Metal corrosion poses a significant challenge for industries by decreasing the lifespan of materials and escalating maintenance and replacement costs. This study is critically important, as it assesses the corrosion resistance properties of annealed steel wire electrodes coated with manganese, employing chronoamperometry and linear voltammetry techniques. The electrodes were immersed in an electrolyte solution and subjected to chronoamperometry at various voltages (−0.55 V, −0.60 V, and −0.70 V) and durations (60 s and 1800 s). Subsequently, linear voltammetry was performed over a potential range from −0.8 V to 0.8 V to generate Tafel plots. The Butler–Volmer equation was applied to the data obtained to determine the corrosion current density. The results indicate that the optimal conditions for forming a highly effective protective manganese layer occur at a potential of −0.70 V for 1800 s. Under these conditions, the electrodes exhibited superior corrosion resistance. This study also revealed that shorter durations and less negative potentials led to less-effective manganese coatings, with higher corrosion rates and reduced stability. These findings are significant for developing efficient corrosion protection methods in industrial and research applications, providing clear parameters for optimizing the manganese electrodeposition process on annealed steel.

Background: Electrochemical impedance spectroscopy (EIS) is indispensable for disentangling charge-transfer, capacitive, and diffusive phenomena, yet reproducible parameter estimation and objective model selection remain unsettled. Methods: We derive closed-form impedances and analytical Jacobians for seven equivalent-circuit models (Randles, constant-phase element (CPE), and Warburg impedance (ZW) variants), enforce physical bounds, and fit synthetic spectra with 2.5% and 5.0% Gaussian noise using hybrid optimization (Differential Evolution (DE) → Levenberg–Marquardt (LM)). Uncertainty is quantified via non-parametric bootstrap; parsimony is assessed with root-mean-square error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC); physical consistency is checked by Kramers–Kronig (KK) diagnostics. Results: Solution resistance ($R_s$) and charge-transfer resistance ($R_{ct}$) are consistently identifiable across noise levels. CPE parameters $(Q,n)$ and diffusion amplitude $\left(\sigma \right)$ exhibit expected collinearity unless the frequency window excites both processes. Randles suffices for ideal interfaces; Randles+CPE lowers AIC when non-ideality and/or higher noise dominate; adding Warburg reproduces the ${45}^{\circ }$ tail and improves likelihood when diffusion is present. The $(R_{ct}+Z_W)\Vert \mathrm{CPE}$ architecture offers the best trade-off when heterogeneity and diffusion coexist. Conclusions: The framework unifies analytical derivations, hybrid optimization, and rigorous statistics to deliver traceable, reproducible EIS analysis and clear applicability domains, reducing subjective model choice. All code, data, and settings are released to enable exact reproduction.

Using a semicontinuous seeded emulsion polymerization process with a gradual variation in feed composition, butyl acrylate (BA)/styrene copolymers were obtained (variable composition copolymers, VCCs). BA seed particles of two different diameters were used to prepare copolymer materials with 50–85 wt.% styrene. In each reaction, cumulative copolymer composition was followed by ¹H-NMR; with such data, a histogram showing the relative content of copolymer chains corresponding to each composition interval was built. The VCCs presented higher moduli and ultimate mechanostatical properties than their equivalent core-shell type polymers. The mechanical performance of VCC materials was explained considering the respective histograms.

This study conducts a comprehensive investigation into the corrosion resistance of annealed steel wire, commonly used in marine construction, emphasizing its importance for the material's durability and functionality in such applications. An innovative approach was implemented by applying a Nickel (II) Chloride Hexahydrate coating through electrodeposition. This process was meticulously adjusted, varying critical parameters such as exposure time and electrical potential. To mimic marine corrosive conditions, a Sodium Chloride solution was used, allowing for a detailed comparative analysis between coated and uncoated samples. The evaluation was based on rigorous quantitative measurements of the wire's surface state and corrosion potential, using TAFEL curves for precise interpretation. The findings empirically demonstrated that the coating significantly enhances corrosion resistance, markedly outperforming untreated steel. These results reinforce the proposition of nickel electrodeposition as an effective and viable strategy for corrosion protection in marine industrial applications.

Background: Electrochemical impedance spectroscopy (EIS) is indispensable for disentangling charge-transfer, capacitive, and diffusive phenomena, yet reproducible parameter estimation and objective model selection remain unsettled. Methods: We derive closed-form impedances and analytical Jacobians for seven equivalent-circuit models (Randles, CPE, Warburg variants), enforce physical bounds, and fit synthetic spectra with 2.5% and 5.0% Gaussian noise using hybrid optimization (Differential Evolution → Levenberg– Marquardt). Uncertainty is quantified via non-parametric bootstrap; parsimony is assessed with RMSE, AIC, and BIC; physical consistency is checked by Kramers–Kronig diagnostics. Results: R_s and R_ct are consistently identifiable across noise levels. CPE parameters (Q, n) and diffusion amplitude (σ) exhibit expected collinearity unless the frequency window excites both processes. Randles suffices for ideal interfaces; Randles+CPE lowers AIC when non-ideality and/or higher noise dominate; adding Warburg reproduces the 45◦ tail and improves likelihood when diffusion is present. The (R_ct + Z_W ) ∥ CPE architecture offers the best trade-off when heterogeneity and diffusion coexist. Conclusions: The framework unifies analytical derivations, hybrid optimization, and rigorous statistics to deliver traceable, reproducible EIS analysis and clear applicability domains, reducing subjective model choice. All code, data, and settings are released to enable exact reproduction.