Electrochemical Impedance Spectroscopy Data Research Articles

Impact of Electrochemical Impedance Spectroscopy Dataset Curation on Solid Oxide Cell Degradation Assessment

Abstract Electrochemical impedance spectroscopy (EIS) has become a standard measurement technique for detecting degradation in single cells and stacks of solid oxide cells (SOCs). Depending on the experimental setup and test equipment, instabilities and unexpected results can be observed in EIS measurements. For example, in the low-frequency range, instabilities can be induced by feed gas flow fluctuations. Another phenomenon are parasitic, inductive impedances that degrade the high-frequency range. To compensate for such influences in large EIS data sets, we propose a specially developed EIS data curation pipeline. Based on the results of its application, we demonstrate the impact on the quantitative and qualitative attribution of electrochemical processes from EIS using equivalent circuit modeling and distribution of relaxation times. Furthermore, the substantial differences in the temporal evolution of the latter during long-term experiments are highlighted for EIS measurements obtained at the SOC stack and single cell level. In addition, the significant misestimation of aging rates, especially with respect to the fuel electrode and the high-frequency series resistance, is shown when comparing EIS measurements, few of which exhibit a parasitic inductive impedance.

Fractional-Order Equivalent-Circuit Model Identification of Commercial Lithium-Ion Batteries

The precise identification of electrical model parameters of Li-Ion batteries is essential for efficient usage and better prediction of the battery performance. In this work, the model identification performance of two metaheuristic optimization algorithms is compared. The algorithms in comparison are the Marine Predator Algorithm (MPA) and the Partial Reinforcement Optimizer (PRO) to find the optimal model parameter values. Three fractional-order (FO) electrical equivalent circuit models (ECMs) of Li-Ion batteries with different levels of complexity are used to fit the electrochemical impedance spectroscopy (EIS) data operating under different states of charge (SoC) and different operating temperatures. It is found that there is a tradeoff between ECM complexity, identification accuracy, and precision.

Natural deep eutectic solvent-supported exfoliated graphite nanoplatelet-based electrochemical sensor for the determination of tyramine in fermented beverages

A novel electrochemical sensor was developed for the determination of tyramine. The sensor was constructed by modifying the surface of a glassy carbon electrode (GCE) with a film composed of exfoliated graphite nanoplatelets (xGnPs) dispersed in a natural deep eutectic solvent (NADES). The NADES was prepared by simply mixing equimolar amounts of caprylic acid and menthol and heating at 80 °C for 30minutes. Characterization studies confirmed the formation of a NADES through the attenuated total reflectance-Fourier transform infrared spectroscopy technique, while transmission electron microscopy demonstrated the ability of the NADES to disperse the xGnPs. Electrochemical impedance spectroscopy data showed that the modified sensor exhibited lower resistivity than the unmodified electrode. By optimizing the parameters of square wave voltammetry and experimental conditions to obtain the highest peak current values for tyramine oxidation, a calibration curve was constructed in a 0.1molL−1 phosphate buffer (pH 7.0), obtaining detection and quantification limits of 0.06 and 0.20mgL−1, respectively. Finally, the sensor was successfully applied to the determination of tyramine in fermented milk, beer, and non-alcoholic beer samples, with satisfactory recovery data.

Pore size, conductivity and surface area effects of rGO/ZnO/PTh hybrid nanocomposite for supercapacitors

This paper presents an easy and low-cost synthesis of reduced graphene oxide (rGO) and Zinc oxide (ZnO) as binary nanocomposite (rGO/ZnO) and rGO, ZnO and polythiophene (PTh) as ternary hybrid nanocomposite (rGO/ZnO/PTh). Firstly, graphene oxide (GO) was synthesized from modified Hummers method. And it was reduced by microwave-assisted method to form as reduced graphene oxide (rGO). Polythiophene (PTh) was chemically synthesized to comparison with nanocomposites. Secondly, binary nanocomposite of rGO/ZnO were synthesized by probe-sonication method. Thirdly, rGO/ZnO/PTh hybrid nanocomposite was chemically synthesized using FeCl3 as an oxidant. All these materials were examined by many different techniques, such as FTIR-ATR, SEM-EDX, TGA-DTA, BET, Four-point probe analysis, cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS) analysis. The average pore width, conductivity and specific capacitance values were comparatively investigated to understand these effects on electrochemical performances of supercapacitor devices.The highest specific capacitance (Csp) was obtained as Csp = 430 F/g for rGO/ZnO nanocomposite at a scan rate of 2 mV/s. Moreover, it also shows a good charge/discharge performance (up to 1000 cycles) with 98.43 % capacitance retention at a scan rate of 100 mV/s by CV method. However, GCD and EIS methods have showed the highest specific capacitances as Csp = 85.6 F/g at 10 A/g, and Csp = 25.8 F/g for both hybrid rGO/ZnO/PTh nanocomposites, respectively. In addition, a new equivalent circuit model [R1(C1(Rct(C2R2)))] was suggested to interpret the EIS data for supercapacitor devices. As a results, both binary and ternary nanocomposites may be recognized as electrode materials with three different methods (CV, GCD and EIS) in supercapacitor applications.

Pyridazine derivatives as effective anti-corrosion additives for carbon steel in 1 M HCl: Electrochemical, surface and theoretical studies

The study aimed to investigate the corrosion inhibition performance of two new pyridazine derivatives namely ((E)-2-(3-(4-(carboxymethoxy)-3-methoxystyryl)-5-(2-chlorobenzyl)-6-oxopyridazin-1(6H)-yl)acetic acid (CO6) and (E)-6-(4-hydroxy-3-methoxystyryl)pyridazin-3(2H)-one (CO39) for carbon steel (C.S) in 1M HCl by combining electrochemical techniques, surface analysis and theoretical studies. The electrochemical impedance spectroscopy (EIS) data indicated that the inhibitory efficiencies of CO6 and CO39 increased with increasing inhibitor concentration but decreased with increasing temperature, achieving 97.0 % and 95.3 %, respectively. The results of the potentiodynamic polarization (PDP) measurements show that the two pyridazine derivatives acted as mixed inhibitors. In addition, experimental studies have shown that CO6 and CO39 reduce corrosion rates by adsorbing on the steel surface following Langmuir adsorption isotherms. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) indicated that a protective layer had been developed on the surface of the carbon steel, which prevented corrosion from progressing. Quantum chemical computations and the molecular structures of the inhibitors were employed to investigate the mechanism of inhibition. The adsorption of the compounds investigated on C.S was validated by theoretical calculations including density functional theory (DFT) and molecular dynamics simulation (MDS).

Optimized Fabrication of Carbon-Fiber Microbiosensors for Codetection of Glucose and Dopamine in Brain Tissue.

Dopamine (DA) signaling is critically important in striatal function, and this metabolically demanding process is fueled largely by glucose. However, DA and glucose are typically studied independently and, as such, the precise relationship between DA release and glucose availability remains unclear. Fast-scan cyclic voltammetry (FSCV) is commonly coupled with carbon-fiber microelectrodes to study DA transients. These microelectrodes can be modified with glucose oxidase (GOx) to generate microbiosensors capable of simultaneously quantifying real-time and physiologically relevant fluctuations of glucose, a nonelectrochemically active substrate, and DA, which is readily oxidized and reduced at the electrode surface. A chitosan hydrogel can be electrodeposited to entrap the oxidase enzyme on the sensor surface for stable, sensitive, and selective codetection of glucose and DA using FSCV. This strategy can also be used to entrap lactate oxidase on the carbon-fiber surface for codetection of lactate and DA. However, these custom probes are individually fabricated by hand, and performance is variable. This study characterizes the physical nature of the hydrogel and its effects on the acquired electrochemical data in the detection of glucose (2.6 mM) and DA (1 μM). The results demonstrate that the electrodeposition of the hydrogel membrane is improved using a linear potential sweep rather than a direct step to the target potential. Electrochemical impedance spectroscopy data relate information on the physical nature of the electrode/solution interface to the electrochemical performance of bare and enzyme-modified carbon-fiber microelectrodes. The electrodeposition waveform and scan rate were characterized for optimal membrane formation and performance. Finally, codetection of both DA/glucose and DA/lactate was demonstrated in intact rat striatum using probes fabricated according to the optimized protocol. Overall, this work improves the reliable fabrication of carbon-fiber microbiosensors for codetection of DA and important energetic substrates that are locally delivered to the recording site to meet metabolic demand.

Multiwalled-Carbon-Nanotube-Modified Li2ZnTi3O8 as Anode with Improved Cycling Stability and Rate Capability for Lithium-Ion Batteries

Due to its better electronic conductivity and larger lithium intercalation capacity, a multiwalled-carbon-nanotube (MWCNT)-modified Li2ZnTi3O8 (LZTO) has significant potential for use as an anode material for Li-ion batteries. The rapid and affordable ball-mill-aided solid state approach is used to synthesize LZTO with MWCNT-modified Li2ZnTi3O8 (LZTO@MWCNTs). Electrochemical performance tests of the anode material were carried out using cyclic voltammetry (CV), galvanostatic charge-discharge measurement, and electrochemical impedance spectroscopy (EIS). The CV experiments reveal that the LZTO@MWCNT electrode has a lower anodic and cathodic peak potential difference (0.184 V) than the LZTO electrode (0.211 V) and LZTO@MWCNT electrode has better electrochemical reversibility than that of pristine electrode after five cycles utilizing the same procedure. LZTO@ MWCNTs anode material has a higher charge-discharge capacity than LZTO anode material at 0.1–5 C rates. After 100 cycles, the initial discharge capacities of LZTO@MWCNT electrode are 227 and 142 mAh g−1 at the 1C–5C rate, respectively, whereas the initial discharge capacities of LZTO electrode are only144 and 28 mAh g−1 in the same condition. The charge transfer and SEI resistance of the LZTO anode are 19.31 and 62.74 ohms, respectively, after 30 cycles at 1C, according to the EIS data. Additionally, the values for the LZTO@MWCNTs anode are 7.93 and 12.06 ohms for charge transfer and SEI resistance, respectively.

Flexible ceramic based ‘paper separator’ with enhanced safety for high performance lithium-ion batteries: Probing the effect of ceramics impregnation on electrochemical performances

Cellulose is now considered as an appealing environment friendly material for the development of sustainable separators for rechargeable batteries. This study thus aims to develop high performance paper based ceramic separators with superior electrolyte wettability (>170 %), high thermal stability (>200 °C) and excellent flame safety. While developing paper based ceramic separator, extensive studies are carried out to understand thenature and functionality of different nano-structured ceramic materials (Al2O3, BaTiO3 and ZrO2) impregnated in paper matrix. Nano-ZrO2 facilitates an effective pathway for Li +-ion transport (tLi+ = 0.51), whereas BaTiO3 and Al2O3 ceramics show moderate Li +-ion transport properties (tLi+ of 0.29 and 0.30 respectively). Electrochemical Impedance Spectroscopy (EIS) measurement clearly reveals that ZrO2 exhibits more interfacial compatibility with the electrodes (MCMB and LiNi1/3Mn1/3Co1/3O2) compared to the other two ceramic separators during full cell operation. The developed paper separators show excellent electrochemical properties in terms of cycling (tested 300 cycles), multi-electrode compatibility and rate capabilities at different current densities of 0.1–1.2 mAcm−2. The pre- and post-electrochemical EIS data reveal that ZrO2 based impregnation offers significantly lower charge transfer resistance compared to the other two separators. The study thus demonstrates a real promise for its successful application in Li-ion battery towards achieving sustainability and safety.

High anti-corrosion barrier of poly (methyl methacrylate)-silica coatings explained: A thousand-days study

Poly (methyl methacrylate) (PMMA)-silica coatings form a few micrometers thick anti-corrosive barrier that blocks corrosive species when exposed to harsh environments. Their excellent anti-corrosive performance stands out for protecting metal alloys immersed in seawater for long periods (>2 years), making them compliant for applications in the marine, aeronautical, and automotive industries. A key approach to understanding the degradation of high-performance coatings over time consists of analyzing their water uptake-induced structural changes. This work examines in detail the uptake and structural modification of PMMA-silica coatings on Al alloys immersed for more than 1000 days in 3.5 wt% NaCl solution. Gravimetry, thermal analysis, infrared spectroscopy and electrochemical impedance spectroscopy (EIS) were employed to monitor the evolution of coated samples. Nuclear magnetic resonance, X-ray photoelectron spectroscopy, electron and atomic force microscopies before and after immersion indicate a slight leaching-induced surface roughening due to silica hydrolysis. These findings comply with the low uptake values (∼0.6 vol%) and a less-Fickian diffusion coefficient obtained from modelling of the EIS data. The high impedance modulus (>GΩ) is related to the highly cross-linked structure, resulting in a very low permeation rate of the electrolyte. The applied methodology is of crucial importance for establishing a standardized analysis for high-performance protective coatings.

Intelligent salivary biosensors for periodontitis: in vitro simulation of oral oxidative stress conditions.

One of the most common oral diseases affecting millions of people worldwide is periodontitis. Usually, proteins in body fluids are used as biomarkers of diseases. This study focused on hydrogen peroxide, lipopolysaccharide (LPS), and lactic acid as salivary non-protein biomarkers for oxidative stress conditions of periodontitis. Electrochemical analysis of artificial saliva was done using Gamry with increasing hydrogen peroxide, bLPS, and lactic acid concentrations. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) were conducted. From EIS data, change in capacitance and CV plot area were calculated for each test condition. Hydrogen peroxide groups had a decrease in CV area and an increase in percentage change in capacitance, lipopolysaccharide groups had a decrease in CV area and a decrease in percentage change in capacitance, and lactic acid groups had an increase of CV area and an increase in percentage change in capacitance with increasing concentrations. These data showed a unique combination of electrochemical properties for the three biomarkers. Scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) employed to observe the change in the electrode surface and elemental composition data present on the sensor surface also showed a unique trend of elemental weight percentages. Machine learning models using hydrogen peroxide, LPS, and lactic acid electrochemical data were applied for the prediction of risk levels of periodontitis. The detection of hydrogen peroxide, LPS, and lactic acid by electrochemical biosensors indicates the potential to use these molecules as electrochemical biomarkers and use the data for ML-driven prediction tool for the periodontitis risk levels.

Revealing the degradation patterns of lithium-ion batteries from impedance spectroscopy using variational auto-encoders

The aging life estimation of lithium-ion batteries (LIBs) is of great significance to the use, maintenance and economic analysis of energy storage systems. The estimation method of aging life based on electrochemical impedance spectroscopy (EIS) has received more attention due to its high accuracy. Due to the high dimensionality of impedance data, it cannot directly reflect the decline trend, this paper proposes a method of combining variational auto-encoders (VAE) and bidirectional gated recurrent unit (BiGRU) for automatic feature extraction from EIS data and battery aging life estimation. Reducing the dimensionality of impedance data through VAE can greatly reduce computational complexity, and the high-quality features extracted are mapped to battery capacity using BiGRU, which can improve estimation accuracy. The validation results show that the method can adapt to various working conditions, and has good estimation accuracy and robustness. MAE and RMSE are less than 1.27 mAh and 1.43 mAh, respectively. We demonstrate that the latent variables extracted from EIS data using unsupervised learning method reliably represent the degradation patterns of LIBs.

Evaluation of electrochemical performance of antimony modified screen-printed carbon electrodes

This study compares the electrochemical performance of screen-printed carbon electrodes (SPCEs) modified with antimony (Sb/SPCEs) under different potentiostatic pre-plating conditions. Neutral Red (NR) was employed as a novel redox probe to evaluate the electrochemical performance of Sb/SPCEs. It was demonstrated that NR in the protonated form performs quasi-reversible redox transformations at bare SPCE and Sb/SPCEs in phosphate buffer solutions (pH 5.5±0.5) in the potential range of (−0.30)–(−0.75) V, where the antimony is not electroactive. Sb/SPCEs were studied electrochemically by cyclic voltammetry (CV) / electrochemical impedance spectroscopy (EIS), and morphologically by scanning electron microscopy (SEM). Cyclic voltammetry investigations revealed the dependence of the electrochemical performance of Sb/SPCEs on the degree of coverage of the substrate with the metal. The obtained CV, EIS, and SEM data are consistent. The lowest charge transfer resistance (Rct) value (6 Ω) was obtained at Sb/SPCE with the highest degree of antimony coverage. To investigate the electroanalytical performance of Sb/SPCEs, nickel (II) ions were utilized as a model analyte. A study of roughness factors and sensitivity towards nickel (II) ions for Sb/SPCEs using two-tailed Pearson's criterion revealed a high degree of correlation between their electrochemical and electroanalytical properties. The results show that using NR as a redox probe can help controlling modification processes during the development of innovative antimony-containing sensors.

Comparative Investigation of the Corrosion Protection Efficacy of Spondias mombin Leaf Extracts on AA2024 Alloy and Low Carbon Steel in Acidic Medium

The anti-corrosive property of Spondias mombin (SM) leaves extract on low carbon steel (LCS) and aluminium alloy (AA2024) in 1M HCl solution was investigated using gravimetric and electrochemical methods. The results obtained from the weight loss experiment performed at various temperatures (30, 45, and 60 0C) show that the inhibition efficiency values decreased with temperature rise but increased as the inhibitor concentrations were increased. At 30 0C, optimum inhibition efficiency (IE) of 80.11% and 78.06% were obtained for LCS and AA2024 respectively. The potentiodynamic polarization (PDP) result reveal that an optimum IE values of 92.1% and 74.0% were respectively obtained for LCS and AA2024 while the electrochemical impedance spectroscopy (EIS) data show IE values of 91.5% and 74.5% for LCS and AA2024 respectively. In all cases, the values of inhibition efficiency obtained for LCS were greater than those obtained for AA2024. This implies that SM leaf extracts offers a better protective-layer on LCS surface compared to the AA2024 alloy in the acidic medium. The isotherm study reveal that Langmuir model best fitted the adsorption of the leaf extracts on LCS while Freundlich model best fitted the leaf extracts adsorption on AA2024 surface.

Accurate ion type and concentration detection using two bare electrodes by machine learning of non-faradaic electrochemical impedance measurements of an automated fluidic system

Detecting ion types and their concentrations in multi-ion aqueous solutions poses a complex challenge with significant implications for sensing applications. This paper introduces an automated fluidic system using two bare electrodes to measure electrochemical impedance spectroscopy (EIS) data and evaluates deep neural network (DNN) for ion type and concentration detection. Additionally, a data-augmentation model is proposed for non-faradaic voltage sweep EIS data, intended for neural network training. To validate the effectiveness of our approach, prepared solutions featuring varying concentrations of LiCl, NaCl, and KCl are experimented. Impedance (Z) vs DC bias voltage (V) data, i.e., ZV-EIS are measured from −400 mV to + 400 mV at 9 different frequencies up to ∼ 200 kHz, for 148 automatically prepared solutions, having total salt concentrations up to 200 mM. DNN models are trained with augmented samples and successfully identified individual ions in test set solutions and their concentrations with 4.51 mM average deviation. The study delves into the nuanced impacts of factors such as sampling voltage window, and measurement frequency on the performance of the machine learning models, 8 mV and 20 Hz giving the most significant results.

Dynamic Fractional-Order Model of Proton Exchange Membrane Fuel Cell System for Sustainability Improvement

The proton exchange membrane fuel cell (PEMFC) stands at the forefront of advancing energy sustainability. Effective monitoring, control, diagnosis, and prognosis are crucial for optimizing the PEMFC system’s sustainability, necessitating a dynamic model that can capture the transient response of the PEMFC. This paper uses a dynamic fractional-order model to describe the behaviors of a stationary micro combined heat and power (mCHP) PEMFC stack. Based on the fractional-order equivalent circuit model, the applied model accurately represents the electrochemical impedance spectroscopy (EIS) and the dynamic voltage response under transient conditions. The applied model is validated through experiments on an mCHP PEMFC stack under various fault conditions. The EIS data is analyzed under different current densities and various fault conditions, including the stoichiometry of the anode and cathode, the stack temperature, and the relative humidity. The dynamic voltage response of the applied model shows good correspondence with experimental results in both phase and amplitude, thereby affirming the method’s precision and solidifying its role as a reliable tool for enhancing the sustainability and operational efficiency of PEMFC systems.

Uneven Usage Battery State of Health Estimation via Fractional-Order Equivalent Circuit Model and AutoML Fusion

To accurately predict the State of Health (SOH) of lithium-ion batteries under the continuously changing charging and discharging conditions in practical applications, this study proposes a hybrid modeling approach that integrates a Fractional Order Equivalent Circuit Model (F-ECM) with the AutoGluon automatic machine learning framework. By leveraging Electrochemical Impedance Spectroscopy (EIS) to capture battery frequency response characteristics, F-ECM accurately fits EIS data to extract detailed internal state parameters. The integration of AutoGluon automates the machine learning process, enhancing the precision of SOH predictions. Through testing and analysis on real battery datasets, this method has demonstrated superior prediction precision and computational efficiency compared to existing mainstream modeling approaches. Specifically, the hybrid method achieved a Root Mean Square Error (RMSE) of 2.12% and a Mean Absolute Error (MAE) of 1.67%. This study presents a highly accurate, interpretable, and adaptable predictive framework for lithium-ion battery health assessment, offering valuable insights for battery health management system development.

Novel electrochemical and thermodynamic conditioning approaches and their evaluation for open cathode PEM-FC stacks

The Air-cooled and open cathode proton exchange fuel cell (PEMFC) offers particular advantages in terms of complexity and weight reduction. Therefore, it seems very attractive to support the aviation industry in their de‑carbonisation process. Major challenges in terms of operational stability and reliability hinders this technology to be used in commercial applications.Therefore, this study thoroughly examines the conditioning and optimization of open cathode PEMFC stacks, drawing insights from experimental findings. Key areas of focus include the enduring impact of pre-humidification, the quantification of efficiency enhancements through reconditioning via oxygen starvation, and the refinement of Electrochemical Impedance Spectroscopy (EIS) data analysis under non-stationary conditions.The pre-humidification enhances stack performance by improving cell voltages from 40.09 V to 41.37 V at 30 A, increasing membrane humidity and improving efficiency. Furthermore, the residual water in the stack also functions as evaporative cooling and can assist in limiting the operating temperature of the stack during system start up. However, it is demonstrated that excessive soaking with water leads to severe flooding phenomena at the beginning of operation.A reconditioning period of 100 s through oxygen starvation induces a notable increase in stack voltage, from 41.37 V to 47.79 V at 35 A, which decreases to 43.56 V after 10 min of operation. This corresponds to an average increase in electric energy provision of about 6.2% at constant hydrogen consumption, attributed to PtOx reduction and increased water production.Despite limitations outside the medium frequency range (approximately 10 Hz – 15 kHz) for non-stationary conditions, EIS aids in understanding of the stack behaviour and supports the interpretation of current and voltage results. A novel evaluation method enables the quantitative description of the condition using EIS data. This data reveals a considerable drop (on average about 21,5% for the 10 A point and about 27,4% for the 30 A point) in charge transfer resistances of the fuel cell from initial operation to measurements after overnight soaking and the first series of oxygen starvation recovery.

Data-driven internal temperature estimation methods for sodium-ion battery using electrochemical impedance spectroscopy

As sodium-ion batteries (SIBs) move towards commercialization, safety monitoring of SIBs has become the next key issue, and how to avoid thermal runaway is one of the toughest challenges. The electrochemical impedance spectroscopy (EIS) method for the internal temperature estimation of lithium-ion batteries has received considerable attention due to its non-invasive detection and high accuracy. However, research on the impedance-temperature characteristics of commercial SIBs remains limited, and EIS methods suitable for estimating the internal temperature of SIBs require further investigation. In this study, four commercial 26,700 SIBs were tested, the EIS results of the batteries in various state-of-charge (SoC) states and at different temperatures were systematically investigated, and a method for estimating the internal temperature of SIBs on the basis of the combination of EIS and machine learning (ML) is proposed. Seven component parameters were extracted as features from the raw EIS data using equivalent circuit model fitting, and four features, which were found to be highly correlated with temperature, were further selected by correlation analysis. The mapping relationship between the extracted features and the internal temperature of the battery was established based on three ML regression models. Results demonstrate that the average estimation error of the multi-layer perceptron models for the internal temperature of the battery with unknown SoC states is only 1.086 °C. This paper fills the gap in the temperature characterization of EIS for SIBs and proposes an effective method for overcoming the cross-coupling of the battery EIS with the SoC and temperature within the framework of mechanical learning to estimate the internal temperature of SIBs.

Evaluating the performance of corn peptone in preventing the corrosion of mild steel immersed in HCl

AbstractThe effects of biodegradable corn peptone on the corrosion behavior of mild steel in 0.1M HCl were evaluated. The results from the weight loss experiments indicate that changing the amount of corn peptone from 50 to 500 ppm considerably decreased the corrosion rate of the coupons from 28.24 to 4.67 mpy. However, heating the solutions had negative effects on this trend. According to the calculations, dissolving the inhibitor modified the thermodynamic parameters of the corrosion phenomenon. In addition, the adsorption of corn peptone was best fitted with the Langmuir isotherm. The Tafel polarization plots revealed that the presence of corn peptone decreased the corrosion current density from 134.9 to 6.7 µA cm−2. This was compatible with the electrochemical impedance spectroscopy data. Furthermore, X‐ray photoelectron spectroscopy analysis confirmed the adsorption of the tested peptone on the surface of the working electrodes, which based on the atomic force microscope images, reduced the surface roughness of the specimens.

Investigation on microstructures, mechanical properties, and corrosion behavior of novel biodegradable Zn-xCu-xTi alloys after hot rolling fabricated by self-developed newly gradient continuous casting

The poor mechanical properties exhibited by pure Zn effectively prohibit its utilization as a viable material for biodegradable implants. Hence, the primary area of interest has been directed towards alloy design and process strategies of biodegradable Zn alloys. To this end, novel biodegradable Zn-xCu-xTi (Cu: x = 0.001–2.72 and Ti: x = 0.03–1.21) alloys were designed and fabricated using a gradient continuous casting method followed by homogenization and rolling. The fabricated samples were then investigated in terms of their microstructures, mechanical properties, and corrosion behavior. The results showed that the as-cast Zn-0.001Cu-0.037Ti and Zn-1.50Cu-1.06Ti alloys possess better mechanical properties and corrosion resistance. The yield strength, elongation, and highest charge transfer resistance (Rct) of Zn-0.001Cu-0.037Ti were 137 MPa, 60 %, and 2227.9 Ω, respectively. On the other hand, the Zn-1.50Cu-1.06Ti alloy exhibited 250 MPa yield strength, 12.7.% elongation, and 11113.0 Ω Rct, respectively. Higher Cu and Ti content also led to larger second phases. However, as-hot rolled Zn-1.50Cu-1.06Ti alloy displayed better mechanical properties as compared to the Zn-0.001Cu-0.037Ti alloy due to a very fine nanocrystalline structure. Furthermore, the hot-rolled Zn-xCu-xTi alloys demonstrated superior corrosion resistance compared to their as-cast counterparts, as evidenced by the analysis of the potentiodynamic polarization and electrochemical impedance spectroscopy data. This improved corrosion resistance is attributed to the refined grain size, fractured second phases which reduce both the anode and cathode areas, and the formation of protective oxide layers. Lastly, the corrosion products primarily consisted of carbon, phosphorus, calcium, and oxygen, suggesting the presence of Zn(OH)2, carbonates, and phosphates.