where the metal surface has lost structural continuity of 0.5 wt.% cu. In contrast, Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e (c) shows a partially protected surface before corrosion of 1 wt.% cu without corrosion. Finally, Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e (d) represents the sample with the highest corrosion resistance, where the surface appears comparatively smooth and intact, showing only fine streaks and minimal surface deposits 1 wt.% cu with corrosion. The absence of major pits or cracks confirms that the protective mechanism, either through alloying, inhibitor adsorption, or composite coating, has successfully suppressed corrosion progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe investigation demonstrated that the incorporation of hybrid reinforcements, carbon nanotubes and micro-titanium particles, significantly enhances the corrosion resistance of copper metal matrix composites when exposed to acidic environments. The gradual reduction in corrosion rate from C0 to C9 confirmed that both CNT content and titanium percentage play a cumulative role in suppressing material degradation, with samples C8 and C9 exhibiting nearly ten-fold improvement in stability compared to pure copper. The comparative regression study further revealed that corrosion behaviour is predominantly nonlinear with exposure time, and among the four predictive models evaluated, polynomial regression consistently delivered the highest accuracy and most reliable residual behaviour, outperforming linear, SVR, and random forest approaches. The agreement between experimental trends, statistical metrics, and SEM surface observations reinforces the validity of both the material design strategy and the modelling framework. Overall, the work establishes a dual contribution: (1) a scalable pathway for developing corrosion-resistant copper composites using hybrid Micro\u0026ndash;Nano reinforcements, and (2) a data-driven methodology that leverages machine learning for corrosion forecasting, reducing dependence on long-duration experimental trials. The combined findings provide a foundation for extending this approach to other alloy systems and service environments, supporting faster materials screening and predictive corrosion engineering in future applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author, Ravitej Y P, upon reasonable request. Graphs, raw measurements, and model code used for regression analysis can be provided to interested researchers for further exploration or validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e There are no funds received for the research on this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution statements:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eSripad Kulkarni \u0026ndash; Conceptualization, Methodology, Experimental Design, Investigation, Data Curation, Draft Writing (Original Draft)\u003c/li\u003e\n \u003cli\u003eRajanish M \u0026ndash; Supervision, Experimental Resources, Validation, Review \u0026amp; Editing\u003c/li\u003e\n \u003cli\u003ePremChand R \u0026ndash; Machine Learning Model Development, Data Analysis, Software, Visualization\u003c/li\u003e\n \u003cli\u003eMahadeva Prasad \u0026ndash; Materials Preparation, Metallurgical Characterization, SEM Analysis\u003c/li\u003e\n \u003cli\u003eRavitej Y. P. (Corresponding Author) \u0026ndash; Project Administration, Research Coordination, Formal Analysis, Writing \u0026ndash; Review \u0026amp; Editing, Communication with Journal\u003c/li\u003e\n \u003cli\u003eRamakumar B V N \u0026ndash; Statistical Modelling, Regression Evaluation, Residual \u0026amp; Q-Q Analysis\u003c/li\u003e\n \u003cli\u003eSubramani N \u0026ndash; Experimental Support, Immersion Corrosion Testing, Data Logging\u003c/li\u003e\n \u003cli\u003eSrinivasan V R \u0026ndash; Validation, Methodology Refinement, Technical Review\u003c/li\u003e\n \u003cli\u003eBalachandra Halemani \u0026ndash; Literature Survey, Data Interpretation, Figures \u0026amp; Tables Formatting\u003c/li\u003e\n \u003cli\u003eJayatirtha M Patil \u0026ndash; Machine Learning Validation, Graph Generation, Software Debugging\u003c/li\u003e\n \u003cli\u003eNityanand B \u0026ndash; Proofreading, Reference Checking, Final Manuscript Formatting\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGirish S et al (2025) Evaluation of thermal properties of epoxy composites filled by nanofiller materials. 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Int J Comput Eng Res 25(5):2250\u0026ndash;3005\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaity D, Siddheshwar PG, Saha S (1998) Adv Fluid Mech Turbomach. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-642-72157-1\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-72157-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHalemani B, Gupta RK, Mishra MK, Patil J Abrasive Wear Behavior of Zr-Reinforced LM13 Composites: Experimental and Machine Learning Analysis, pp. 1\u0026ndash;26\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-bio--and-tribo-corrosion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jbtc","sideBox":"Learn more about [Journal of Bio- and Tribo-Corrosion](http://link.springer.com/journal/40735)","snPcode":"40735","submissionUrl":"https://submission.nature.com/new-submission/40735/3","title":"Journal of Bio- and Tribo-Corrosion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Copper metal matrix composites, Carbon nanotubes (CNT), Micro-titanium reinforcement, Corrosion behaviour, Polynomial regression, Machine-learning prediction","lastPublishedDoi":"10.21203/rs.3.rs-8045353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8045353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis Paper examines the corrosion behaviour of copper-based metal matrix composites reinforced with hybrid combinations of carbon nanotubes (CNTs) and micro-titanium particles, and evaluates the use of machine-learning models for corrosion prediction. Ten compositions (C0\u0026ndash;C9) were fabricated by varying CNT content (0.5\u0026ndash;1.5 wt.%) and titanium content (1\u0026ndash;5 wt.%). Immersion corrosion tests were conducted in 0.5 N and 1 N acidic media for 24\u0026ndash;96 hours. Results showed a clear reduction in corrosion rate with increasing reinforcement levels, with the unreinforced copper sample (C0) exhibiting the highest corrosion, while samples C8 and C9 showed the lowest values (\u0026asymp;\u0026thinsp;0.001 mm/y), indicating a significant improvement in corrosion resistance. To enable predictive modelling, four regression approaches, Linear Regression, Polynomial Regression (Poly2), Support Vector Regression (SVR), and Random Forest (RF), were trained using the experimental dataset. Polynomial regression consistently provided the highest accuracy (R\u0026sup2; \u0026gt;0.95 in most cases), while SVR showed poor predictive capability with negative R\u0026sup2; values for several samples. Residual analysis and Q\u0026ndash;Q plots confirmed that polynomial regression exhibited the most stable and normally distributed error behaviour. SEM surface morphology supported the corrosion results, revealing severe pitting and degradation in C0, whereas C8 and C9 showed smooth, minimally damaged surfaces, confirming strong corrosion protection. The study demonstrates that CNT\u0026ndash;titanium hybrid reinforcement significantly enhances corrosion resistance in copper MMCs, and that polynomial regression offers a reliable machine-learning tool for forecasting corrosion trends. The combined experimental\u0026ndash;computational approach provides a framework for accelerated design and screening of corrosion-resistant metal composites.\u003c/p\u003e","manuscriptTitle":"Corrosion Behaviour and Machine-Learning-Based Prediction of CNT and Micro- Titanium Reinforced Copper Composites","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 06:57:54","doi":"10.21203/rs.3.rs-8045353/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-19T10:47:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T17:44:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T03:46:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T10:46:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T14:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42229721073334896350218164308062598174","date":"2025-11-28T09:40:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T16:27:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271498061905108117747225400550882467071","date":"2025-11-23T22:37:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261094269946120799515389798683060819379","date":"2025-11-22T15:25:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296101172685337240582185296791105819179","date":"2025-11-22T12:31:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128635335999925376600331576607299706690","date":"2025-11-22T12:01:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-21T21:30:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-21T09:55:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50314411634669056557823718368669723473","date":"2025-11-20T16:46:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315586945140202432293106317881157677579","date":"2025-11-20T13:00:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-20T11:50:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-07T00:42:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-07T00:41:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Bio- and Tribo-Corrosion","date":"2025-11-06T08:02:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-bio--and-tribo-corrosion","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jbtc","sideBox":"Learn more about [Journal of Bio- and Tribo-Corrosion](http://link.springer.com/journal/40735)","snPcode":"40735","submissionUrl":"https://submission.nature.com/new-submission/40735/3","title":"Journal of Bio- and Tribo-Corrosion","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2f69f164-f172-4c99-92b8-c7164c5b5e7d","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:05:02+00:00","versionOfRecord":{"articleIdentity":"rs-8045353","link":"https://doi.org/10.1007/s40735-026-01118-9","journal":{"identity":"journal-of-bio--and-tribo-corrosion","isVorOnly":false,"title":"Journal of Bio- and Tribo-Corrosion"},"publishedOn":"2026-02-04 15:59:19","publishedOnDateReadable":"February 4th, 2026"},"versionCreatedAt":"2025-12-01 06:57:54","video":"","vorDoi":"10.1007/s40735-026-01118-9","vorDoiUrl":"https://doi.org/10.1007/s40735-026-01118-9","workflowStages":[]},"version":"v1","identity":"rs-8045353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8045353","identity":"rs-8045353","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}