Search Results (272)

The coronavirus disease 2019 (COVID-19) pandemic highlighted the need for rapid, accurate, and point-of-care diagnostic technologies, accelerating interest in biosensors as next-generation analytical platforms. However, biosensor performance is governed by a connected sequence of processes, including bioprobe–target recognition, sensor fabrication, structural optimization, and signal interpretation. Because these processes involve multiple interacting variables, conventional empirical approaches often have limitations in efficiently optimizing biosensor performance and interpreting complex analytical signals. Artificial intelligence (AI) and machine learning (ML) provide tools to model these relationships and support prediction-guided biosensor development. This review discusses recent progress in AI-assisted biosensor development in three sequential stages. First, AI-assisted bioprobe design is reviewed, including in silico aptamer discovery, smart-SELEX-based aptamer screening, and peptide receptor design for improving molecular recognition. Second, AI-driven sensor fabrication and structural optimization are discussed, focusing on electrochemical feature extraction, paper-based microfluidic device optimization, and optical biosensor parameter prediction. Third, ML-based signal analysis is examined as a strategy for converting complex electrochemical, colorimetric, and optical responses into quantitative analytical outputs. By organizing these examples as a connected workflow rather than as separate applications, this review highlights how AI can link molecular design, device engineering, and signal interpretation to accelerate the development of next-generation biosensors. Full article

Nitric oxide (NO) is a small, highly reactive gaseous signaling molecule that plays diverse and context-dependent roles in microbial physiology and host–microbe interactions. Over the past decade, increasing evidence has revealed the dual nature of NO as both an antimicrobial effector and a signaling mediator involved in microbial stress responses, metabolism, biofilm dynamics, quorum sensing, virulence regulation, and symbiotic interactions. In microbial systems, NO influences adaptation to environmental stress and contributes to mechanisms associated with persistence and antimicrobial resistance. In host organisms, NO functions as a key component of innate immunity while also participating in beneficial interactions involving rhizobia, mycorrhizal fungi, and probiotic microorganisms. Despite its biological significance, accurate detection and quantification of NO remain challenging because of its transient nature, high reactivity, low physiological concentrations, and interference from related reactive oxygen and nitrogen species. Recent advances in biosensing technologies have substantially improved NO detection capabilities through the development of electrochemical, optical, enzyme-based, microfluidic, wearable, and implantable sensing platforms. These innovations are complemented by analytical techniques including electron paramagnetic resonance spectroscopy, mass spectrometry, fluorescence-based imaging, and advanced microscopy, which enhance sensitivity, specificity, and spatiotemporal resolution in complex biological environments. Concurrently, statistical and computational approaches—including sensor calibration models, multivariate analyses, machine learning algorithms, and bioinformatics pipelines—have become increasingly important for extracting biologically meaningful information from NO-related datasets. Unlike previous reviews that primarily focus on either NO biology or sensing technologies, this review integrates current knowledge of NO-mediated microbial physiology and host–microbe interactions with recent developments in biosensor engineering, analytical methodologies, statistical frameworks, and emerging artificial intelligence (AI)-driven data interpretation. We further highlight applications of NO detection in infectious disease diagnostics, antimicrobial screening, probiotic and biofertilizer evaluation, environmental microbiome monitoring, and real-time studies of symbiosis and infection. Finally, future directions including miniaturized sensing platforms, multi-omics integration, AI-assisted analytics, and sensor standardization are discussed. By unifying molecular, analytical, and computational perspectives, this review provides a multidisciplinary framework and roadmap for advancing NO-based research and translational applications across microbial, environmental, and host-associated systems. Full article

Vanadium redox flow batteries (VRBs) are a promising technology for large-scale energy storage because of their high safety, long cycle life, and flexible capacity design. However, pump malfunctions during operation may disturb electrolyte flow distribution, induce electrochemical instability, and, under severe conditions, accelerate stack degradation, thereby reducing system safety and operational reliability. Restricted by factors including the nonlinear coupling between sensor signals and operating conditions, as well as the intricate electrochemical processes triggered by pump faults, effective fault diagnosis for VRB pumps remains a prominent challenge. The paper proposes a novel Temporal Convolutional Network (TCN)–Long Short-Term Memory (LSTM)–Multi-Head Self-Attention (MATT) deep learning framework for intelligent pump fault diagnosis. The framework operates through three complementary stages. Comprehensive experimental validation is conducted using a purpose-built VRB fault experimental platform under various current conditions. The results show that the proposed model achieves diagnostic accuracies exceeding 90% for all three investigated pump fault types, namely bilateral pump fault, positive pump fault, and negative pump fault. Comparative analysis confirms that the proposed model significantly outperforms other architectures. The effectiveness of the MATT in enhancing temporal feature extraction and fault diagnosis accuracy for VRB systems is validated. Full article

Calibration-free electrochemical sensing is increasingly promoted as a route to simpler, more deployable analytical devices. However, the term is used inconsistently, ranging from genuinely absolute measurement to factory-calibrated, ratiometric, self-referenced, drift-corrected or model-assisted operation. This review critically examines what calibration-free sensing can and cannot mean in electrochemical analysis. We argue that a strict claim requires that the reported measurand be obtained from an internally constrained physical, chemical or stoichiometric relationship, with the required parameters known, controlled or independently measured within an uncertainty framework. Potentiometric, amperometric, coulometric, impedimetric, biosensing and affinity-based approaches are compared to show where empirical calibration is removed and where it is shifted to fabrication, internal correction, model fitting, matrix correction or context-specific validation. Particular attention is given to coulometric and thin-layer systems, geometry-constrained devices, electrochemical aptamer-based sensors and self-diagnostic platforms. We propose a classification scheme, a decision tree and a minimum assumption map linking measurand definition, electrochemical signals, signal-to-result relationships, parameter sources, uncertainty, matrix transfer, reproducibility and independent-method agreement. The review provides a practical framework for distinguishing genuinely calibration-free measurements from calibration-reducing, conditionally calibration-free and drift-corrected strategies. Full article

Food contamination by Aflatoxin M1 and Atrazine remains a critical food-safety concern, requiring sensitive detection methods that can operate reliably in complex matrices. Here, we report an AI-assisted antibody-functionalized electrochemical sensing platform for the detection and classification of Aflatoxin M1 and Atrazine across corn, corn flour, and protein matrices. The sensor used analyte-specific antibodies immobilized on an electrochemical electrode surface, where target binding produced measurable changes in the interfacial electrochemical response. Sensor performance was evaluated using cyclic voltammetry, coulometry, and electrochemical impedance spectroscopy (EIS), with EIS providing strong frequency-dependent signatures for concentration-dependent analysis. Spike-and-recovery studies further demonstrated the applicability of the platform in food-matrix conditions. To improve interpretation of complex electrochemical signals, full-spectrum EIS features were integrated with machine learning models for concentration-level classification into low, mid, and high groups. The AI workflow achieved an overall classification accuracy of 93.33%, with 96.67% specificity, 93.44% PPV, 96.66% NPV, and 0.982 AUC for Atrazine, and 96.70% specificity, 93.38% PPV, 96.67% NPV, and 0.987 AUC for Aflatoxin M1. In addition, analyte classification between Aflatoxin M1 and Atrazine reached 97.4% accuracy and 0.994 ROC-AUC. Overall, this work demonstrates a matrix-aware electrochemical immunosensing strategy enhanced by AI-based signal interpretation for food contaminant detection. Full article

Organic coatings are the most widely utilized corrosion protection strategy for metallic materials. Nevertheless, they can degrade over time through the effects of UV, moisture, and corrosive media, compromising their protective performance. In order to monitor the coating performance for predictive maintenance, an electrochemical sensor was fabricated using 6005A aluminum alloy and coated with four coating systems: (1) epoxy primer, (2) epoxy primer/polyurethane topcoat, (3) epoxy primer/polyurethane topcoat/aluminum-powder-containing polyester resin, and (4) epoxy primer/polyurethane topcoat/aluminum-powder-containing polyester resin/acrylic coat. The sensors and corresponding coupon samples were exposed for 24 months at two sites in Thailand: Pathum Thani (PTI, suburban) and Chon Buri (CBI, mild marine). Electrochemical impedance spectroscopy (EIS) measurements were conducted at a fixed frequency of 117 Hz, synchronized with on-site meteorological monitoring. Impedance data were converted into a coating aging index (AI) to quantitatively assess the coating degradation. Coating deterioration was observed in PTI as early as at 6 months of exposure. Machine learning modeling revealed that cumulative rainfall was the dominant environmental factor influencing coating degradation. The single epoxy primer layer exhibited the poorest durability, while the incorporation of polyurethane, aluminum-pigmented polyester, and acrylic layers significantly prolonged the protective service life of the coating system. Full article

This study focuses on a group of scanning electrochemical probe microscopies used to reveal the early stages of galvanic coupling corrosion reactions, based on the use of microelectrochemical sensors for measuring local potentials and currents associated with chemical reactions occurring at anodic and cathodic sites, and their correlation with results obtained with conventional electrochemical techniques. Although galvanic corrosion between dissimilar metals is generally analyzed by assuming that the anodic and cathodic half-cell processes occur in different metals, the use of microelectrochemical techniques reveals that the corrosion process is actually more heterogeneous. Cathodic activity is present in both metals, but to very different degrees. Anodic activity is also localized, as the surface of the more reactive metal is not fully available to undergo anodic dissolution. Because galvanic corrosion processes are heterogeneously distributed over the surface of the coupled materials, even in model systems, the identification of cathodic sites and reactions is often insufficient when monitored by conventional electrochemical methods. These observations are particularly relevant when corrosion protection measures aim to minimize or eliminate the activity of cathodic reaction sites. Full article

Brain-on-a-chip (BOC) refers to a miniaturized in vitro platform that integrates living neuronal networks on a micro-engineered chip, enabling the simulation of brain functions, neural activities and physiological responses. BOC technology is an advanced evolution of microphysiological systems (MPS) and Lab-on-a-Chip platforms, providing novel paradigms for in vitro modeling and exploring early-stage biocomputing by interfacing living neural networks with engineered electronics. Microelectrode arrays (MEAs) serve as the critical physical interface for bidirectional communication in these systems. In this review, we systematically examine the technological landscape and engineering requirements of MEAs tailored for BOC applications, evaluating them across electrical characteristics, structural properties, and biocompatibility. Two primary classes of current MEA technologies, including planar arrays for 2D neural cultures and 3D flexible arrays for brain organoids, are discussed in detail. We highlight the transition from passive planar electrodes to high-density active CMOS and TFT-based arrays, and detail how 3D flexible MEAs utilize endogenous integration and exogenous wrapping strategies to overcome tissue-mechanics mismatches. Furthermore, the integration of MEAs with microfluidics, optoelectronics, and electrochemical sensors to enable multimodal monitoring is explored. With the advantages of the various MEAs, the application of MEAs for BOC, particularly in biological computing and network plasticity research, is discussed. Finally, future technological developments in scalability bottlenecks, chronic stability, and the incorporation of artificial intelligence for MEAs of BOC are prospected. Full article

Electrochemical sensing provides an alternative approach for the trace detection of bioactive substances in fruits. However, the complex matrix in fruit tissues, the coexistence of multiple active components, and the varied pH environments limit the sensing performance and accurate quantitative detection of conventional electrochemical sensors. Herein, a dual-mode electrochemical sensor based on a Co₃O₄@N-MWCNTs modified glassy carbon electrode was developed for the sequential detection of quercetin, rutin, and glucose in fruits under acidic and alkaline conditions. The as-prepared electrode exhibited improved charge transfer efficiency and favorable electrocatalytic activity toward the three target analytes. Under optimal conditions, the sensor displayed wide linear ranges of 0.5~70 μM for quercetin and 0.5~5 μM for rutin in acidic environment, with low detection limits of 0.124 μM and 0.045 μM, respectively. In alkaline environment, the detection limit for glucose was determined to be 8.86 μM. Moreover, four combined machine learning models with feature selection algorithms were established, among which the CARS-RFE+RFR model achieved the best prediction accuracy and robustness for multicomponent quantification. Furthermore, the proposed sensing system was applied to the rapid determination of quercetin, rutin, and glucose in real litchi samples, with recoveries ranging from 98.4% to 105.4%. This study provides a feasible electrochemical strategy for multicomponent detection in complex plant matrices, showing good applicability for rapid on-site analysis in agricultural and food-related applications. Full article

Micro- and nanoplastic (MNP) pollution has emerged as a global environmental and health concern, driving the rapid development of sensor technologies for faster, more sensitive, and field-deployable detection. This review synthesizes recent advances in optical, electrochemical, and biosensor platforms for MNP analysis and compares their analytical performance and practical feasibility. Optical sensors, including plasmonic, spectroscopic, and colorimetric systems, enable label-free and often rapid detection with material discrimination capability, and are well-suited for screening applications, though they commonly exhibit higher detection limits and matrix interference. Electrochemical sensors demonstrate the highest analytical sensitivity overall, frequently reaching low µg L⁻¹ to ng mL⁻¹ levels, with strong potential for miniaturization and on-site deployment; performance is further enhanced by nanostructured electrodes, photoelectrochemical designs, and signal amplification strategies. Biosensors incorporating peptides, aptamers, enzymes, or engineered proteins provide improved polymer selectivity and enable targeted detection, but face challenges related to stability, cross-reactivity, and reproducibility in complex samples. Practically, portable electrochemical and simple optical colorimetric platforms are currently the most feasible for field use, while hybrid bio-electrochemical systems show the highest performance potential. Future research should prioritize robust selective recognition elements, antifouling interfaces, standardized validation protocols, mixed-polymer quantification models, and integration with machine learning to enable reliable, real-world MNP monitoring. Full article

Electrical monitoring of brain activity can be performed discreetly and continuously over long periods of time using intra-auricular electroencephalography (intra-auricular EEG), a promising technique suitable for subjects who are difficult to monitor, such as newborns or patients with neurological conditions requiring discreet but long-term neurophysiological assessment. The concept of intra-aural EEG can be realized through the development of systems that include wearable sensors, whose performance critically depends on the development of biocompatible electrode materials that exhibit low impedance and can maintain and provide stable contact between the electrode and the epithelial tissue. Based on our previous work on carbon nanotube (CNT)-based hydrogel composites for intra-aural EEG electrodes, this study focuses on the electrochemical characterization of hydrogels initially prepared from gelatin methacrylate (GelMA)/2-hydroxyethyl methacrylate (HEMA) doped with varying concentrations of CNTs (0–3 wt%). In the present study, the materials obtained in the first stage were evaluated using electrochemical impedance spectroscopy (EIS) under both liquid and dry conditions, supplemented by measurements of hydration capacity. The results show that the composite with 3% CNT content exhibits suitable properties, making the material making the 3 wt% CNT formulation a promising platform for the further development of 3D-printable hydrogel electrodes for intra-aural EEG applications. Equivalent circuit modeling reveals improved ionic and electronic conductivity compared to the undoped hydrogel, attributed to better CNT dispersion and polymer crosslinking. This work provides insights into the structure–property relationships of CNT–hydrogel composites and lays the foundation for the further development of a 3D-printed and in vitro/in vivo validated prototype of intra-aural EEG sensors. Full article

Robotic tactile interfaces involving artificial skins often experience sliding contact conditions. At sliding interfaces, frictional loading, tangential stress, and impending slip dominate sensing behavior. This work demonstrates three-dimensional finite element (3D-FE) and Poisson–Nernst–Planck (PNP) modeling of layered ionic polymer–metal composite (IPMC) artificial skin under finger-like reciprocating sliding contact. The layered structure consists of a Nafion-based IPMC core sandwiched between thin upper and lower electrodes. A rigid acrylic slider is used to simulate reciprocating finger motion relative to the surface of the IPMC skin. A time-dependent contact mechanics model is first utilized to simulate temporal variations in normal and tangential contact fields for various coefficients of friction. Electrochemical response is then determined in COMSOL Multiphysics by coupling ion transport and electrostatics in a PNP framework to predict the output sliding current. Parametric studies are used to investigate the dependence of sensor response on the coefficient of friction, reciprocating history, layer geometry, and transport parameters. From the results, it can be noted that the resulting parameter offers a robust and physically meaningful description of the magnitude of contact-induced shear stress under multi-mode loadings, yet retaining the capability of responding to the presence of friction-induced mechanical excitation. The current model is aimed at dynamic shear sensitivity detection in sliding contacts. It is not designed for texture discrimination or fragment identification tasks. Thus, the current study demonstrates an important coupling parameter for 3D IPMC sensor models under contact and sets up a framework for enhanced electro-chemo-mechanical modeling of soft ionic tactile sensors. 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

Portable non-enzymatic electrochemical glucose sensors offer potential for decentralized healthcare and medical education; however, their integration into simulation-based teleconsultation training workflows remains limited. This study presents the functional integration of a portable copper-modified electrochemical glucose sensor into a web- and Android-based telemedicine platform within a simulation-based medical education framework. Screen-printed carbon electrodes were electrochemically activated and modified via copper electrodeposition. Surface and electrochemical characterization were performed using SEM-EDX and cyclic voltammetry, respectively, followed by chronoamperometry for quantitative detection. Glucose solutions in PBS (pH 10) were measured using 70 µL samples, and the resulting signals were converted into glucose values (mg/dL) through a calibration model and incorporated into simulated gynecological teleconsultation workflows. The sensor exhibited a stable amperometric response at +0.60 V, with a linear range of 3.125–50 mM (R² = 0.9822), an area-normalized sensitivity of 0.061 µA·mM⁻¹·cm⁻², and a limit of detection of 1.39 mM. Implementation within the simulation scenario (n = 26) demonstrated 69% high/very high perceived usability and 88% high/very high educational value. These results support the feasibility of functionally integrating portable electrochemical sensing into simulation-based teleconsultation training and provide a proof-of-concept framework for future technical refinement and broader educational validation. Full article

Accurate color classification plays a critical role across diverse fields, from textile manufacturing and environmental monitoring to biomedical diagnostics. This study introduces a machine-learning-driven approach to spectral color sensing using SENSIPATCH, a compact, wearable sensor system; while SENSIPATCH integrates multiple sensing modalities, including bioimpedance, electrochemical, thermal, humidity, and vibrational sensors, this work specifically utilizes its spectrometer module, which comprises multi-wavelength LEDs and photodiodes. Targeting the classification of 100 standardized PANTONE colors, the proposed framework is evaluated under controlled lighting conditions to ensure repeatable spectral acquisition. The experimental design includes both firm and loose contact scenarios to emulate variability in wearable placement. A structured data-preprocessing pipeline involving baseline correction, bootstrapping, and Z-score normalization was employed to enhance signal quality and improve model generalization. Five machine learning models were evaluated: Random Forest, SVM, MLP, CNN, and LSTM. The MLP demonstrated the strongest classification performance. Notably, the MLP achieved consistent accuracy across both contact conditions, indicating robustness against sensor placement variations. These results highlight the feasibility of compact LED-based wearable spectroscopy for reliable color classification under controlled measurement conditions, providing a baseline for future extensions to more diverse lighting conditions. Full article