This study investigates the effectiveness of salicylate (SAL) as an electrolyte additive on the discharge behavior of high-purity (HP) Mg anode in an aqueous half-cell system, using an integrated approach of mathematical modeling and experimental analysis. A finite element-based model is developed to elucidate the key mechanisms by which SAL influences the voltage profile and pH. Systematic electrochemical measurements, especially intermittent discharge tests combined with electrochemical impedance spectroscopy (EIS), demonstrate that SAL can enhance initial voltage stability of HP Mg anode. Moreover, the model incorporates the SAL-Mg complexation factor to describe the role of SAL in modifying the deposit film on HP Mg surface. The agreement between model predictions and experimental observations suggests that SAL facilitates the formation of compact Mg(OH)2 deposits and sustains a favorable pH environment within the half-cell compartment. This integrated approach provides new insights into understanding and optimizing additive effects for Mg-air batteries.

Screening electrolyte additives that effectively inhibit anode self-corrosion while enhancing cell voltage is a critical challenge for advancing sustainable battery technologies. Herein, glutamate, a common food flavoring agent, is proposed as a bio-compatible electrolyte additive for Mg-air batteries. The employing glutamate not only address the challenge, leading to an exceptional energy density of 2.52 kWh & sdot;kg-1 in a half-cell discharge test, but also surpasses most reported additives in terms of environmental friendliness, cost-effectiveness, and broad applicability to various Mg anodes. Insights into the mechanistic understanding of the effect of glutamate on anode self-corrosion processes were provided. Advanced synchrotron microtomography analysis revealed the glutamate-triggered anodic chunk effect, while in-operando scanning localized microprobe techniques were employed to investigate oxygen reduction at the anode surface. Besides, a novel 3D equilibrium predominance diagram was constructed to determine optimal glutamate concentrations, showing strong alignment with experimental findings. In summary, this work presents a robust strategy for designing electrolyte compositions, accurately quantifying Mg anode weight loss attributable to the chunk effect, and providing a potential biocompatible electrolyte additive for implantable Mg batteries.

The effect of metallic impuritiesImpurities on mechanical and corrosive properties of aluminum alloysAluminum alloy are well documented. It is attributed to the microstructural changes in the alloy. Especially precipitationPrecipitation of electrochemically active or brittle intermetallicIntermetallic particles is problematic for the final component’s integrity. In this work, a workflow is proposed to predict the microstructureMicrostructure of AlSi7Mg0.3 cast alloy, depending on the Fe and Cu impurityImpurities content and the corresponding corrosion and mechanical propertiesMechanical properties via a cascade of different simulationSimulation methods.

Atmospheric corrosion of maritime structures remains one of the most challenging issues facing offshore industry. It is well-known that this process significantly reduces the steel strength leading to structural damage with undesirable consequences. Forecasting atmospheric corrosion levels with precision is essential for preventing failures, organizing preventive maintenance schedules, and ensuring the durability of structural operations. This study introduces a new hybrid deep learning (HDL) model called Convolutional Gated Recurrent Unit (CGRU) for forecasting atmospheric corrosion in steel structures subjected to maritime conditions using time-series signals based on real experimental setup. By leveraging both the feature extraction strengths of Convolutional layers, which capture spatial hierarchies from input, and the ability of Gated Recurrent Unit (GRU) layers to learn long-term dependencies, the proposed CGRU model can capture both spatial and temporal features of atmospheric corrosion data within time-series signals, resulting in precise predictions. The performance of the proposed CGRU model is compared with that of other state-of-the-art models such as Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Deep Neural Network (DNN). The applicability of the proposed model is validated using an experimental time-series corrosion dataset gathered from sensors installed on test site in Gangseo-gu/Busan South Korea. The outcomes of the study will contribute to future monitoring and maintenance concepts ensuring sustainability and safety of maritime structures by providing insights into the practical utilization of deep learning for improving corrosion management in the field of maritime engineering.

The ability to assess the risk of corrosion of metallic structures in particular environments holds considerable significance in the field of automotive industry. In recent years, machine learning has evolved into a crucial tool to evaluate the complex and multidimensional corrosion phenomena. In this paper, the special case of non-aqueous alcoholate pitting corrosion of AA1050 in ethanol-blended fuels with water and chloride contamination is examined via supervised machine learning techniques in order to distinguish between safe and unsafe conditions. The data space was created by conducting dedicated experiments with varying ethanol-fuel-water ratios, temperatures, and surface preparations. The classifier's performance rating of 0.87 (balanced accuracy) indicates an outstanding predictive ability and highlights the model's usefulness as decision support for subsequent experiments. A novel microreactor is used to generate data for alcoholate corrosion of AA1050 in ethanol-containing fuels. Based on these data, a supervised machine learning classifier is trained, which correlates temperature, potentially harmful solution compositions (e.g., chloride content, ethanol content), and surface preparations of the AA1050 with corrosion occurrence. By distinguishing between corrosive and non-corrosive conditions, the classifier provides corrosion susceptibility maps and serves as a decision support tool for upcoming experimental design. image

Raw data pertaining to the publication "Exploring the effect of microstructure and surface recombination on hydrogen effusion in Zn-Ni coated martensitic steels by advanced computational modelling".

The significance of incorporating anion species into electrolyte solvation structures, particularly with doubly charged Mg2 + ions, is investigated using the grand canonical density functional theory (GC-DFT) approach. In an extension of previously established methodology, the work explores the thermodynamic stability in acetonitrile (AN) at the interface with Mg3Bi2 and Mg2Sn. Two different anions, TFSI- and ClO4-$\text{ClO}_4<^>-$, are strategically incorporated based on energy comparisons. Despite the known chemical compatibility of alloy anodes with the electrolyte solution, the research reveals a novel form of solvent degradation, which is also reported in the case of pure Mg anode with conventional electrolytes. Notably, the AN molecule adjacent to anion species exhibits reduced susceptibility to reduction (-0.8 - -0.4 V vs Mg2 +/Mg) in the lower potential range in comparison with the solvation structure of full dissociation. Charge density difference and density of states analyses detail solvent molecules becoming electrophilic, with the LUMO overlapping with the Fermi level at lower potentials when the electrostatic interaction with anion species are considered. Experimental studies using nuclear magnetic resonance (NMR) spectroscopy and linear sweep voltammetry (LSV) validate the theoretical results, providing a comprehensive understanding. This methodology, augmenting prior approaches, provides valuable guidance for electrolyte composition based on predominant solvation structures in multivalent solutions. A grand canonical DFT method in combination with interface models is used to investigate the reduction nature of Acetonitrile solvent. The realistic solvation structures are determined in Mg(TFSI)2 and Mg(ClO4)2 electrolyte solutions where the solvent molecules have different reduction stability depending on the presence of the different anion species. image

The voltage drop appearing at Mg anode-electrolyte interface is a critical issue for the battery power and energy density of aqueous primary Mg-air batteries. The respective voltage loss is typically assigned to the deposits layer forming on the anode surface during discharge. In this work, we experimentally and computationally investigate the critical factors affecting the voltage drop at Mg anode towards a deeper understanding of the contribution of deposit and its growth. A two-dimensional (2D) mathematical model is proposed to compute the voltage drop of Mg-0.15Ca wt.% alloy (Mg-0.15Ca) by means of a semi-empirical formulas and experiments-based modification model, considering the effect of discharge current density, the negative difference effect (NDE) and surface deposits layer itself. This model is utilized to simulate the discharge potential of the anode at predefined experimental current densities. The computed voltage drop (half-cell voltage) is in good agreement with the experimental value. The applicability of the mathematical model is successfully validated on the second material (namely high-purity Mg).

Optimizing electrochemical kinetics by regulation ion/charge transfer efficiency and stabilizing the electrode structure of electrode materials is crucial to maximize the rapid charging and long cycling sodium-ion storage. Herein, VS2/Bi2S3 spring-type heterointerfaces hollow microspheres with spatial confinement and sulfur vacancy defects are synthesized as a fast-charging anode for sodium-ion hybrid capacitors (SIHCs). The experimental studies coupled with density functional theory calculations verify that the strong coupling between VS2 and Bi2S3 induces a stable built-in electric field, largely promoting the charge and sodium-ion transfer efficiency. Sulfur vacancy defects at the heterointerfaces produce additional sodium-ion pseudocapacitive storage, which improves the reversible capacity and large-rate fast charge performance of the VS2/Bi2S3 electrode. Finite element analysis and in situ expansion test confirm that the spring-type heterostructured hollow microspheres formed by flat-morphology VS2 and zigzag-morphology Bi2S3 stacking mitigate the lattice expansion and contraction during sodium-ion insertion/extraction, accommodate the mechanical stresses, and maintain the integrity of the heterojunction interface. When employed in coin SIHC, it achieves a high energy/power density of 135 Wh kg-1/22 kW kg-1, and an ultralong life of 50 000 cycles; the assembled pouch SIHC (1 Ah) demonstrates a high specific energy of 120 Wh kg-1 with fast-charging at 10 C, and 95.5% capacity retention after 1000 cycles.

This study explores the effects of nitro (–NO2) and amino (–NH2) functional groups on the intercalation of isophthalic acid (IPA) into ZnAl layered double hydroxide (LDH) through experimental and theoretical approaches. ZnAl LDHs intercalated with IPA, 5-aminoisophthalic acid (AIPA) and 5-nitroisophthalic acid (NIPA) were synthesized via the co-precipitation method. Experimental results revealed different ion-exchange behavior reflected on loading and release properties. UV–Vis analysis determined the loading capacity sequence as NIPA ​≥ ​AIPA ​> ​IPA, while release kinetics in NaCl solution followed AIPA ​> ​IPA ​> ​NIPA. Density functional theory (DFT) simulations highlighted deprotonated carboxylic groups on the benzene rings as primary binding sites, and the affinity of species to the interlayers was estimated as NIPA2− ​> ​AIPA2− ​> ​IPA2−. The interlayer arrangements are proposed considering the interlayer distances, interaction sites, surface area per charge, density deviations and patterns of intercalates in its natural crystal structures. The results reveal that the functional groups significantly influence the interlayer structure of species in the LDH. The addition of –NO2 group stabilizes the species via interaction with hydroxide layers, while the –NH2 renders AIPA less stable in the interlayer due to its electron accepting nature which containing two positively charged hydrogen atoms.

Inhomogeneous degradation of Mg results in a potential safety risk for the application of its implants. The particular concern is the occurrence of localized accelerated degradation of Mg under the relatively enclosed condition. The present work investigated such a phenomenon using the Mg-4Zn tube in artificial blood plasma. Its occurrence mechanism was explored through elaborate experiments, including the hydrodynamic platform, real-time electrochemical detection, COMSOL simulation, and morphological observations. It is found the deficiency of Ca2+ and PO43+ in the localized solution induced by the excessively high pH value was responsible for its occurrence.

Aluminum alloys are widely used in automotive construction, and since the introduction of biogenic ethanol into fuels, the issue of nonaqueous alcoholate corrosion has become an important topic. In this paper, the kinetics of AA1050 temperature-induced alcoholate pitting corrosion are examined experimentally with a specially constructed microreactor. The generated data are utilized to create a phase field model for the pit growth phase. The effects of ethanol-blend composition and water content are quantitatively assessed and simulated. Phase field simulations allow for the first time the mechanistic characterization of the chemical corrosion process with a water content of up to 0.3% and an estimation of relevant reaction parameters at temperatures of up to 150 degrees C. The approach can further be utilized to develop strategies for minimizing corrosion risk in-service. A novel microreactor is used to generate data for alcoholate corrosion of AA1050. The data are utilized to create a phase field model, which enables understanding of the present corrosion mechanism depending on temperature and water content. image

In this study, two classes of surface pre-treatment methods, namely three blast-cleaning methods with different abrasive materials (for initial preparation) and three mechanical methods (for repair applications) were applied to study their effects on surface morphology, composition, and corrosion of AA6082 substrates in artificial seawater. Coating adhesion and coating corrosion protection ability were investigated by applying a single-layer epoxy coating (250 μm dry film thickness) onto the substrates.The mechanical methods delivered cleaner surfaces with more regular surface morphologies, whereas the blast-cleaning methods revealed less clean surfaces and more irregular surface morphologies. The latter methods promoted the detrimental embedment of abrasive particles into the substrates. The corrosion performance of the pre-treated substrates was worse after blast-cleaning compared to mechanical treatments. However, all methods met the adhesion requirements for offshore applications. Regarding coating degradation, only the filiform corrosion test delivered meaningful results, whereas the duration of the cyclic corrosion test (3000 h) was too short to distinguish between the different methods because of neglectable degradation. Mechanical interlocking and isotropic surface morphologies are found to be more important for coating performance than a cleaner surface, and the remains of blasting material were neglectable too.

This paper provides a comprehensive derivation and application of the nonlocal Nernst-Planck-Poisson (NNPP) system for accurate modeling of electrochemical corrosion with a focus on the biodegradation of magnesium-based implant materials under physiological conditions. The NNPP system extends and generalizes the peridynamic bi-material corrosion model by considering the transport of multiple ionic species due to electromigration. As in the peridynamic corrosion model, the NNPP system naturally accounts for moving boundaries due to the electrochemical dissolution of solid metallic materials in a liquid electrolyte as part of the dissolution process. In addition, we use the concept of a diffusive corrosion layer, which serves as an interface for constitutive corrosion modeling and provides an accurate representation of the kinetics with respect to the corrosion system under consideration. Through the NNPP model, we propose a corrosion modeling approach that incorporates diffusion, electromigration and reaction conditions in a single nonlocal framework. The validity of the NNPP-based corrosion model is illustrated by numerical simulations, including a one-dimensional example of pencil electrode corrosion and a three-dimensional simulation of a Mg-10Gd alloy bone implant screw decomposing in simulated body fluid. The numerical simulations correctly reproduce the corrosion patterns in agreement with macroscopic experimental corrosion data. Using numerical models of corrosion based on the NNPP system, a nonlocal approach to corrosion analysis is proposed, which reduces the gap between experimental observations and computational predictions, particularly in the development of biodegradable implant materials.

Using the grand canonical density functional theory (GC-DFT) approach, the investigation focuses on the significance of incorporating anion species into electrolyte solvation structures, particularly with doubly charged Mg2+ ions. Our work extends previous methodologies by examining the thermodynamic stability of acetonitrile (AN) at the interface with Mg3Bi2 and Mg2Sn. Based on energy comparisons, two distinct anions, TFSI− and ClO−, are strategically introduced. Despite the known chemical compatibility of alloy anodes with the electrolyte solution, our research discovers a novel form of solvent degradation, which has also been noted with pure Mg anodes and conventional electrolytes. Notably,a the AN molecule near anion species exhibits less susceptibility to reduction (-0.8 — -0.4 V vs. Mg2+/Mg) in the lower potential range, compared to the fully dissociated solvation structure. Charge density difference and density of states analyses detail how solvent molecules become electrophilic, causing the LUMO to overlap with the Fermi level at lower potentials when the electrostatic interaction with anion species is considered. Experimental studies using nuclear magnetic resonance (NMR) spectroscopy and linear sweep voltammetry (LSV) validate the theoretical outcomes, providing a comprehensive understanding. By augmenting prior approaches, our methodology offers valuable guidance for electrolyte composition based on predominant solvation structures in multivalent solutions.

Stress corrosion cracking (SCC) failure is a multi -physics phenomena and is usually modelled at the microstructural level, which includes mechanical, chemical, and electrochemical contributions. In aggressive corrosive environments, materials prone to localized corrosion, can suffer heavy pitting corrosion. Subsequently, pit -to -crack transition events might be initiated. Respective mechanical assisted pitting corrosion propagation processes are determined by mechanical, chemical, and electrochemical properties as well as transport properties of ions at grain boundaries. Variations in the electrolyte exposure conditions (including the kinetics at the solid-liquid interface), different mechanical loading scenarios, grain -specific crystal anisotropies, and other local factors combine to produce a highly complex SCC-type interaction scenario at various time and length scales. Since corrosion is a slow process whereas brittle fracture is a rapid failure mechanism, the individual domain discretization-based solvers need to use distinct time steps and appropriate solver settings to become capable to accurately simulate such coupled events. In order to address the issues that arise while accounting for scaling effects in both time and space, and retaining coupled mechanistic interplay and including the aspects specified earlier, a sophisticated modelling method is necessary for the analysis of structural failure by SCC. In this work, a partitioned multi -physics computational approach is presented, using two separate single physics solvers coupled by the open -source coupling library preCICE. In the proposed computational setup, two separate software environments are used, with dedicated solver settings and different time steps, to simulate the mechanical fracture and dissolution -driven pitting corrosion for various loading and corrosion conditions, while also taking into account the effects of microstructural anisotropy. For a 2D polycrystalline model representing face -centered -cubic (fcc) material systems, selected numerical experiments are conducted that predict the evolution of fracture and crack path resulting from SCC. The framework has been further extended to simulate 3D problems as well. The corresponding results are evaluated to show the applicability of the proposed methodology.

The microstructure and composition of corrosion film deposited on pure Mg were systematically investigated, and for the first time DFT was used to reveal the adsorption behavior of carboxylates on the surface of corrosion layer rather than the metallic Mg. Presence of carboxylate inhibitor dramatically modified composition of the corrosion film and bonded directly with Mg atoms in the outermost MgO based corrosion layer by means of carboxyl. The bonded Mg atoms were lifted by 0.8 – 1.1Å from their original position when abundant inhibitors started to act as complexing agent, leading to deteriorated inhibition effect.

With the high interest in aerosol deposition in order to form high-quality coatings by solid-state impact, there is an increasing demand for developing general guidelines to estimate needed particle velocities and thus process parameter sets for successful deposition of ceramic materials. By using modeling approaches, rather different material properties in first instance can be expressed in terms of binding energies. Needed velocities for possible bonding can then derived by impact simulations and compared to experimental results from the literature. In order to study the role of binding energy on the impact behavior of ceramic particles in aerosol deposition, a molecular dynamics study is presented. Single-particle impacts are simulated for a range of binding energies, particle sizes and impact velocities. The results show that increasing the binding energy from 0.22 to 0.96 eV results in up to three times higher characteristic velocities corresponding to the threshold of bonding or grain size-dependent fragmentation of the particles. However, regardless of the binding energy, exceeding the characteristic velocities results in a similar deformation and fragmentation pattern. This allows for a general representation of the impact behavior as a function of normalized impact velocity for different ceramic materials. Apart from dealing with prerequisites for bonding of different materials by aerosol deposition, this study could also be generally relevant to the high-velocity deformation behavior of ceramics with different grain sizes.