Search Results (420)

Anaerobic digestion (AD) is increasingly recognized as a key technology for renewable energy generation and sustainable waste management within the circular economy. However, its performance is highly sensitive to feedstock variability and environmental fluctuations, making stable operation and high methane yields difficult to sustain. Conventional monitoring and control systems, based on limited sensors and mechanistic models, often fail to anticipate disturbances or optimize process performance. This review discusses recent progress in electrochemical, optical, spectroscopic, microbial, and hybrid sensors, highlighting their advantages and limitations in artificial intelligence (AI)-assisted monitoring. The role of soft sensors, data preprocessing, feature engineering, and explainable AI is emphasized to enable predictive and adaptive process control. Various machine learning (ML) techniques, including neural networks, support vector machines, ensemble methods, and hybrid gray-box models, are evaluated for yield forecasting, anomaly detection, and operational optimization. Persistent challenges include sensor fouling, calibration drift, and the lack of standardized open datasets. Emerging strategies such as digital twins, data augmentation, and automated optimization frameworks are proposed to address these issues. Future progress will rely on more robust sensors, shared datasets, and interpretable AI tools to achieve predictive, transparent, and efficient biogas production supporting the energy transition. Full article

Corrosion is a complex, surface-initiated process that demands nanoscale, real-time characterization to understand its initiation and propagation. Atomic force microscopy (AFM) and scanning Kelvin probe force microscopy (SKPFM) have emerged as powerful tools in corrosion science, enabling high-resolution imaging and electrochemical mapping under realistic conditions. This review, inspired by pioneering work at KTH by Professors Christofer Leygraf and Jinshan Pan, highlights advanced analytical strategies that extend the capabilities of AFM and SKPFM beyond traditional line-profile analysis. Techniques such as power spectral density (PSD) analysis, multimodal Gaussian histogram fitting, statistical roughness quantification, and deconvolution methods are discussed in the context of case studies on aluminum alloys, stainless steels, magnesium alloys, biomedical implants, and protective coatings. By integrating in situ imaging, electrochemical mapping, and statistical data processing, these approaches provide deeper insights into localized corrosion, micro-galvanic coupling, and surface reactivity. Future directions include coupling AFM-based methods with high-speed imaging, machine learning, and spectro-electrochemical techniques to accelerate the development of corrosion-resistant materials and enable probabilistic diagnostics of corrosion initiation susceptibility. Full article

Accurate state of charge (SoC) estimation is critical for the safety, performance, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. This study investigates the application of Electrochemical Impedance Spectroscopy (EIS) data in conjunction with tree-based ensemble machine learning algorithms—Random Forest, Extra Trees, Gradient Boosting, XGBoost, and AdaBoost—for precise SoC prediction. A real dataset comprising multi-frequency EIS measurements was used to train and evaluate the models. The models’ performances were assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that Extra Trees achieved the best accuracy (MSE = 1.76, RMSE = 1.33, R² = 0.9977), followed closely by Random Forest, Gradient Boosting, and XGBoost, all maintaining RMSE values below 1.6% SoC. Predictions from these models closely matched the ideal 1:1 relationship, with tightly clustered error distributions indicating minimal bias. AdaBoost returned a higher RMSE (3.06% SoC) and a broader error spread. These findings demonstrate that tree-based ensemble models, particularly Extra Trees and Random Forest, offer robust, high-accuracy solutions for EIS-based SoC estimation, making them promising candidates for integration into advanced battery management systems. Full article

Impedimetric biosensors have emerged as a versatile class of electrochemical devices, enabling highly sensitive and real-time detection of diverse analytes. Their applications extend across healthcare diagnostics, environmental monitoring, food safety, and agriculture. By virtue of their compact size, high sensitivity, selectivity, portability, and ease of operation, these sensors have advanced rapidly in both research and practical applications. This review consolidates the wide spectrum of current applications and technological advances reported in the literature. Additionally, it examines the prospects of integrating impedimetric biosensors with emerging technology fields, including artificial intelligence, machine learning, and flexible and wearable devices. By providing an overview of the different categories of impedimetric biosensors, their detection strategies, sensing modalities, and applications, this review presents a comprehensive perspective on the current progress and future opportunities in impedimetric biosensing. Full article

The multi-layer workpiece and multi-wire electrochemical microfabrication method (MWECM) has considerable potential in improving production efficiency and is considered a promising technology for manufacturing high-quality array microstructures. However, due to the accumulation of electrolytic by-products between workpiece layers, the machining accuracy is relatively low, which still limits its application in industrial environments. To address this issue, this article introduces a method to enhance mass transfer, which involves multi-layer workpieces and multi-wire electrochemical microfabrication, and employs horizontal electrolyte flushing (MWECMF). This innovation promotes the effective discharge of electrolytic deposits, thereby enhancing the renewal of electrolytes within the electrode gap. And use flow field simulation to optimize the interlayer spacing of workpieces and determine the optimal workpiece spacing. In addition, single factor experiments were conducted to determine the optimal processing parameters, including wire feed speed, power supply voltage, frequency, and duty cycle. Finally, at a feed rate of 1.2 µm/s, an array microstructure was successfully fabricated using a two-wire electrode setup and a four-layer workpiece configuration, achieving an overall machining rate of 9.6 µm/s. Compared to traditional tools or workpiece vibration mass transfer, the MWECMF method significantly improves the machining efficiency of wire electrochemical microfabrication (WECM). Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and efficient operation of electric vehicles (EVs). Conventional approaches, including Coulomb counting, electrochemical impedance spectroscopy, and equivalent circuit models, provide useful insights but face practical limitations such as error accumulation, high equipment requirements, and limited applicability across different conditions. These challenges have encouraged the use of machine learning (ML) methods, which can model nonlinear relationships and temporal degradation patterns directly from cycling data. This paper reviews four machine learning algorithms that are widely applied in SOH estimation: support vector regression (SVR), random forest (RF), convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). Their methodologies, advantages, limitations, and recent extensions are discussed with reference to the existing literature. To complement the review, MATLAB-based simulations were carried out using the NASA Prognostics Center of Excellence (PCoE) dataset. Training was performed on three cells (B0006, B0007, B0018), and testing was conducted on an unseen cell (B0005) to evaluate cross-battery generalisation. The results show that the LSTM model achieved the highest accuracy (RMSE = 0.0146, MAE = 0.0118, R² = 0.980), followed by CNN and RF, both of which provided acceptable accuracy with errors below 2% SOH. SVR performed less effectively (RMSE = 0.0457, MAPE = 4.80%), reflecting its difficulty in capturing sequential dependencies. These outcomes are consistent with findings in the literature, indicating that deep learning models are better suited for modelling long-term battery degradation, while ensemble approaches such as RF remain competitive when supported by carefully engineered features. This review also identifies ongoing and future research directions, including the use of optimisation algorithms for hyperparameter tuning, transfer learning for adaptation across battery chemistries, and explainable AI to improve interpretability. Overall, LSTM and hybrid models that combine complementary methods (e.g., CNN-LSTM) show strong potential for deployment in battery management systems, where reliable SOH prediction is important for safety, cost reduction, and extending battery lifetime. Full article

Gas Detection Systems (GDSs) are critical safety technologies deployed in semiconductor wafer fabrication facilities to monitor the presence of hazardous gases. A GDS receives input from gas detectors equipped with consumable gas sensors, such as electrochemical (EC) and metal oxide semiconductor (MOS) types, which are used to detect toxic, flammable, or reactive gases. However, over time, sensors degradations, accuracy drift, and cross-sensitivity to interference gases compromise their intended performance. To maintain sensor accuracy and reliability, routine manual calibration is required—an approach that is resource-intensive, time-consuming, and prone to human error, especially in facilities with extensive networks of gas detectors. This systematic review (PROSPERO on 11th October 2025 Registration number: 1166004) explored minimizing or eliminating the dependency on manual calibration. Findings indicate that using properly calibrated gas sensor data can support advanced data analytics and machine learning algorithms to correct accuracy drift and improve gas selectivity. Techniques such as Principal Component Analysis (PCA), Support Vector Machines (SVMs), multivariate regression, and calibration transfer have been effectively applied to differentiate target gases from interferences and compensate for sensor aging and environmental variability. The paper also explores the emerging potential for integrating calibration-free or self-correcting gas sensor systems into existing GDS infrastructures. Despite significant progress, key research challenges persist. These include understanding the dynamics of sensor response drift due to prolonged gas exposure, synchronizing multi-sensor data collection to minimize time-related drift, and aligning ambient sensor signals with gas analytical references. Future research should prioritize the development of application-specific datasets, adaptive environmental compensation models, and hybrid validation frameworks. These advancements will contribute to the realization of intelligent, autonomous, and data-driven gas detection solutions that are robust, scalable, and well-suited to the operational complexities of modern industrial environments. Full article

Heavy metal ions (HMIs) threaten ecosystems and human health due to their carcinogenicity, bioaccumulativity, and persistence, demanding highly sensitive, low-cost real-time detection. Electrochemical sensing technology has gained significant attention owing to its rapid response, high sensitivity, and low cost. Molybdenum disulfide (MoS₂), with its layered structure, tunable bandgap, and abundant edge active sites, demonstrates significant potential in the electrochemical detection of heavy metals. This review systematically summarizes the crystal structure characteristics of MoS₂, various preparation strategies, and their mechanisms for regulating electrochemical sensing performance. It particularly explores the cooperative effects of MoS₂ composites with other materials, which effectively enhance the sensitivity, selectivity, and detection limits of electrochemical sensors. Although MoS₂-based materials have made significant progress in theoretical and applied research, practical challenges remain, including fabrication process optimization, interference from complex-matrix ions, slow trace-metal enrichment kinetics, and stability issues in flexible devices. Future work should focus on developing efficient, low-cost synthesis methods, enhancing interference resistance through microfluidic and biomimetic recognition technologies, optimizing composite designs, resolving interfacial reaction dynamics via in situ characterization, and establishing structure–property relationship models using machine learning, ultimately promoting practical applications in environmental monitoring, food safety, and biomedical fields. Full article

Fe₄₂Mn₂₈Cr₁₅Co₁₀Si₅ is a highly strain-hardenable high-entropy alloy (HEA) that is challenging to machine with traditional metal cutting tools. The electrochemical behavior of this HEA was examined in nitrate- and chloride-based electrolytes to understand the electrochemical machining (ECM) process. Potentiodynamic and potentiostatic tests were conducted on this alloy in 1 M and 2.35 M NaNO₃ solutions, with and without additions of 0.01 M nitric acid and 0.01 M citric acid. A 20% NaCl solution was also tested as an electrolyte. Nitrate solutions caused passivation of the HEA, while no passivation was observed in chloride solutions. Surface analysis with X-ray photoelectron spectrometry (XPS) indicated that adding citric acid helped reduce surface passivation. The Faradaic efficiency of ECM increased with higher applied voltage. The chloride solution showed higher Faradaic efficiency than nitrate-based solutions. Specifically, the Faradaic efficiency of 20% NaCl at 10 V is 57.4%, compared to 21.9% for 20% NaNO₃ + 0.01 M citric acid at 10 V. Electrochemical parameters, including anodic and cathodic exchange current densities, Tafel slopes, and corrosion current densities, were calculated from the experimental data. The corrosion current densities in the 20% nitrate solutions ranged from 2.35 to 3.2 × 10⁻⁵ A/cm², while the 20% chloride solution had a lower corrosion rate at 1.45 × 10⁻⁵ A/cm². These electrochemical parameters can help predict the dissolution behavior of the HEA in nitrate and chloride solutions and aid in optimizing the ECM process. Full article

Polymer materials have become promising candidates for next-generation energy storage, with structural tunability, multifunctionality, and compatibility with a variety of device platforms. They have a molecular design capable of customizing ion and electron transport routes, integrating redox-active species, and enhancing interfacial stability, surpassing the drawbacks of traditional inorganic systems. New developments have been made in multifunctional polymers that have the ability to combine conductivity, mechanical properties, thermal stability, and self-healing into a single scaffold system, which is useful in battery, supercapacitor, and solid-state applications. By incorporating polymers with carbon nanostructures, ceramics, or two-dimensional materials, hybrid polymer nanocomposites improve electrochemical performance, durability, and mechanical compliance, and the solid polymer electrolytes, as well as artificial solid electrolyte interphases, resolve dendrite growth and safety issues. The multifunctionality also extends to flexibility, stretchability, and miniaturization, which implies that polymers are suitable for use in wearable devices and biomedical devices. At the same time, sustainable polymer innovation focuses on bio-based feedstocks, which can be recycled, and green synthesis pathways. Polymer discovery using artificial intelligence and machine learning is faster than standard methods, predicts structure–property–performance relationships, and can be rationally engineered. Although there are difficulties in stability during long periods, scalability, and trade-offs between indeedness and mechanical endurance, polymers are a promising avenue with regard to dependable, safe, and sustainable power storage. This review presents the molecular strategies, multifunctional uses, and prospects, where polymers are at the center of the next-generation energy technologies. Full article

Traditional mechanical processing techniques are confronted with significant challenges when machining advanced materials possessing excellent mechanical properties. Electrochemical machining (ECM), as a material removal technology based on the principle of anodic dissolution, demonstrates distinctive advantages including the absence of contact stress, independence from material hardness, and elimination of mechanical residual stress and recast layers. These characteristics render ECM particularly suitable for high-precision applications requiring superior surface quality. This review systematically summarizes the applications, recent progress, and current challenges of ECM in surface processing. According to diverse surface requirements, ECM technology is classified into two core directions based on primary objectives. The first direction focuses on surface quality enhancement, where nanoscale planarization, residual stress reduction, and uniform surface performance are achieved through precise regulation of anodic dissolution. The second direction concerns material shaping, which is subdivided into macro-scale and micro-scale processing. Macro-scale forming combines electrochemical dissolution with mechanical action to maintain high material removal rate (MRR) while achieving micron-level precision. Micro-scale forming employs nanosecond pulse power supplies and precision electrode/mask designs to overcome manufacturing limitations of micro-nano features on hard-brittle materials. Despite progress achieved, key technical bottlenecks persist, including unstable dynamic control of the inter-electrode gap, environmental concerns regarding electrolytes, and tooling degradation. Future research should prioritize the development of green processing technologies, intelligent control systems, multi-scale manufacturing strategies, and multi-energy field hybrid technologies to enhance the capability of ECM in meeting increasingly stringent surface requirements for advanced materials. Full article

The turbine mortise is a critical structural feature of turbine disks, and its manufacturing quality directly determines the performance and service life of aircraft engines. With the increasing application of advanced nickel-based superalloys, severe tool wear in conventional mechanical broaching of turbine mortises has emerged as a key limitation, substantially elevating production costs. Electrochemical broaching (ECB), which removes material through anodic dissolution reactions, eliminates tool wear and thus offers low cost and efficiency advantages, making it a promising method for turbine mortise fabrication. In this study, COMSOL Multiphysics 6.2 was employed to simulate the multiphysics field comprising the electric field, flow field, temperature field, bubble ratio, and dynamic mesh and elucidate the evolution of the electric field during the ECB process. ECB experiments of specimens on Inconel 718 were conducted under different feed speeds. On this basis, optimal processing parameters were identified. The results of the mid-position ECB experiments revealed five distinct dissolution states: pre-processing, pre-transition, stable dissolution, post-transition, and post-processing stages. A material dissolution mechanism model for the ECB process was established. Finally, fir-tree turbine mortises were successfully manufactured on Inconel 718 using a self-developed specialized electrochemical machining system at a feed speed of 70 mm/min. The mortise profile demonstrated dimensional deviations of (+16 to −21) μm, with working surface variations maintained within ±5 μm. The machined surfaces exhibited uniform and dense morphology with a surface roughness of Ra 0.275 μm. Three sets of mortise specimens processed under identical parameters showed excellent consistency, presenting a maximum deviation in profile removal thickness of +4.1 μm. The tool cathode was repeatedly reused without any detectable wear. Full article

The accurate prediction of batteries’ state of health has been an important research topic in recent years, given the surge in electric vehicle production. Dynamically assessing the current state of health of a battery can help predict how long the battery will last during the next discharge cycle, which is directly related to an electric vehicle’s autonomy calculations. Data-driven approaches have been successful in accurately estimating the state of health through machine learning-based models. Within this research topic, limited studies have been carried out to explore the transfer learning capabilities of these models to improve performance and reduce computational costs related to training. This paper aims to compare the performance of different machine learning models to adapt to diverse battery working conditions, as well as their transfer learning capabilities to batteries with different electrochemical compositions. A new transformer-based model is proposed for the SOH estimation problem. The results show that the proposed transformer model can improve its prediction performance through transfer learning when compared to the same model trained exclusively on the target dataset. When pre-trained on the NASA dataset and fine-tuned on the Oxford dataset, the transformer achieved an average RMSE of 0.01461, outperforming the best-performing model (an ANN with an RMSE of 0.01747) trained exclusively on the target data by 17%. On top of improving its performance, the model is also able to outperform a competing transformer model from the literature, which reported an RMSE of 0.90170 on a similar cross-composition transfer task. Full article

Foodborne pathogens remain a significant public health concern, necessitating the development of rapid, sensitive, and reliable detection methods for various food matrices. Traditional biosensors, while effective in many contexts, often face limitations related to complex sample environments, signal interpretation, and on-site usability. The integration of artificial intelligence (AI) into biosensing platforms offers a transformative approach to address these challenges. This review critically examines recent advancements in AI-assisted biosensors for detecting foodborne pathogens in various food samples, including meat, dairy products, fresh produce, and ready-to-eat foods. Emphasis is placed on the application of machine learning and deep learning to improve biosensor accuracy, reduce detection time, and automate data interpretation. AI models have demonstrated capabilities in enhancing sensitivity, minimizing false results, and enabling real-time, on-site analysis through innovative interfaces. Additionally, the review highlights the types of biosensing mechanisms employed, such as electrochemical, optical, and piezoelectric, and how AI optimizes their performance. While these developments show promising outcomes, challenges remain in terms of data quality, algorithm transparency, and regulatory acceptance. The future integration of standardized datasets, explainable AI models, and robust validation protocols will be essential to fully harness the potential of AI-enhanced biosensors for next-generation food safety monitoring. Full article

Aflatoxin (AF) contamination in pistachios remains a critical food safety and trade challenge, given the potent carcinogenicity of AF-B₁ and the nut’s high susceptibility to Aspergillus infection throughout production and storage. Traditional decontamination methods such as roasting, irradiation, ozonation, and acid/alkaline treatments can reduce AF levels but often degrade sensory and nutritional quality, implying the need for more sustainable approaches. In recent years, innovative nonthermal interventions, including pulsed light, cold plasma, nanomaterial-based adsorbents, and bioactive coatings, have demonstrated significant potential to decrease fungal growth and AF accumulation while preserving product quality. Biosensing technologies such as electrochemical immunosensors, aptamer-based systems, and optical or imaging tools are advancing rapid, portable, and sensitive detection capabilities. Combining these experimental strategies with artificial intelligence (AI) and machine learning (ML) models can increasingly be applied to integrate spectral, sensor, and imaging data for predicting fungal development and AF risk in real time. This review brings together progress in nonthermal reduction strategies, biosensing innovations, and data-driven approaches, presenting a comprehensive perspective on emerging tools that could transform pistachio safety management and strengthen compliance with global regulatory standards. Full article