Search Results (236)

The electrochemical performance of eight commercially available bioelectrodes for electrophysiological measurements was systematically evaluated in isotonic saline solution. The studied bioelectrodes included sintered Ag/AgCl pellet, cup and ring, an Ag/AgCl multipin, tin (Sn) ring and disc, a gold cup, and a stainless-steel needle. Open circuit potential (OCP) and drift rate, electrochemical impedance spectroscopy (EIS), and electrochemical noise (ECN) measurements were performed to assess interfacial stability, impedance behavior, and generated noise in time and frequency domains. Scanning electron microscopy (SEM) and Energy-dispersive X-ray spectroscopy (EDS) were used to study the morphology and chemical composition of the bioelectrodes. Ag/AgCl-based bioelectrodes exhibited the highest OCP stability and potential reproducibility, lowest impedance, and electrochemical noise, attributed to the fast and reversible Ag/AgCl electrochemical equilibrium, and high area related to roughness and porosity. EIS analysis showed predominantly low-resistance charge-transfer behavior and high capacitance for Ag/AgCl bioelectrodes, while tin, gold, and stainless-steel bioelectrodes displayed higher impedance and mixed capacitive/resistive responses associated with passive oxide films and slower interfacial kinetics. Tin, gold, and stainless-steel bioelectrodes also presented substantially higher low-frequency noise and OCP drift rate. Among all tested bioelectrodes, sintered Ag/AgCl bioelectrodes demonstrated the most favorable electrochemical characteristics for electrophysiological signal acquisition, particularly for low-amplitude and low-frequency biosignals. Full article

In this work, an experimental evaluation was performed using four analytical methods applied to electrochemical noise (EN) signals to estimate the corrosion rate ($C_r$) of reinforced concrete structures. A dataset comprising 10,166 synchronized EN files acquired over approximately 220 days was analyzed. The EN signals were obtained from various natural aqueous media, including seawater and river water, as well as from two laboratory reference media (3.5% NaCl solution and reverse-osmosis water). The Statistical Method (SM), the Fast Fourier Transform (FFT), the Maximum Entropy Method (MEM), and the Stockwell Transform (ST) were used to calculate $C_r$. The resulting corrosion rates were evaluated using a two-way analysis of variance (ANOVA) with full interaction, followed by Tukey HSD post hoc comparisons. Significant effects were found for both the analytical methods and the exposure media ($p<0.001$). Among the methods evaluated, MEM showed the greatest statistical stability and robustness, while ST showed the greatest tolerance to noise and the non-stationary characteristics of the EN signals. Estimated corrosion rates ranged from $0.0366 \mathrm{mm}/\mathrm{year}$ in reverse-osmosis water (MEM) to $0.2022 \mathrm{mm}/\mathrm{year}$ in 3.5% NaCl (MEM). For ST, the corresponding values ranged from $0.0652 \mathrm{mm}/\mathrm{year}$ to $0.3504 \mathrm{mm}/\mathrm{year}$ in the same media. These results demonstrate that both the analytical method and the corrosive medium significantly influence EN-based corrosion rate estimates and highlight the potential of MEM and ST for long-term corrosion monitoring of reinforced concrete. Full article

Inconel superalloys are employed in demanding components of different equipment. However, they can be exposed to atmospheric corrosion systems, such as marine and industrial environments. This research is focused on studying the localized corrosion susceptibility of Inconel 600, 690 and 718 exposed to H₂SO₄, 1 wt.% and 3.5 wt. % NaCl solutions, simulating marine and industrial atmospheres at 25 ± 0.5 °C. Localized corrosion behavior was characterized by electrochemical noise (EN) and cyclic potentiodynamic polarization (CPP) curves according to ASTM 6-199 ASTM G61 standards. The EN technique was analyzed through time series and analysis for chaotic systems, such as Hurst, Lyapunov and Husdorff coefficients, to determine the corrosion type of each system to reduce the uncertainty in common statistical analysis. The EN results show how Inconel superalloys tend to present localized attacks, being more notorious in NaCl. The application of specialized methods such as Hurst and Lyapunov helped to determine the corrosion system when alloys were characterized by EN. The results indicated that all superalloys exhibit positive hysteresis under CPP, indicating susceptibility to localized pitting corrosion. Full article

Magnetic sensors offer a physically grounded and non-invasive approach to probing biological processes that remain inaccessible to optical, electrochemical, and radio-frequency techniques in complex agricultural environments. In recent years, advances in both classical and quantum magnetic sensors have enabled the detection of bioelectromagnetic signals across plants, soils, animals, and aquatic systems, spanning spatial scales from ionic currents to organ-level electrophysiology and population-level dynamics, positioning magnetometry as an emerging modality within the broader biosensor landscape. This review surveys the evolution of magnetic sensing technologies for agricultural and animal systems, from robust classical sensors used in navigation and soil mapping to quantum-enabled platforms, including Optically Pumped Magnetometers (OPMs) and Nitrogen-Vacancy (NV) centers, capable of resolving pT to fT biomagnetic signals. We synthesize the characteristic amplitudes, frequency ranges, and physiological origins of agriculturally relevant magnetic signals, and critically assess how techniques originally developed for medical magnetoencephalography, magnetocardiography, and low-field magnetic resonance imaging (LF-MRI) are being translated into field-deployable agricultural applications. Beyond sensing hardware, we highlight the essential role of artificial intelligence in extracting weak biological signals from dominant environmental noise, enabling synthetic gradiometry, low-field image reconstruction, and scalable interpretation in unshielded settings. Finally, we discuss how the integration of magnetic biosensing with digital twins supports predictive, multiscale monitoring of plant, animal, and ecosystem health. Together, these developments position magnetometry as an enabling technology for next-generation biosensors in precision and sustainable agriculture. Full article

Corrosion fatigue is an accelerated failure mechanism in metallic components and coated systems, where the effectiveness of the polymer coating is determined by the structural integrity and adhesion at the coating/substrate interface. This study investigated the corrosion fatigue interaction in AISI 410 steel with and without a poly(3-hexylthiophene)/poly (methyl methacrylate) (P3HT/PMMA) coating exposed to a 3 wt.% NaCl solution under four stress levels $\left(∆\sigma \right)$ at room temperature. Electrochemical noise (EN) was recorded during the test, the surface and interface were characterized using scanning electron microscopy (SEM), and the mechanical behavior was quantified using $da/dN$ vs. $∆K$ and $\sigma$ vs. N curves. The coated samples exhibited a wider potential range ($\approx \pm$400 mV) than the uncoated steel ($\approx \pm$200 mV), indicating localized electrochemical activity under the coating. SEM observations revealed microblisters at low stress levels and coating cracking at high stress levels, with localized substrate exposure, slip bands, and microcracks. Overall, the results showed that the corrosion fatigue is governed by electrochemical activity under the coating and cathodic delamination, which reduces adhesion, locally exposes the steel, and causes the initiation and propagation of cracks. Full article

Battery Energy Storage Systems (BESSs) are increasingly deployed in grid-scale applications, electric mobility, and renewable integration, where safety, reliability, and longevity are critical. Thermal runaway remains one of the most severe failure modes in lithium-ion batteries, often triggered by complex interactions between electrochemical, thermal, and mechanical phenomena. This paper presents an extended hybrid Physics-Informed Neural Network (PINN) framework for thermal anomaly prediction and early detection of runaway precursors in BESS. The proposed architecture integrates governing physical laws, specifically the Bernardi heat generation equation and Fick’s diffusion law, within a deep learning pipeline composed of a physics module, a temporal Bi-LSTM, and an attention mechanism for explainability, which may represent an obstacle in the application of deep learning algorithms. Beyond the initial formulation, the extended version presented here provides a deeper theoretical background, an expanded methodological justification, a more comprehensive comparison with state-of-the-art approaches, and a detailed discussion on scalability, uncertainty, and deployment challenges. The results for synthetic yet physically consistent datasets represent a proof of concept of the PINN approach, which can achieve superior generalization, robustness to noise, and interpretability compared to purely data-driven baselines, achieving an accuracy above 90% and an AUC of 0.95. The framework contributes to proactive safety management in cyber-physical energy systems and establishes a foundation for real-time, physics-aware anomaly detection in safety-critical BESS applications, e.g., marine transportation contexts and port environments. Full article

Potentiostats are essential to electrochemical sensing, enabling precise control of electrode potentials and measurement of current responses. As demand grows for portable, wearable, and point-of-care systems, potentiostat design has evolved from benchtop instruments to compact, low-power, and wirelessly connected platforms. This review provides a comprehensive, system-level perspective on modern potentiostat architectures, covering operational principles, analog front-end design, signal generation and acquisition, communication protocols, and software integration. Unlike prior reviews that treat these aspects independently, this work integrates electrochemical theory with electronic design and data communication frameworks. Key components, including operational amplifiers, transimpedance amplifiers, DAC/ADC subsystems, and microcontroller-based control, are examined alongside communication protocols such as SPI, I²C, Bluetooth Low Energy, Wi-Fi, and NFC. Critical challenges related to miniaturization, noise, power constraints, and reproducibility are analyzed using representative platforms. This review highlights the transition of potentiostats into integrated, intelligent, and connected sensing systems, and outlines design considerations for scalable electrochemical applications in clinical, environmental, and industrial domains. Full article

The corrosion of carbon steel in marine–industrial atmospheric environments remains a significant challenge due to the combined effect of aggressive ions such as chlorides and sulfates. In this context, this study aims to explore the inhibitory action of expired omeprazole applied to mild steel AISI 1018 evaluated on a solution simulating atmospheric corrosion (0.1 M Na₂SO₄ + 3% wt NaCl) over 72 h. The material was characterized using EDS to determine its composition of AISI 1018 steel, while Raman spectroscopy was employed to identify the functional groups and heteroatoms present on the molecular structure of omeprazole. Electrochemical noise (EN) measurements were used to evaluate the corrosion rate, type of corrosion and mechanism. Also, quantum chemical calculations of density function theory (DFT) were performed to predict the relationship between molecular structure and inhibition efficiency. The results indicate that 50 ppm provides the most stable and effective corrosion inhibition over time, as evidenced by increases in noise resistance and inhibition efficiency. In contrast, 75 ppm exhibits improved surface morphology at the end of the exposure period, which indicates enhanced surface coverage. The DFT results reveal that omeprazole possesses suitable electronic properties for corrosion inhibition, including moderate reactivity, electron-donating ability, and favorable charge distribution that promotes adsorption onto the metal surface. SEM analysis corroborates that surface damage is significantly reduced in the presence of the inhibitor, particularly at 75 ppm. This study provides new insights into the use of expired pharmaceutical compounds as corrosion inhibitors and demonstrates the capability of combining electrochemical noise analysis with DFT to evaluate both inhibition efficiency and film stability. Full article

Accurately estimating the state of health (SOH) of lithium-ion batteries remains challenging for battery management systems. Traditional data-driven methods, such as long short-term memory (LSTM), lack physical interpretability and often fail to generalize across varying operating conditions. To address this, a physics-informed SP-LSTM framework is proposed that integrates the single particle model (SPM) with a bidirectional LSTM network. A hybrid optimization strategy combining particle swarm optimization and the limited-memory Broyden–Fletcher–Goldfarb–Shanno with bounds (L-BFGS-B) is first used to identify key SPM parameters, which are then combined with macro external features (charging time, discharge energy, IC peak) to form a seven-dimensional fusion vector. A dual-stream Bi-LSTM architecture separately models fast-varying macro trends and slow-varying physical parameters, achieving robust SOH mapping. Validated on the NASA PCoE dataset, the proposed SP-LSTM achieves a root mean square error (RMSE) of 0.0136 and a mean absolute error (MAE) of 0.0089 on an independent test set (B0018), outperforming the baseline LSTM by 38.2% in RMSE. Noise robustness tests (0–3% voltage noise) and Sobol global sensitivity analysis further confirm its stability and interpretability. By embedding electrochemical priors into the data-driven pipeline, this work provides a practical physics-data collaborative framework for accurate and trustworthy battery SOH estimation. Full article

The deterioration of mirror coatings in aggressive environments is one of the main causes of staining, which is a manifestation of corrosion. Electrochemical Impedance Spectroscopy (EIS) allows the electrochemical behavior of these coatings to be evaluated. However, an issue with ANNs is that to perform predictions with high accuracy, it is necessary to adjust their parameters using a large amount of samples. Depending on the ANN architecture, the requirement can range from hundreds to thousands of data points. This is a problem in many real cases, since measurements are expensive in terms of time and resources. In this study, we use a Transfer Learning approach. First, we generate and use synthetic data to train a neural network; then, real data are used to fine-tune the model. The results show that the ANN can identify patterns of coating deterioration with high accuracy and provides an effective mechanism for early performance evaluation. A high accuracy of 0.98 is achieved in the advanced stage, which means that the ANN detects severely damaged protective coatings. For the initial stage, an accuracy of 0.71 and a recall of 0.56 are obtained, indicating that the model has significant difficulty detecting initial damage due to very subtle changes, in the low signal versus noise ratio and the behavior of the protective coatings’ properties when they are close to their intact state. Full article

As a promising class of catalysts for enzymatic glucose sensors, Prussian blue analogues (PBAs) exhibit exceptional biomimetic activity. However, their performance is often constrained by poor intrinsic conductivity, which typically limits their sensitivity. To address this limitation, this study presents an effective approach using direct in situ growth of PBAs on the electrode substrates, which enables the effective integration of PBA-based electrochemical systems. A porous Ni framework was first electrodeposited onto a screen-printed gold electrode substrate, followed by the reduction of Pt onto the porous Ni. Subsequently, NiFe PBA was synthesized in situ using the porous Pt/Ni structure as a sacrificial template. Functionalized with glucose oxidase (GO_x), the PBA/Pt/Ni biosensor exhibited excellent performance for glucose detection in buffer solution, with a high sensitivity of 262.6 μA mM⁻¹·cm⁻² and an ultra-low detection limit of 1.45 μM (calculated at a signal-to-noise ratio of 3, S/N = 3). Notably, its sensitivity corresponds to a two-fold enhancement relative to the electrodes modified with commercial Prussian blue using the conventional drop-casting method. Even when tested in human sweat samples, the biosensor achieved a high sensitivity of 236.4 μA mM⁻¹·cm⁻² and a linear detection range of 20–1000 μM, with the broad sensing range fully encompassing the typical physiological concentrations of glucose in human sweat. This excellent performance arises from the high specific surface area of the porous Pt/Ni structure and the tight connection between PBA and the sacrificial Ni anode. This research presents a promising design strategy for advanced, wearable, and non-invasive health-monitoring platforms. Full article

Biodegradable magnesium implants offer significant clinical promise, but their safe use requires reliable real-time in vivo monitoring of coating integrity. Existing methods lack sufficient sensitivity and temporal resolution to detect degradation at early stages, and there are no computational tools able to predict the success of a given sensor design before animal experiments. In the present paper, we present BioElectroSynth—a digital simulator of an implantable zero-resistance ammetry (ZRA) corrosion sensor in a mouse model. The simulator combines electrochemical noise, cardiac and muscular bioelectric interference, and instrumental limitations into a unified model, enabling virtual experiments, which mimic the complexity of the in vivo system. Using Monte Carlo analysis, we establish that a 2% breach in a chitosan coating on an AZ91 magnesium alloy electrode is statistically detectable from approximately 30 recordings of 30 s each, and quantify how electrode area, its location, sampling rate, and coating quality jointly determine detection sensitivity. The framework provides the first quantitative tool for predicting in vivo experiment feasibility from standard in vitro electrochemical data alone. By identifying instrument and design configurations that are statistically underpowered before any animal use, the approach directly supports the 3R principles of humane research. Full article

The global transition toward carbon-neutral energy solutions has established Solid Oxide Fuel Cells (SOFCs) as a key technology for next-generation power generation. This work presents a comprehensive numerical study and multi-factor statistical analysis of an anode-supported SOFC fueled by synthetic diesel. A three-dimensional computational fluid dynamics model, validated against experimental data, was integrated with a Taguchi L₂₇ orthogonal array to systematically evaluate the influence of six key parameters: temperature, fuel mass flow rate, operating pressure, current load, flow channel configuration, and methane molar fraction. Statistical analysis through the signal-to-noise ratio and analysis of variance identified the operating current as the most significant factor affecting cell voltage, followed by the fuel mass flow rate and temperature. The experiments showed that the highest levels of all factors (except for the current, which had the lowest level) maximize electrochemical performance while maintaining a steam-to-carbon ratio (S/C) within a range of 0.83 to 0.92, calculated based on total carbon content, ensuring sufficient humidification for internal reforming across all tested fuel compositions. Furthermore, a multiple linear regression model was developed as a computationally efficient surrogate, demonstrating exceptional predictive accuracy with an R² of 0.9954 and a mean relative error of 1.76% across independent validation cases. These results provide a robust methodology for rapid design and sensitivity analysis of internal-reforming SOFCs, offering a precise tool for optimizing fuel utilization in high-temperature electrochemical systems. Full article

To investigate the battery impedance variation after the occurrence of nail penetration, this paper adopts Dynamic Electrochemical Impedance Spectroscopy (DEIS) for real-time monitoring of the impedance changes of lithium-ion batteries during the nail penetration process. A piecewise multi-frequency superimposed sinusoidal excitation is designed, which not only complies with the stability principle of battery testing but also ensures the signal-to-noise ratio of the excitation signal. By injecting the designed excitation signal into the operating battery and combining it with the rapid DEIS generation technology, the acquisition of DEIS data within the target frequency band in a short time is realized. Based on the obtained DEIS data, a fractional-order model is established and fitted for analysis before and after nail penetration. The results show that the steel nail introduces inductive reactance and impedance to the battery. Due to the parallel connection between the steel nail and the internal resistance of the battery, the overall impedance decreases, exhibiting a short-circuit state, and both the real and imaginary parts of the impedance experience an abrupt change at the moment of nail penetration. Considering the characteristic of abrupt impedance change of the battery after nail penetration, a battery nail penetration detection method based on DEIS is proposed. Considering the abrupt change characteristics of battery impedance after nail penetration, this paper proposes a battery nail penetration detection method based on DEIS. This method can effectively solve the problem of low sensitivity of traditional voltage monitoring methods in detecting nail penetration during battery operation. It has higher sensitivity and faster response speed compared with traditional methods, enabling online monitoring of battery states. Additionally, this paper also explores its potential application in real-world vehicles. Full article

Background noise and intensive sample preparation frequently compromise the field screening of Bacillus anthracis. Addressing these analytical bottlenecks, we constructed a giant magnetoresistive (GMR) biosensor incorporating geometrically tailored trapezoidal magnetic flux concentrators (MFCs). 3D finite element magnetic simulations directed the MFC topology to mitigate edge saturation, reconciling central magnetic gain with spatial uniformity. The resulting platform demonstrated a 100-fold sensitivity improvement over recent electrochemical methods, achieving a limit of detection (LOD) of 10 CFU/mL in standard buffers, with the entire testing process completed within 40 min. Direct target quantification remained viable in heterogeneous matrices—muddy water, whole milk, and apple cider—circumventing tedious pretreatment. This geometric and magnetic optimization yields a pragmatic sensing architecture tailored for on-site biodefense monitoring. Full article