Biosensors

23 pages, 26296 KB

Open AccessArticle

Electric-Field-Assisted Co-Deposition of Bacteriorhodopsin and PEDOT:PSS on Interdigitated Electrodes for Biohybrid Photodetectors

by Abraham Ruiz Gómez, Juan Carlos Ferrer Millán, José Luis Alonso Serrano, Alba Hortal Foronda and Susana Fernández de Ávila López

Biosensors 2026, 16(7), 398; https://doi.org/10.3390/bios16070398 - 22 Jul 2026

Viewed by 386

Abstract

This work presents the fabrication and characterization of biohybrid optoelectronic devices based on the integration of bacteriorhodopsin (bR) and PEDOT:PSS on interdigitated electrodes (IDEs). A three-phase methodology was developed to systematically optimize the active layer. First, the effect of an electric field applied [...] Read more.

This work presents the fabrication and characterization of biohybrid optoelectronic devices based on the integration of bacteriorhodopsin (bR) and PEDOT:PSS on interdigitated electrodes (IDEs). A three-phase methodology was developed to systematically optimize the active layer. First, the effect of an electric field applied during PEDOT:PSS drying was investigated, identifying a drying voltage of 1.2 V as the optimum among the tested conditions, achieving a maximum responsivity of

(35.65 \pm 1.36)

mA/W, a minimum noise equivalent power of

(5.99 \pm 0.13) \times 10^{- 10}

W·H

z^{- 1 / 2}

, and a maximum specific detectivity of

(3.55 \pm 0.07) \times 10^{8}

cm H

z^{1 / 2}

W^{- 1}

. Second, the compatibility of a physiological buffer for bR stabilization was assessed, demonstrating that a 60% PEDOT:PSS/40% buffer composition preserved and further improved the optoelectronic performance of the polymer matrix. Finally, bacteriorhodopsin was incorporated into the optimized PEDOT:PSS/buffer formulation, yielding the hybrid bR_1.2V_60% device, which exhibited an on/off ratio of 2.45 at 0 V and a maximum responsivity of

(5.63 \pm 0.76)

mA/W under reverse bias. Morphological analysis suggested improved film homogeneity, together with a continuous polymer matrix containing dispersed crystalline buffer-salt inclusions. Dynamic measurements showed a stable and reversible short-term photoresponse over successive illumination cycles (

Δ I_{ON} = 18.54 \pm 0.17

μ

A) with full baseline recovery. These results demonstrate a scalable strategy for the development of biohybrid photodetectors with potential for low-power and sustainable optoelectronic applications. Full article

(This article belongs to the Section Optical and Photonic Biosensors)

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12 pages, 5803 KB

Open AccessArticle

Design of a Metasurface-Enhanced Mid-Infrared Biosensor for Fingerprint Signal Enhancement of Staphylococcus aureus Biofilms

by Bowei Yang, Ang Zhou, Yuxiang Yang, Yu Zhao and Chunying Pang

Biosensors 2026, 16(7), 397; https://doi.org/10.3390/bios16070397 - 22 Jul 2026

Viewed by 296

Abstract

Mid-infrared spectroscopy provides molecular fingerprint information for bacterial biofilm analysis, but the absorption signal of a thin biofilm layer is usually weak. In this work, a metasurface-enhanced mid-infrared biosensor was designed to enhance the fingerprint response of Staphylococcus aureus biofilms. The biofilm transmission [...] Read more.

Mid-infrared spectroscopy provides molecular fingerprint information for bacterial biofilm analysis, but the absorption signal of a thin biofilm layer is usually weak. In this work, a metasurface-enhanced mid-infrared biosensor was designed to enhance the fingerprint response of Staphylococcus aureus biofilms. The biofilm transmission spectrum was measured by Fourier-transform infrared spectroscopy, and the film thickness was obtained by atomic force microscopy using an edge step-height method. Based on these measurements, an effective extinction coefficient was extracted and used in finite-difference time-domain simulations. A metal–insulator–metal metasurface was then optimized to cover the main biofilm absorption bands in the mid-infrared region. Two resonator designs were studied: a polarization-dependent structure and a polarization-insensitive structure. The polarization-dependent design showed a strong response under x-polarized incidence and weak coupling under y-polarized incidence. The polarization-insensitive design provided a more balanced response for orthogonal polarizations. At the selected biofilm fingerprint wavelengths, the highest enhancement factors reached 8.57 and 7.24 for the polarization-dependent and polarization-insensitive structures, respectively. Near-field distributions confirmed that the enhancement mainly originated from localized electric fields at the metal resonator edges. These results provide a proof-of-concept design strategy for enhancing weak mid-infrared fingerprint signals from S. aureus biofilms. Full article

(This article belongs to the Special Issue Advanced Plasmonics and Surface-Enhanced Technologies in Biosensing Applications)

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30 pages, 16795 KB

Open AccessReview

A Review of SERS-Based Bacterial Detection from Nanomaterials to Integrated Clinical Platforms

by Yueqi Yang, Jing Li, Xinyi Hu, Zong Dai and Jianhe Guo

Biosensors 2026, 16(7), 396; https://doi.org/10.3390/bios16070396 - 21 Jul 2026

Viewed by 352

Abstract

Pathogenic bacterial infections remain a persistent global public health crisis. However, traditional clinical detection methods—such as culture-based assays and polymerase chain reaction (PCR)—are often time-consuming and labor-intensive, and they lack sufficient sensitivity for low-abundance pathogens, hindering rapid point-of-care diagnosis. With label-free, ultra-sensitive molecular [...] Read more.

Pathogenic bacterial infections remain a persistent global public health crisis. However, traditional clinical detection methods—such as culture-based assays and polymerase chain reaction (PCR)—are often time-consuming and labor-intensive, and they lack sufficient sensitivity for low-abundance pathogens, hindering rapid point-of-care diagnosis. With label-free, ultra-sensitive molecular fingerprinting, Surface-Enhanced Raman Scattering (SERS) has emerged as a powerful tool for rapid pathogen identification. This review summarizes the evolution of SERS-based bacterial detection from fundamental nanomaterials to integrated clinical diagnostic platforms. The article explores four core dimensions: functional integration and enrichment strategies of colloidal probes; structural design and multifaceted capture mechanisms of solid substrates; synergistic advantages of microfluidic systems in enabling automated “sample-to-answer” architectures; and the translational potential of SERS-based lateral flow assays (LFAs) for robust point-of-care testing (POCT). This review reveals a research shift from maximizing electromagnetic enhancement toward overcoming matrix effects in clinical samples and ensuring robustness. In synergy with microfluidics, LFAs, and AI, SERS technology is bridging the bench-to-bedside gap, offering a roadmap for next-generation decentralized, high-precision diagnostics. Full article

(This article belongs to the Special Issue Advanced SERS-Based Biosensors for Rapid and Sensitive Detection)

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45 pages, 16414 KB

Open AccessReview

Nano-Carbon Biointerfaces in Biosensors for Cancer: A Scoping Review Mapping the Transition from Proof-of-Concept to Translational Applicability (2024–2026)

by Barbara R. Geraldino, Nilséia A. Barbosa, Priscila M. Galdino, Eduardo X. F. G. Migon, Danielle Godoy, Tatiana Cunha and Fernando M. Araújo-Moreira

Biosensors 2026, 16(7), 395; https://doi.org/10.3390/bios16070395 - 21 Jul 2026

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Abstract

Nano-carbon biointerfaces offer versatile platforms for cancer biomarker detection, but their progression from analytical proof-of-concept to clinically usable diagnostic evidence remains uneven. This scoping review maps 191 primary studies published from 2024 to 2026, covering nano-carbon families, surface chemistries, transduction architectures, biological matrices, [...] Read more.

Nano-carbon biointerfaces offer versatile platforms for cancer biomarker detection, but their progression from analytical proof-of-concept to clinically usable diagnostic evidence remains uneven. This scoping review maps 191 primary studies published from 2024 to 2026, covering nano-carbon families, surface chemistries, transduction architectures, biological matrices, and translational endpoints in cancer biosensing. The evidence space spans four nano-carbon dimensional classes: zero-dimensional carbon dots and quantum dots, one-dimensional carbon nanotubes, two-dimensional graphene-derived materials, and three-dimensional hybrid composites. Across these platforms, analytical sensitivity did not scale monotonically with nano-carbon dimensionality; instead, performance was shaped by the interaction between material architecture, biointerface chemistry, recognition strategy, transduction modality, and matrix context. A Translational Readiness Matrix showed that approximately 67% of studies remained at Low Evidence Level, whereas only approximately 10% reached Strong Evidence Level. Five recurring bottlenecks constrained translation: incomplete reproducibility reporting, limited Real-matrix Validation, scarce comparator-based clinical evidence, insufficient manufacturing-scale data, and weak regulatory or deployment planning. To address these gaps, this review proposes a nine-item minimum reporting checklist and a four-stage validation roadmap to support more reproducible, comparable, and clinically oriented nano-carbon biosensor development. Full article

(This article belongs to the Special Issue Nano-Carbons in Biosensors)

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45 pages, 8462 KB

Open AccessArticle

Hybrid Edge–Cloud Asymmetric Analytics for Portable Multimodal BCI Biosensors

by Sayantan Ghosh, Padmanabhan Sindhujaa, Pradakshana Senthil Kumar, Anand Mohan, Pachaiyappan Mahalakshmi, Balázs Gulyás, Domokos Máthé and Parasuraman Padmanabhan

Biosensors 2026, 16(7), 394; https://doi.org/10.3390/bios16070394 - 21 Jul 2026

Viewed by 566

Abstract

Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of [...] Read more.

Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge–cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k = 5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation. Full article

(This article belongs to the Special Issue Artificial Skins and Wearable Biosensors for Healthcare Monitoring—2nd Edition)

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13 pages, 1524 KB

Open AccessArticle

Sample-to-Answer Point-of-Care Blood Lead Level Test

by Rachel L. Warren, Alexander R. Pueschel, Wei W. Yu and Ian M. White

Biosensors 2026, 16(7), 393; https://doi.org/10.3390/bios16070393 - 21 Jul 2026

Viewed by 358

Abstract

Children who are exposed to lead may have extensive health problems, in particular intelligence deficits and developmental delays. Wide-reaching screening programs are essential to identify children in need of remediation and medical intervention. Lead exposure is most problematic in low- and middle-income countries, [...] Read more.

Children who are exposed to lead may have extensive health problems, in particular intelligence deficits and developmental delays. Wide-reaching screening programs are essential to identify children in need of remediation and medical intervention. Lead exposure is most problematic in low- and middle-income countries, as well as in underserved populations in wealthier regions of the world. To increase accessibility, it is critical that screening tools are inexpensive, portable, and easy to use. Here we report a low-cost, handheld, sample-to-answer system for the detection of lead in whole blood samples. Our assay simultaneously lyses blood cells, liberates lead from hemoglobin, aggregates proteins and cellular debris, and separates the solubilized lead from the aggregate via a simple filtration device. Using a screen-printed carbon electrode, anodic stripping voltammetry with a low-cost potentiostat, and our sample-to-answer workflow, we achieved a limit of detection of 1.44 μg/dL, which is below the blood lead reference value established by the US Centers for Disease Control and Prevention (3.5 μg/dL). We validated our system using pre-quantified reference samples from lead-exposed animals and demonstrated excellent agreement with our calibration curve, including for samples near the 3.5 μg/dL threshold. Full article

(This article belongs to the Special Issue Point-of-Care Testing Using Biochemical Sensors for Health and Safety)

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37 pages, 2121 KB

Open AccessReview

From Static to Dynamic: The Convergence of Nanomaterials and 3D/4D Bioprinting for Adaptive Wearable Sports Biosensors

by Haya Akkad, Fatih Ciftci, Esma Ahlatcıoğlu Özerol and Ahmet Akif Kizilkurtlu

Biosensors 2026, 16(7), 392; https://doi.org/10.3390/bios16070392 - 20 Jul 2026

Viewed by 368

Abstract

Wearable biosensors have swiftly progressed from stiff laboratory prototypes to flexible, skin-like systems capable of ongoing physiological monitoring. Yet, the active and mechanically intense nature of athletic performance reveals the limits of static device designs made solely through traditional 3D printing. This review [...] Read more.

Wearable biosensors have swiftly progressed from stiff laboratory prototypes to flexible, skin-like systems capable of ongoing physiological monitoring. Yet, the active and mechanically intense nature of athletic performance reveals the limits of static device designs made solely through traditional 3D printing. This review offers a thorough analysis of the shift from custom 3D-printed platforms to adaptive 4D-printed wearable biosensors that include time-sensitive, stimuli-responsive materials. We carefully investigate how nanomaterial-engineered transducers, including carbon nanomaterials, MXenes, and metallic nanostructures, improve electrochemical sensitivity, signal stability, and mechanical durability in sweat-based and electrophysiological sensing. Additionally, we examine the integration of thermoresponsive polymers, moisture-activated hydrogels, shape-memory materials, and self-healing networks that support autonomous control of skin–sensor contact, microfluidic sweat management, and structural stability under high mechanical strain. Case studies focused on sports monitoring demonstrate how these innovations enable multimodal measurement of mechanical, chemical, and molecular biomarkers in real time. Lastly, we address manufacturing scalability, regulatory issues, and translational challenges necessary to move from proof-of-concept to clinical deployment. By combining nanomaterial-driven electrochemical precision with 4D-printed mechanical adaptability, this review maps a path toward adaptive, self-regulating wearable platforms that sustain analytical accuracy even under extreme physiological demands. Full article

(This article belongs to the Section Wearable Biosensors)

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48 pages, 22497 KB

Open AccessArticle

Region-Specific Information-Theoretic Feature Representation of Wearable Plantar Insole Signals for Parkinson’s Disease Gait Assessment

by Hao Li, Xinyu Zhang, Qikai Wang and Jun Ma

Biosensors 2026, 16(7), 391; https://doi.org/10.3390/bios16070391 - 20 Jul 2026

Viewed by 311

Abstract

Parkinson’s disease (PD) is associated with gait impairment, bilateral asymmetry, and increased gait variability, highlighting the need for objective and interpretable wearable gait assessment. Plantar insole recordings directly capture foot–ground loading, but their use in PD assessment is often limited by global or [...] Read more.

Parkinson’s disease (PD) is associated with gait impairment, bilateral asymmetry, and increased gait variability, highlighting the need for objective and interpretable wearable gait assessment. Plantar insole recordings directly capture foot–ground loading, but their use in PD assessment is often limited by global or low-order descriptors that do not fully represent regional loading organization. This study proposes a region-specific information-theoretic framework for PD gait assessment using wearable plantar-pressure insoles. Bilateral plantar insole signals were reorganized into five anatomical regions: heel, rearfoot, midfoot, forefoot, and toe. Self-information index (SII), Shannon entropy (EN), negentropy (NEG), sample entropy (SEN), and Kullback–Leibler divergence (KL) features were extracted to characterize self-information fluctuation, probabilistic uncertainty, non-Gaussian organization, temporal irregularity, and directional distributional discrepancy in plantar-pressure dynamics. The resulting feature representation was evaluated at gait-cycle, walking-recording, and subject-independent levels using conventional classifiers, ablation analysis, subject-balanced cycle aggregation, and an information-theoretic three-dimensional feature-space rule model (ITFS-RM). KNN achieved an accuracy of 0.9668 at the gait-cycle level, and MLP achieved an accuracy of 0.9344 at the walking-recording level. Under stricter subject-independent evaluation, the accuracy was 0.8475, and subject-balanced-cycle aggregation achieved an accuracy of 0.8655. Region-specific analysis and ablation experiments showed spatially heterogeneous HC–PD differences, with the toe region showing the most consistent contribution. SII, KL, and NEG provided stable discriminative contributions, particularly in toe-related and regional-transition features. ITFS-RM provided explicit feature combinations, value ranges, and spatial rule boundaries for interpretable walking-recording level and subject-grouped separation. These results support region-specific information-theoretic analysis as an interpretable representation of plantar-pressure dynamics for PD gait assessment and emphasize the need for subject-wise validation when repeated walking recordings are available. Full article

(This article belongs to the Special Issue Wearable Sensors and Systems for Continuous Health Monitoring)

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20 pages, 4826 KB

Open AccessArticle

Expanding Biolayer Interferometry Applications: Enhanced Accuracy, Precision, and Sensitivity in Residual Biomolecule Detection and Quantitation of Bispecifics and AAV Viral Particles

by Stuart Knowling, Kirsty McBain and David Apiyo

Biosensors 2026, 16(7), 390; https://doi.org/10.3390/bios16070390 - 18 Jul 2026

Viewed by 360

Abstract

Biolayer Interferometry (BLI) has traditionally been used for characterization of protein–protein interactions (PPI) with proteins, such as antibodies and their antigens, through kinetic and quantitation assays. Limitations, for example in sensitivity and the availability of established assay formats, have restricted its adoption across [...] Read more.

Biolayer Interferometry (BLI) has traditionally been used for characterization of protein–protein interactions (PPI) with proteins, such as antibodies and their antigens, through kinetic and quantitation assays. Limitations, for example in sensitivity and the availability of established assay formats, have restricted its adoption across other analytical applications. This article highlights three case studies which demonstrate the expansion of BLI into novel applications, spanning the areas of protein detection and viral vector characterization. The first case study details the use of a multi-step signal amplification assay to enable the detection of low abundant molecules, such as cytokines, at lower concentrations than can be detected using the standard one-step binding approach. Cytokines are sandwiched between biotinylated and HRP-conjugated antibodies, then dipped into 3-amino-9-ethylcarbazole (AEC) reagent, resulting in an enhancement of the cytokine detection sensitivity. The second case study uses BLI for the quantitation of mixed populations of a bispecific antibody (bsAb). Bridging and dual binding assay formats are evaluated for their assessment of bsAb antigen binding kinetics and ability to determine the ratio of correctly assembled bsAb within a sample. In the third case study, BLI detection principles are used to estimate the percentage full capsids in a mixed population of AAV particles. Collectively, these case studies demonstrate the versatility of Octet^® BLI and highlight its potential to support an increasing range of analytical workflows beyond its traditional applications. Full article

(This article belongs to the Special Issue Novel Optical Fiber Sensors for Biochemical and Biological Applications)

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14 pages, 2555 KB

Open AccessArticle

A Colorimetric Aptasensor for Rapid Detection of Sulfadimethoxine in Aquaculture

by Hong Liang, Jiahao Tan, Tingyu Wang, Yaomei Wang and Chen Zhang

Biosensors 2026, 16(7), 389; https://doi.org/10.3390/bios16070389 - 18 Jul 2026

Viewed by 293

Abstract

Sulfadimethoxine (SDM) is a sulfonamide antibiotic widely used in the aquaculture of aquatic organisms. Its excessive residues in animal-derived food products can cause irreversible harm to human health and the environment. Current primary detection methods for SDM, such as instrumental methods and Immunoassay [...] Read more.

Sulfadimethoxine (SDM) is a sulfonamide antibiotic widely used in the aquaculture of aquatic organisms. Its excessive residues in animal-derived food products can cause irreversible harm to human health and the environment. Current primary detection methods for SDM, such as instrumental methods and Immunoassay techniques, demonstrate high sensitivity and accuracy. However, their industrial application is impeded by laborious sample pretreatment, reliance on specific equipment, and dependence on specially trained personnel. Therefore, there is an urgent need to develop a simple and rapid method for detecting SDM residues. In this study, we constructed a novel colorimetric sensing platform based on functional nucleic acids for SDM detection. This sensor incorporates a nucleic acid aptamer capable of specifically recognizing SDM, a G-quadruplex/Hemin complex with peroxidase-like catalytic activity, and a shielding sequence that suppresses catalytic activity while undergoing SDM-induced conformational changes. The colorimetric signal was generated using a 3,3′,5,5′-Tetramethylbenzidine (TMB) chromogenic substrate, and the sensor’s performance was evaluated via absorbance measurements with a microplate reader. After optimizing detection conditions, the sensor exhibited a linear response to SDM concentrations ranging from 0.155 to 3.10 ng/mL, with a detection limit of 0.0796 ng/mL. Furthermore, the sensor demonstrated excellent selectivity and achieved recoveries of 83.0% to 107% in spiked aquaculture water and fish samples, with coefficients of variation below 10.4%, confirming its superior practicality for real-world sample analysis. Full article

(This article belongs to the Section Environmental, Agricultural, and Food Biosensors)

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17 pages, 6683 KB

Open AccessArticle

Elucidating the Mechanism of Interactions Between Aminoglycosides and AuNPs: Why the Classical Colorimetric Assay May Falsely Report Aptamer Affinity

by Yaning Liang, Shiyi Fang, Zhuoer Chen, Yuzhuo Chen, Qingqing Yang, Xuelan Shu and Tao Le

Biosensors 2026, 16(7), 388; https://doi.org/10.3390/bios16070388 - 17 Jul 2026

Viewed by 324

Abstract

Gold nanoparticles (AuNPs) are widely used in aptasensors because of their high extinction coefficient and aggregation-dependent color differences. However, recent studies have indicated that nonspecific interactions between target molecules and AuNPs may dominate the detection signal rather than aptamer–target specific binding. This study [...] Read more.

Gold nanoparticles (AuNPs) are widely used in aptasensors because of their high extinction coefficient and aggregation-dependent color differences. However, recent studies have indicated that nonspecific interactions between target molecules and AuNPs may dominate the detection signal rather than aptamer–target specific binding. This study systematically investigated the interactions between 13 aminoglycoside antibiotics and AuNPs. We found that all aminoglycoside antibiotics interacted strongly with AuNPs, considerably reducing their salt stability. Furthermore, methoxy polyethylene glycol thiol reversed AuNP aggregation induced by aminoglycoside antibiotics, indicating that it occurs at the secondary minimum. Using density functional theory, we analyzed the molecular structures and charge distribution characteristics of the aminoglycoside antibiotics, elucidating that they replace citrate ions on AuNP surfaces via a ligand exchange mechanism, thereby inducing aggregation. Additionally, both aptamer targets and complementary DNA struggled to desorb the aptamer (KAN6-1) from the AuNP surfaces. Our study demonstrates that the label-free colorimetric assay based on aggregation of unmodified citrate–AuNPs is neither suitable for characterizing the binding affinity of aminoglycoside aptamers nor viable for constructing corresponding colorimetric sensors to detect this class of antibiotics. Thus, researchers should incorporate mechanistic verification and rigorous controls when employing this system to ensure reliable results. Full article

(This article belongs to the Special Issue Aptamer-Based Sensing: Designs and Applications)

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Graphical abstract

19 pages, 1505 KB

Open AccessArticle

Magnetic Beads-Based Electrochemical Label-Free DNA-Bioassay for the Detection of Peanut Allergen Ara h2 in Food Matrices

by Juan Pablo Hervás-Pérez, Sergio Izcara and Marta Sánchez-Paniagua

Biosensors 2026, 16(7), 387; https://doi.org/10.3390/bios16070387 - 17 Jul 2026

Viewed by 396

Abstract

The reliable detection of the peanut allergen Ara h2 in processed foods remains a major challenge, since thermal and high-pressure treatments can alter protein structure and limit the performance of immunoassays. DNA-based methods provide a robust alternative to this approach. In this work, [...] Read more.

The reliable detection of the peanut allergen Ara h2 in processed foods remains a major challenge, since thermal and high-pressure treatments can alter protein structure and limit the performance of immunoassays. DNA-based methods provide a robust alternative to this approach. In this work, a highly sensitive label-free electrochemical genoassay for Ara h2 DNA detection was developed using streptavidin-coated magnetic beads (MBs). A biotinylated capture probe (CP) immobilized on the MBs’ surface enabled specific target recognition through a sandwich hybridization strategy with a secondary probe, allowing for direct electrochemical detection without enzymatic labels. Two transduction strategies were evaluated: (i) electrochemical impedance spectroscopy (EIS) with ferri/ferrocyanide as a redox probe, and (ii) differential pulse voltammetry (DPV) using methylene blue. The ferri/ferrocyanide-based EIS approach showed the best sensitivity and discrimination between hybridized and non-hybridized states. A linear dependence was observed with the concentration of the synthetic Ara h2 target over the 0.05 to 20 nM range, with a detection limit of 0.025 nM. CP-MBs showed good stability for at least 20 days. Applicability was demonstrated in soy beverages, rice beverages, and low-fat cow’s milk, with recoveries close to 100% and negligible matrix effects. Full article

(This article belongs to the Special Issue Nanobiosensors Based on Electrochemical Principles)

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33 pages, 4736 KB

Open AccessReview

Red-to-NIR-Fluorescent Graphene Quantum Dots for Biomedical Applications

by Shuyi He, Weichao Liu, Kang Qin and Steven Xu Wu

Biosensors 2026, 16(7), 386; https://doi.org/10.3390/bios16070386 - 16 Jul 2026

Viewed by 557

Abstract

Graphene quantum dots (GQDs) have attracted extensive interest in biomedical applications because of their favorable physicochemical properties, including environmental friendliness, excellent water solubility, high chemical stability, and facile surface modification. However, most GQDs exhibit fluorescence in the ultraviolet or visible region, which limits [...] Read more.

Graphene quantum dots (GQDs) have attracted extensive interest in biomedical applications because of their favorable physicochemical properties, including environmental friendliness, excellent water solubility, high chemical stability, and facile surface modification. However, most GQDs exhibit fluorescence in the ultraviolet or visible region, which limits their biomedical applications because autofluorescence from biological systems reduces the signal-to-noise ratio in biosensing and bioimaging. Over the past decade, the emission of GQDs has been extended from the UV–visible region into the red-to-near-infrared (NIR) region. Red-to-NIR fluorescence enables higher-resolution imaging and deeper tissue penetration by reducing light scattering and minimizing tissue absorption and autofluorescence. In this review, we summarize recent advances in red-to-NIR-fluorescent GQDs for biomedical applications, including their synthesis, optical properties, surface engineering, and applications in biosensing, bioimaging and theranostics. Finally, we discuss the current challenges and future potential development of the red-to-NIR-fluorescent GQDs. Full article

(This article belongs to the Special Issue New Advances in Bioimaging and Biosensing Based on Nanomaterials)

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17 pages, 13146 KB

Open AccessArticle

Universal, Rapid, and Cleavable Labeling of Antibodies by Fluorophores and DNA Oligonucleotides for Multiplex Immunostaining and Spatial Proteomics Through MIST Linker

by Arafat Meah, Shuo Yin, Saimoen Strrrz Anderson, Ming Lin, Shuo Liang, Meghana Davuluri, Yi-Xian Qin, Sandeep K. Mallipattu and Jun Wang

Biosensors 2026, 16(7), 385; https://doi.org/10.3390/bios16070385 - 15 Jul 2026

Viewed by 473

Abstract

Direct antibody labeling is essential for immunoassays, multiplexed imaging, and biosensing; however, current methods are often time-consuming, restricted by antibody source, risk compromising protein performance, or vary with multiple steps. We introduce multiplex in situ tagging (MIST) Linker, a rapid and Fc-site-specific labeling [...] Read more.

Direct antibody labeling is essential for immunoassays, multiplexed imaging, and biosensing; however, current methods are often time-consuming, restricted by antibody source, risk compromising protein performance, or vary with multiple steps. We introduce multiplex in situ tagging (MIST) Linker, a rapid and Fc-site-specific labeling tool that conjugates fluorophores or DNA oligonucleotides to antibodies from diverse commercial sources in as fast as 10 min using minimal starting material. MIST Linker achieves >90% cleavage upon UV exposure, facilitating rapid cyclic imaging on a single specimen. Validated across multiple species and sources, the platform outperforms conventional two-step immunofluorescence and immunohistochemistry in various tissues and cell lines. By enabling the rapid, cost-effective customization of antibody panels, MIST Linker significantly lowers the barrier to accessing antibody–DNA conjugates for spatial biology. When integrated with the spatial MIST platform and MIST-Explorer, it enables high-plex, single-cell spatial proteomics at high signal-to-noise ratios in human clinical biopsies, mouse specimens and cell lines. This toolkit provides an efficient, accessible solution for high-resolution spatial mapping, allowing for the in-depth analysis of cell subpopulations, biomarker distributions, and signaling events in complex biological specimens. Thus, MIST Linker offers a versatile, accessible, and scalable solution for antibody-labeling-based research and clinical diagnosis. Full article

(This article belongs to the Section Biosensors and Healthcare)

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44 pages, 4311 KB

Open AccessReview

Nanomaterial-Assisted Physical Mass Loading and Signal Amplification Strategies for Exosome Isolation and Sensing in Liquid Biopsy: A Review

by Sumedha Nitin Prabhu

Biosensors 2026, 16(7), 384; https://doi.org/10.3390/bios16070384 - 14 Jul 2026

Viewed by 530

Abstract

Exosomes and small extracellular vesicles are promising liquid-biopsy biomarkers because they carry molecular information from their cells of origin and can be accessed from minimally invasive biofluids. Reliable separation and detection are made more difficult by their small size, low abundance, diverse composition, [...] Read more.

Exosomes and small extracellular vesicles are promising liquid-biopsy biomarkers because they carry molecular information from their cells of origin and can be accessed from minimally invasive biofluids. Reliable separation and detection are made more difficult by their small size, low abundance, diverse composition, and co-occurrence with lipoproteins, protein aggregates, and other extracellular particles. To improve exosome enrichment, capture, and sensing, nanomaterial-assisted techniques have become crucial. Using a mechanism-based approach that differentiates between non-gravimetric signal amplification and genuine physical mass loading, this study offers an organized comparison of nanomaterial-enabled exosome sensing techniques. This distinction is helpful because different transducers measure different physical quantities: while optical, electrochemical, fluorescent, catalytic, and nucleic acid-based platforms typically benefit from enhanced signal generation rather than increased mass, resonant and gravimetric sensors benefit from increased inertial or surface-bound mass. In terms of amplification mechanism, transducer compatibility, sample-matrix tolerance, workflow complexity, and translational maturity, the review contrasts metallic nanoparticles, magnetic systems, metal–organic frameworks, carbon and two-dimensional materials, quantum dots, upconversion nanomaterials, DNA nanostructures, and polymer-based platforms. The gap between analytical sensitivity and clinical utility, including separation purity, recovery, biological heterogeneity, pre-analytical variability, interference from complex biofluids, and the need for uniform validation, is given special focus. The review concludes that no single nanomaterial or amplification method is universally optimal; instead, platform-aware, application-specific integration of isolation, amplification, and validation techniques is necessary for clinically meaningful exosome sensing. Full article

(This article belongs to the Special Issue Functional Nanomaterials for Advanced Biosensing: From Molecular Design to Real-World Applications)

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24 pages, 11435 KB

Open AccessArticle

A Deep Learning Framework for EEG-Based Decoding of Visually Imagined Arrows with Different Colors and Directions

by Rami Alazrai, Oula Hatahet, Sahar Qaadan, Youssef Alothman and Mohamed Bader-El-Den

Biosensors 2026, 16(7), 383; https://doi.org/10.3390/bios16070383 - 14 Jul 2026

Viewed by 566

Abstract

Brain–computer interface (BCI) systems have demonstrated significant potential across medical, educational, and entertainment domains. Recently, visual imagery (VI) has emerged as an alternative to traditional motor imagery (MI) paradigms, offering a broader spectrum of control signals for dexterous assistive devices. In this study, [...] Read more.

Brain–computer interface (BCI) systems have demonstrated significant potential across medical, educational, and entertainment domains. Recently, visual imagery (VI) has emerged as an alternative to traditional motor imagery (MI) paradigms, offering a broader spectrum of control signals for dexterous assistive devices. In this study, we propose a novel BCI framework for classifying visually imagined arrows defined by different colors and directions. The proposed framework employs the Choi–Williams time–frequency distribution (CW-TFD) to construct a joint time–frequency–spatial representation (TFSR) of EEG signals. The resulting TFSR is converted into grayscale images and provided as input to a newly designed convolutional neural network (CNN), which performs 16-class decoding of visually imagined arrows defined by combined color and direction attributes. A new EEG dataset was collected from 16 subjects who imagined 16 distinct arrows comprising four colors and four directions. The framework achieved an average classification accuracy of 95.05% and a Cohen’s kappa score of 0.947 across the 16 classes. To comprehensively evaluate the proposed approach, three comparative analyses were conducted. First, multiple time–frequency representations were assessed for VI-based EEG decoding. Second, the proposed CNN architecture was benchmarked against several state-of-the-art pre-trained deep learning models. Third, the framework was compared with conventional machine learning classifiers using handcrafted features. Results demonstrate that the constructed CWD-based TFSR combined with the proposed CNN consistently outperforms alternative representations and classification models. These findings demonstrate the feasibility of decoding an expanded set of visually imagined color–direction arrow commands in a subject-specific EEG-based BCI setting, supporting further development of calibrated VI-based BCI systems for assistive and interactive applications. Full article

(This article belongs to the Special Issue Recent Advances in Artificial Intelligence (AI)-Enabled Biosensing Technologies)

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29 pages, 7201 KB

Open AccessReview

Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis

by Yunseon Han, Haebin Jo, Minyoung Ju, Seowoo Bae, Ju Young Kim, Jinho Yoon and Taek Lee

Biosensors 2026, 16(7), 382; https://doi.org/10.3390/bios16070382 - 13 Jul 2026

Viewed by 666

Abstract

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 [...] Read more.

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

(This article belongs to the Special Issue AI/ML-Enabled Biosensing: Shaping the Future of Disease Detection)

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19 pages, 2605 KB

Open AccessArticle

Multimodal Electrophysiological Signals for Machine Learning-Aided Parkinson’s Disease Diagnosis

by Bo Jiang, Han Liu, Yuchen Ran, Yan Zhou, Keke Chen, Xiao Yang, Jiayuan Zhao, Mengxuan Hu, Boyan Fang and Guangying Pei

Biosensors 2026, 16(7), 381; https://doi.org/10.3390/bios16070381 - 13 Jul 2026

Viewed by 447

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder affecting motor and autonomic nervous system functions. In this study, six synchronized modalities—electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), respiration (Resp), photoplethysmography (PPG), and gait (Gait)—were recorded from 25 PD patients and 25 healthy controls. A Random [...] Read more.

Parkinson’s disease (PD) is a neurodegenerative disorder affecting motor and autonomic nervous system functions. In this study, six synchronized modalities—electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), respiration (Resp), photoplethysmography (PPG), and gait (Gait)—were recorded from 25 PD patients and 25 healthy controls. A Random Forest classifier was used to perform both unimodal and multimodal signal classification. Among unimodal models, ECG achieved the highest accuracy (84%), whereas the performance of multimodal combinations did not increase linearly with the number of modalities; integrating three or more complementary signals was sufficient to substantially improve classification. The full six-modality model achieved an accuracy of 95.00%, precision of 94.17%, recall of 97.14%, F1 score of 95.21%, and an AUC of 0.98. Incremental analysis further indicated that selecting key complementary modalities can maintain high classification performance while reducing equipment requirements, simplifying experimental procedures, and improving participant comfort, providing guidance for the development of efficient, non-invasive PD diagnostic tools. Full article

(This article belongs to the Special Issue Recent Advances in Microneedle Array Electrodes in Biomedicine)

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15 pages, 1972 KB

Open AccessArticle

Seasonal Variations and Indoor–Outdoor Characteristics of Fluorescent Aerosol Particles in Japanese Office Buildings

by Shota Tsuchiya, U. Yanagi, Hoon Kim, Kei Shimonosono and Naoki Kagi

Biosensors 2026, 16(7), 380; https://doi.org/10.3390/bios16070380 - 11 Jul 2026

Viewed by 515

Abstract

Fluorescent aerosol particles (FAPs) are widely used as a real-time proxy for primary biological aerosol particles; however, their seasonal characteristics and size-resolved distributions in office environments remain poorly understood. In this study, FAPs were measured in ten office spaces located in four distinct [...] Read more.

Fluorescent aerosol particles (FAPs) are widely used as a real-time proxy for primary biological aerosol particles; however, their seasonal characteristics and size-resolved distributions in office environments remain poorly understood. In this study, FAPs were measured in ten office spaces located in four distinct regions of Japan during summer and winter using a real-time Bioaerosol Sensor. Indoor and outdoor FAP concentrations, indoor/outdoor ratios, and the size-resolved FAP fraction were evaluated. Indoor FAP concentrations were generally below 100 particles per liter (p/L), although peak concentrations of 140 p/L in summer and 195 p/L in winter were observed. Significant seasonal differences were detected in most offices, with several buildings showing higher concentrations in winter. Many offices exhibited relative humidity levels below 40% during winter, suggesting that dry indoor conditions may have promoted particle resuspension and contributed to elevated FAP concentrations. Indoor–outdoor comparisons suggested contributions from both indoor sources and outdoor infiltration. The size-resolved FAP fraction increased markedly with particle size, with median indoor values reaching 40–74% for 2.0–5.0 μm particles and 96–100% for particles > 5.0 μm. These findings indicate that FAPs in office environments are strongly associated with coarse particles and exhibit substantial seasonal and building-dependent variability. Full article

(This article belongs to the Special Issue Fluorescent Materials with Excellent Biocompatibility and Their Application in Bio-Sensing, Bio-Imaging (3rd Edition))

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32 pages, 12672 KB

Open AccessReview

Mechanical Characterization in Red Blood Cells Using Optical Tweezers: A Review

by Xinyu Yang, Yuting Sun, Hong Jin, Jianguo Feng and Shangzhong Jin

Biosensors 2026, 16(7), 379; https://doi.org/10.3390/bios16070379 - 10 Jul 2026

Viewed by 656

Abstract

Given that red blood cells (RBCs) are the most abundant cells in blood, their morphology and mechanics strongly affect blood rheology. Furthermore, changes in the physiological functions and health status of an organism can also affect RBC mechanics. Therefore, understanding the mechanical properties [...] Read more.

Given that red blood cells (RBCs) are the most abundant cells in blood, their morphology and mechanics strongly affect blood rheology. Furthermore, changes in the physiological functions and health status of an organism can also affect RBC mechanics. Therefore, understanding the mechanical properties of RBCs holds substantial research value in the biomedical field. The technology of optical tweezers (OT) has become a crucial method for measuring and analyzing the mechanical properties of RBCs, owing to their unique advantages such as non-contact manipulation and piconewton-level force sensitivity. This review first outlines the basic mechanical properties of RBCs, the mechanical sensing principles of optical tweezers, and their basic manipulation modes. It also focuses on the measurement and application of key mechanical parameters, such as the deformation index and shear modulus. Furthermore, the review covers the integration of optical tweezers with Raman spectroscopy, fluorescence, and microfluidics. These combined approaches allow for the simultaneous acquisition of mechanical and molecular data, dynamic monitoring of mechanical state changes, and analysis of external stimuli and physiological mechanisms, thereby supporting disease diagnosis, drug efficacy evaluation, and artificial blood quality assessment. Finally, it discusses current challenges and future directions. Full article

(This article belongs to the Section Optical and Photonic Biosensors)

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26 pages, 3238 KB

Open AccessReview

Evolution of Whole-Cell Biosensor Detection Technology for PAHs and Their Halogenated Derivatives Driven by Performance Requirements

by Jingfang Zhang, Wenhui Mao, Shiqi Xia, Liangshu Hu, Mingzhang Guo and Huilin Liu

Biosensors 2026, 16(7), 378; https://doi.org/10.3390/bios16070378 - 10 Jul 2026

Viewed by 530

Abstract

Polycyclic aromatic hydrocarbons (PAHs) and their halogenated derivatives are important targets in environmental monitoring and pollution control because of their persistence, bioaccumulation, and potential carcinogenicity. Reliable strategies for detecting these pollutants remain essential for environmental risk assessment. In recent years, microbial whole-cell biosensors [...] Read more.

Polycyclic aromatic hydrocarbons (PAHs) and their halogenated derivatives are important targets in environmental monitoring and pollution control because of their persistence, bioaccumulation, and potential carcinogenicity. Reliable strategies for detecting these pollutants remain essential for environmental risk assessment. In recent years, microbial whole-cell biosensors have attracted increasing attention as analytical tools for pollutant detection and toxicity evaluation. These biosensors employ living cells to recognize target compounds and generate measurable signals through endogenous metabolic pathways and transcriptional regulatory networks. As a result, they can reflect biologically relevant responses and operate in complex environmental matrices, making them suitable for in situ monitoring. This review summarises recent advances in whole-cell biosensors for detecting PAHs and their halogenated derivatives. We discuss the design strategies for constructing these whole-cell biosensors and outline their technological development. Recent efforts to improve biosensor performance are also highlighted. Current research trends indicate a shift from optimizing individual genetic components to improving overall system robustness, standardized evaluation, and practical field deployment. These developments provide important insights for designing reliable and engineerable whole-cell biosensing platforms for monitoring PAHs and related pollutants. Full article

(This article belongs to the Special Issue Advanced Biosensors Based on Molecular Recognition)

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15 pages, 4076 KB

Open AccessArticle

A Weighted Neural Network Model Based on Laboratory Tests for Identifying Lymph Node Metastases in Esophageal Squamous Cell Carcinomas

by Qiangqiang Ouyang, Ziming Gao, Jingbo Yang, Shaoyi Wang, Zonglin Li, Yifan Zhang, Tianyou Chen, Xinhua Xu, Runkun Han and Hao Chen

Biosensors 2026, 16(7), 377; https://doi.org/10.3390/bios16070377 - 10 Jul 2026

Viewed by 468

Abstract

Lymph node metastasis (LNM) is a key prognostic factor in esophageal squamous cell carcinoma (ESCC), and accurate preoperative prediction remains challenging. Blood biomarkers provide a conventional, preoperative diagnostic technique that is cost-effective and free from radiation risks. So far, previous studies have been [...] Read more.

Lymph node metastasis (LNM) is a key prognostic factor in esophageal squamous cell carcinoma (ESCC), and accurate preoperative prediction remains challenging. Blood biomarkers provide a conventional, preoperative diagnostic technique that is cost-effective and free from radiation risks. So far, previous studies have been published on the precise diagnosis of lymph node metastases using conventional ultrasound or CT techniques. While there is a lack of research studies that address the diagnosis of LNM from blood biomarkers. In this work, we acquired a cohort of blood biomarkers of 1933 patients and designed a weighted neural network (WNN) model for the accurate prediction of LNM from blood biomarkers. The WNN model is designed with a neural network classifier trained on blood biomarkers labeled with pathological nodal (pN) stages of LNM. The experimental findings demonstrate that the WNN model achieved 83.1% accuracy and an AUC of 0.88 on the original, non-augmented test set for diagnosing LNM, while CT only achieved 50.4% accuracy (AUC 0.60) and ultrasound achieved 60.5% accuracy (AUC 0.67). Additionally, SHAP analysis reveals that three blood biomarkers—white blood cells (WBC#), monocytes (Mono#), and neutrophils (Neut#)—significantly impact the WNN model’s output. This WNN model shows promise as a research tool to diagnose LNM. Full article

(This article belongs to the Special Issue The Smart Biosensors Era: AI in Cancer Detection and Imaging)

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11 pages, 1387 KB

Open AccessArticle

Ultrasensitive Fluorescence Sensing of Chlorpyrifos Using Core–Shell Au@Ag Nanoparticle-Enhanced Inner Filter Effect on g-C₃N₄

by Mengli Wang, Yuanyuan Xia, Yulei Li, Lifen Chen, Kunyan Wang, Shuangshuang Wu and Yuelan Zhang

Biosensors 2026, 16(7), 376; https://doi.org/10.3390/bios16070376 - 9 Jul 2026

Viewed by 464

Abstract

In this work, we developed a novel, ultrasensitive fluorescence sensing platform for determination of organophosphorus pesticides (OPs), using chlorpyrifos as a representative model analyte. The sensing strategy was constructed upon the key inner filter effect (IFE) between graphitic carbon nitride (g-C₃N [...] Read more.

In this work, we developed a novel, ultrasensitive fluorescence sensing platform for determination of organophosphorus pesticides (OPs), using chlorpyrifos as a representative model analyte. The sensing strategy was constructed upon the key inner filter effect (IFE) between graphitic carbon nitride (g-C₃N₄) nanosheets and silver-coated gold core–shell nanoparticles (Au@Ag NPs). Initially, gold nanoparticles (Au NPs), silver nanoparticles (Ag NPs), and Au@Ag NPs were successfully synthesized, and their fluorescence quenching efficiencies toward g-C₃N₄ were systematically evaluated. Owing to the superior spectral overlap with the fluorescence emission of g-C₃N₄, Au@Ag NPs exhibited the most obvious quenching effect and were thereby selected as the optimal quencher for sensor fabrication. Then, acetylcholinesterase (AChE) catalyzed the hydrolysis of acetylthiocholine (ATCH) into thiocholine. The generated thiocholine then induced aggregation of Au@Ag NPs via electrostatic and Ag-S interactions, which reduced the IFE efficiency and ultimately restored the fluorescence of g-C₃N₄. In contrast, the presence of chlorpyrifos effectively inhibits AChE activity, thereby suppressing ATCH hydrolysis and the subsequent aggregation of Au@Ag NPs. The fluorescence intensity of g-C₃N₄ was quenched by Au@Ag NPs and the signal was low. Under optimal experimental conditions, the response signal was found to be proportional to chlorpyrifos (CPF). This work presents a rapid, cost-effective, and highly sensitive approach for CPF residue analysis, holding great potential for applications in food safety monitoring and environmental surveillance. Full article

(This article belongs to the Special Issue Research Progress of Nanomaterial-Mediated Biosensors: From Fabrication to Signal Amplification)

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18 pages, 22554 KB

Open AccessArticle

Capillary-Driven Microfluidic Electrical Screening of Influenza H3N2-Infected A549 Cells Using AgNP-Decorated Laser-Patterned Villous Microstructures

by Zhaochi Chen and Minh-Quang Tran

Biosensors 2026, 16(7), 375; https://doi.org/10.3390/bios16070375 - 9 Jul 2026

Viewed by 532

Abstract

A capillary-driven microfluidic electrical screening platform was developed using silver nanoparticle (AgNP)-decorated laser-patterned villous microstructures on a glass substrate for the analysis of H3N2-infected A549 cells. The device integrated nanosecond laser patterning, AgNP conductive thin-film formation, passive capillary transport, and direct electrical readout [...] Read more.

A capillary-driven microfluidic electrical screening platform was developed using silver nanoparticle (AgNP)-decorated laser-patterned villous microstructures on a glass substrate for the analysis of H3N2-infected A549 cells. The device integrated nanosecond laser patterning, AgNP conductive thin-film formation, passive capillary transport, and direct electrical readout within a single microfluidic sensing structure. Villous-like arrays were fabricated using a 1064 nm IR pulsed laser at a fluence of 4.35 J/cm², with a repetition rate of 300 kHz, pulse overlap of 96.7% and scanning speed of 500 mm/s. The fabricated structures exhibited a diameter of 60 μm, height of 80 μm and interpillar pitches ranging from 30 to 90 μm. After AgNP deposition, the surface showed a dominant Ag content of 59.2%, confirming successful formation of conductive microstructured electrodes. The 30 μm pitch structure produced the highest current response of 22 μA at 1 V and the highest ΔI_norm of 0.053 after introduction of H3N2-infected A549 samples. Wettability and capillary transport were tunable by pitch, with contact angles (CAs) decreasing from 140° to 30° and flow velocities decreasing from 0.1 mm/s to 0.03 mm/s. Formalin-fixed H3N2-infected A549 cells were electrically distinguished from non-infected A549 controls over 10¹–10⁶ PFU/μL, with detectable responses down to 10¹ PFU/μL. These results demonstrate a label-free, self-driven, and fabrication-oriented microfluidic strategy for electrical screening of virus-associated cellular samples. Full article

(This article belongs to the Special Issue Integrated Microfluidic Biosensing Systems: Designs and Applications)

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50 pages, 8233 KB

Open AccessReview

Smart Wearable EEG Devices: A Review of Lightweight, Multi-Sensor Systems for Sleep and Everyday Neurophysiology

by Helena Kosnacova, Dusan Horvath, Diana Vitazkova, Erik Foltan, Michal Pecik and Erik Vavrinsky

Biosensors 2026, 16(7), 374; https://doi.org/10.3390/bios16070374 - 8 Jul 2026

Viewed by 1107

Abstract

Wearable electroencephalography (EEG) is rapidly evolving toward lightweight, user-friendly systems that enable brain monitoring in naturalistic settings. Traditional multi-channel, gel-based systems provide broad scalp coverage and high signal fidelity but are impractical for unsupervised or long-term use. This review focuses on the emerging [...] Read more.

Wearable electroencephalography (EEG) is rapidly evolving toward lightweight, user-friendly systems that enable brain monitoring in naturalistic settings. Traditional multi-channel, gel-based systems provide broad scalp coverage and high signal fidelity but are impractical for unsupervised or long-term use. This review focuses on the emerging generation of smart wearable EEG devices that are easy to wear, require minimal setup, and typically integrate additional physiological sensors such as photoplethysmography (PPG), temperature, or motion sensors. We review wearable EEG systems across four main form factors: head-worn EEG devices, smart EEG patches and tattoos, in-ear and headphone-based EEG, and glasses-integrated EEG. Head-worn systems offer broader signal coverage and support more complex applications such as sleep staging, human–machine interaction, and epilepsy monitoring. Patch-based systems are well suited to comfortable long-term monitoring, particularly in sleep-related applications. Ear-center systems provide high user comfort and stable signal acquisition from non-traditional electrode locations. Glasses-integrated devices represent an emerging option for unobtrusive daytime neurophysiology. Each category is examined in terms of sensor fusion, technical parameters, and embedded algorithms, with particular emphasis on automated signal analysis. We conclude with a discussion on current limitations, regulatory and usability challenges, and future directions toward unobtrusive, AI-powered neurotechnology for home and clinical use. Full article

(This article belongs to the Special Issue Advances in Flexible and Wearable Biosensors)

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23 pages, 2003 KB

Open AccessSystematic Review

Nanotechnology-Based Detection of Sickle Cell Disease and Thalassemia: A Systematic Review

by Manjyot Kaur, Janesh Kumar Gautam, Aishwarya Rajendra Sharma, Vishal Singh, Disha Chouhan, Akash Baghel, Bontha V. Babu and Suman Sundar Mohanty

Biosensors 2026, 16(7), 373; https://doi.org/10.3390/bios16070373 - 8 Jul 2026

Viewed by 559

Abstract

Sickle cell disease (SCD) and thalassemia are genetic disorders that necessitate accurate diagnosis for effective management and improved patient outcomes. The advent of nanotechnology has paved the way for innovative, precise detection methods, offering enhanced sensitivity and specificity. The present systematic review aims [...] Read more.

Sickle cell disease (SCD) and thalassemia are genetic disorders that necessitate accurate diagnosis for effective management and improved patient outcomes. The advent of nanotechnology has paved the way for innovative, precise detection methods, offering enhanced sensitivity and specificity. The present systematic review aims to assess the analytical performance of nanotechnology-based detection methods for SCD and thalassemia, with a focus on evaluating the analytical performance and identifying the most sensitive nanotechnology-based techniques. An extensive literature search was conducted across five databases (ScienceDirect, PubMed, Embase, Google Scholar), yielding 23 studies that met the inclusion criteria. These studies showcased the potential of nanotechnology-based methods for detecting SCD and thalassemia. The studies utilized diverse samples, including blood, serum, genomic DNA, and purchased oligonucleotides, with most reporting limit of detection (LOD) values. Specifically, gold nanoparticles (AuNPs) exhibited exceptional sensitivity, with detection limits ranging from 2.6 aM to 0.035 pM. Surface modification and functionalization of AuNPs significantly enhance their detection capabilities. Other nanostructures, including silver nanoparticles, quantum dots, and graphene quantum dots, also demonstrate promising diagnostic capabilities. The results showed that nanotechnology-based methods demonstrated improved analytical sensitivity, with LOD ranging from 2.6 aM to 50 nM. This systematic review provides a comprehensive overview of the analytical performance of nanotechnology-based detection methods, shedding light on their potential to revolutionize diagnosis and treatment. Overall, it highlights the transformative potential of nanotechnology in improving molecular diagnostic accuracy for SCD and thalassemia. Full article

(This article belongs to the Section Biosensors and Healthcare)

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15 pages, 29934 KB

Open AccessArticle

Fluorescent Sensor Array Based on Black Plum Peels-Derived Carbon Dots for Multiplex Heavy Metal Ions Identification

by Ling Yang, Dandan Peng, Haihu Tan, Yahu Wang, Xin Lu, Fanming Zeng, Shi Gang Liu and Yuejun Liu

Biosensors 2026, 16(7), 372; https://doi.org/10.3390/bios16070372 - 8 Jul 2026

Viewed by 578

Abstract

Accurate discrimination of multiple heavy metal ions is essential for environmental monitoring. This study developed a simple fluorescent sensing array utilizing carbon dots derived from black plum peels (PCDs) for the precise identification of metal ions in environmental waters. Three structurally distinct PCDs [...] Read more.

Accurate discrimination of multiple heavy metal ions is essential for environmental monitoring. This study developed a simple fluorescent sensing array utilizing carbon dots derived from black plum peels (PCDs) for the precise identification of metal ions in environmental waters. Three structurally distinct PCDs were hydrothermally synthesized using phenylenediamine isomers as nitrogen dopants, exhibiting distinct fluorescence response patterns to target ions. Pattern recognition was performed using linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA). The optimized system (pH 5–7) achieved high discrimination accuracy for eight metal ions (Sn²⁺, Ag⁺, Hg²⁺, Fe³⁺, Cr³⁺, Pb²⁺, Sb³⁺, and Cu²⁺) at 5–400 μM concentrations. The array effectively identified the binary and ternary mixtures of Hg²⁺/Cu²⁺/Cr³⁺ and successfully detected target ions in river water samples. This cost-effective and scalable approach demonstrates strong potential for applications in water quality monitoring and food safety. Full article

(This article belongs to the Special Issue Biosensors for Environmental Monitoring and Food Safety)

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16 pages, 2189 KB

Open AccessArticle

Biosensors Based on Plasmonic Spoon-Shaped Platforms as a Point-of-Care Tool for Escherichia coli Detection

by Francesco Arcadio, Alessandro Capo, Alessia Calabrese, Chiara Marzano, Mimimorena Seggio, Rosalba Pitruzzella, Federica Passeggio, Shahab Bashir, Muhammad Shoaib, Carla Zannella, Anna De Filippis, Giuseppe Portella, Luigi Zeni and Nunzio Cennamo

Biosensors 2026, 16(7), 371; https://doi.org/10.3390/bios16070371 - 8 Jul 2026

Viewed by 553

Abstract

The Enterobacteriaceae family is a significant source of foodborne pathogens and represents a severe threat to human and animal health. These bacteria can penetrate the dairy supply chain through direct contact with cattle and the livestock environment and can survive production processes. Escherichia [...] Read more.

The Enterobacteriaceae family is a significant source of foodborne pathogens and represents a severe threat to human and animal health. These bacteria can penetrate the dairy supply chain through direct contact with cattle and the livestock environment and can survive production processes. Escherichia coli (E. coli), one of the most diffuse bacteria in raw and processed milk, exposes consumers to the risk of contaminated milk. As a result of this exposition, several milk-borne illness outbreaks have been reported worldwide, underscoring the urgent need for effective detection and prevention measures. Conventional analysis methods are effective but have significant limitations, including the requirement of pre-treatment and pre-enrichment steps. Thus, the need for advanced detection techniques that can accurately identify these pathogens without pre-treatment steps is critical. In this work, a proof-of-concept biosensor based on a spoon-shaped optical biochip was developed to detect E. coli via surface plasmon resonance (SPR) phenomena and was combined with a polyclonal antibody layer against E. coli as a molecular recognition element (MRE). The proposed label-free biosensing strategy, achieved by exploiting simple SPR spoon-shaped biochips, exhibits a remarkable detection limit (6.8 colony-forming units, CFU/mL) and high specificity towards other interfering bacteria belonging to the Enterobacteriaceae family. In addition, tests on commercial milk samples were carried out, achieving recovery values of 95% and 102% for whole milk and infant milk, respectively. The proposed spoon-shaped biosensor enables label-free biosensing without the need for microfluidic systems. It provides a rapid response (10 min), paving the way for its use as a point-of-care test (POCT) in real-world settings. Full article

(This article belongs to the Special Issue Advanced Optical Biosensing: Design, Materials and Practical Applications)

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19 pages, 5237 KB

Open AccessArticle

Distributed Wireless Neural Recording System for Multi-Region Brain Activity Monitoring

by Liu Yang, Changhua You, Gang Wang, Xuan Zhang, Canyang Wang, Bo Cheng, Zhengtuo Zhao, Ning Xue and Lei Yao

Biosensors 2026, 16(7), 370; https://doi.org/10.3390/bios16070370 - 7 Jul 2026

Viewed by 600

Abstract

Distributed neural interfaces for multi-region implantation require both scalable interconnects and robust telemetry, yet conventional centralized or fully distributed architectures often trade-off wiring complexity, resource reuse, and transmission stability. This work presents a distributed wireless neural recording system based on a parallel-link architecture [...] Read more.

Distributed neural interfaces for multi-region implantation require both scalable interconnects and robust telemetry, yet conventional centralized or fully distributed architectures often trade-off wiring complexity, resource reuse, and transmission stability. This work presents a distributed wireless neural recording system based on a parallel-link architecture and a custom 12-channel neural recording Application-Specific Integrated Circuit (ASIC). Each remote module is connected to a central hub through an independent four-wire link (VDD/GND/LVDS±). The ASIC integrates modular digital pixels (MDPs), an on-chip oscillator, a Manchester encoding, and a Low-Voltage Differential Signaling (LVDS) output to reduce interconnect count while maintaining reliable serial transmission. Fabricated in SMIC 0.18 μm CMOS, the chip occupies 4.84 mm × 0.36 mm and consumes 10.13 mW in total, with 48.5 μW/channel consumed by the recording channels excluding the LVDS driver. It achieves 5.6 μVrms input-referred noise and a measured per-channel sampling rate of 28.93 kSps. A compact 20 mm² recording module and an FPGA-based central hub with real-time decoding and compression were implemented for validation. In vivo mouse experiments demonstrate clear action-potential recordings across 12 channels, confirming the feasibility of stable and scalable multi-region neural signal acquisition. Full article

(This article belongs to the Special Issue Implantable, Wireless Biosensors and Biodevices for Neuroscience Research, 2nd Edition)

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17 pages, 4097 KB

Open AccessArticle

Morphological and Thermographic Factors of the Lower Limbs Before Competition and Their Impact on Performance at the Spanish National Cross Country Championships

by Alessio Cabizosu, Victor Ruiz-Angui, Carmen Carazo-Díaz, Francisco Javier Martínez-Noguera and Pedro E. Alcaraz

Biosensors 2026, 16(7), 369; https://doi.org/10.3390/bios16070369 - 7 Jul 2026

Viewed by 471

Abstract

Introduction: Cross-country running performance is influenced by a complex interaction of physiological, biomechanical, and morphological factors. Recently, infrared thermography (IRT) has emerged as a non-invasive method to assess skin temperature (TSK) and detect potential asymmetries associated with neuromuscular status, fatigue, and injury risk. [...] Read more.

Introduction: Cross-country running performance is influenced by a complex interaction of physiological, biomechanical, and morphological factors. Recently, infrared thermography (IRT) has emerged as a non-invasive method to assess skin temperature (TSK) and detect potential asymmetries associated with neuromuscular status, fatigue, and injury risk. However, limited evidence exists regarding its relationship with competitive performance in endurance athletes. Methods: An observational study, conducted with STROBE guidelines, included 24 national-level cross-country athletes competing in the 2026 Spanish National Championships. Pre-competition assessments comprised bilateral thermographic analysis of the anterior and posterior thigh and leg regions, alongside some anthropometric measurements (thigh and leg circumferences) following ISAK standards. Performance was evaluated using official race times. Independent t-tests and linear regression models were applied to assess sex differences and associations between variables. Results: No significant sex differences were observed in thigh circumference, whereas males presented significantly greater leg volume (right p = 0.020; left p = 0.042). Thermographic analysis showed no differences in bilateral thermal asymmetry (ΔTSK) between sex quadriceps (p = 0.077), hamstrings (p = 0.695), shins (p = 0.510), and calves (p = 0.194); however, higher absolute temperatures were observed in males in specific thigh regions (right anterior p = 0.039, right posterior p = 0.015, left posterior p = 0.020). Males achieved significantly faster race times during the first four laps, t1 (p ≤ 0.001), t2 (p = 0.002), t3 (p = 0.002), and t4 (p = 0.008), but there was no difference in the fifth lap, t5 (p = 0.179). Statistically significant correlations were observed between temperature differences in the various anatomical regions and competition results during the first four laps, in three of the four regions analyzed (anterior thigh p = 0.035, posterior thigh p = 0.010, anterior leg p ≤ 0.001). Conclusions: Pre-competition thermal asymmetry of the lower limbs appears to be negatively associated with endurance performance, potentially reflecting suboptimal neuromuscular status or incomplete recovery. IRT represents a practical and sensitive tool for monitoring athletes’ physiological readiness. Full article

(This article belongs to the Section Biosensor and Bioelectronic Devices)

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